sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: stable-diffusion-webui
Issue: [Feature Request]:  can you merge oneflow frameswork
Labels: enhancement,extension-request

### 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 ?

can you merge oneflow frameswork

### Proposed workflow

1. Go to .... 
2. Press ....
3. ...


### Additional information

_No response_
Repository: stable-diffusion-webui
Issue: [Feature Request]: In Batch img2img, allow batch_size to process n images at once instead of n times each image
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 ?

When using Batch img2img, the Batch Size option could also have a check box that changes the behavior of the batch. Instead of creating batches of n times each image, it would create batches of n different images.

### Proposed workflow

Upon selecting img2img, there would be a checkbox next to Batch Size that enable/disable the feature.

### Additional information

The following changes are enough to get the most basic behavior:
in img2img.py:
line 33: ` for i, images in enumerate([images[i:i+p.batch_size] for i in range(0, len(images), p.batch_size)]):`
line 41: `img = [Image.open(image) for image in images]`
line 43: `img = [ImageOps.exif_transpose(i) for i in img]`
line 44: `p.init_images = img`
line 50: `for n, (processed_image, image) in enumerate(zip(proc.images, images)):`

But then, more changes are required to make masking work properly. Instead of having a single mask for all the images, we'd need one mask *per* image.
Repository: deno
Issue: deno v1.25 and above hangs on specific machines with no error
Labels: needs investigation

Hello. I use some vim plugins which depend deno. Recently, everything broke on some machines when I updated deno and I have found that on some remote machines v1.25 breaks...


with version 1.24.3 I get this output,
```
$ deno run https://deno.land/std/examples/welcome.ts
Welcome to Deno!
``` 

But with any version >= 1.25 I get the following:

```
remote$ deno run https://deno.land/std/examples/welcome.ts
# ...hangs for many minutes before completing                                                                                                                       
```

This only happens on some remote machines, and my desktop handles all versions correctly. Any idea what could be causing this?
Repository: yt-dlp
Issue: [feature request] auto increment duplicate file 
Labels: enhancement

### DO NOT REMOVE OR SKIP THE ISSUE TEMPLATE

- [x] I understand that I will be **blocked** if I remove or skip any mandatory\* field

### Checklist

- [X] I'm requesting a feature unrelated to a specific site
- [X] I've looked through the [README](https://github.com/yt-dlp/yt-dlp#readme)
- [X] I've verified that I'm running yt-dlp version **2022.10.04** ([update instructions](https://github.com/yt-dlp/yt-dlp#update)) or later (specify commit)
- [X] I've searched the [bugtracker](https://github.com/yt-dlp/yt-dlp/issues?q=) for similar issues **including closed ones**. DO NOT post duplicates
- [X] I've read the [guidelines for opening an issue](https://github.com/yt-dlp/yt-dlp/blob/master/CONTRIBUTING.md#opening-an-issue)

### Provide a description that is worded well enough to be understood

I am well aware there are ` %(epoch)s` options available but it results in filenames too long to prevent false positives of duplicate files possibility.
The thing is it happens too frequently m3u8 is named `index.m3u8`, example link ends with https://xxx/index.m3u8 which yt-dlp will think it's downloaded even if they are totally different videos. 
Can yt-dlp add a feature so that duplicate file is auto increment?


example
```
index 0.mp4
index 1.mp4
index 2.mp4
and so on
```

thanks for keeping yt-dlp updated, I truly appreciate it

### Output

```shell
[generic] index: Downloading webpage
WARNING: [generic] Falling back on generic information extractor
[generic] index: Downloading m3u8 information
[info] index: Downloading 1 format(s): 0
[download] index [index].mp4 has already been downloaded
```


I think picture speaks thousand words, say I have a third video of this url:
`http://39.135.138.59:18890/PLTV/88888910/224/3221225664/index.m3u8`

on 9 Nov I downloaded 2 different videos `index.mp4` and `index 00001.mp4` as in screenshot below with different size. Now if I download third URL, ytdl will think this url belong to the same file
![image](https://user-images.githubusercontent.com/24624808/201115224-866c0240-3316-47a8-baee-8e0d5bd1f217.png)
<br>
I don't have debug environment so I can't make a pull request, but lemme post the autonumber code below:
```py
if (autorename)
    fileNumber = 0
    while os.path.exists(f'{title} {fileNumber}.{ext}'):
        fileNumber += 1

#only start to download and save the file as   f'{title} {fileNumber}.{ext}'
```
Repository: kubernetes
Issue: applyconfiguration: support conversion to corresponding type
Labels: sig/api-machinery,kind/feature,help wanted,triage/accepted

### What would you like to be added?

Several tests use a builder pattern with manually written helper functions to populate objects with certain fields set. That code pre-dates the introduction of the generated applyconfiguration packages (like `k8s.io/client-go/applyconfigurations/core/v1`).

Now applyconfiguration can do almost the same thing, but there are gaps:
- no DeepCopy: this is useful to duplicate some base object, then add some fields without modifying the original
- no conversion to the corresponding type: that is the last step in the builder pattern
- no implementation of meta.Object: would be useful for some generic functions

Conversion can be done right now, but it still depends on manually writing code like this:
```
func toNode(in *corev1ac.NodeApplyConfiguration) *corev1.Node {
	out := &corev1.Node{}
	convert(in, out)
	return out
}

// convert can convert between an apply configuration and the corresponding object
// (in both directions!) by first marshaling to and and from JSON. That works because
// both types have the same JSON encoding.
func convert(in, out interface{}) {
	buffer, err := json.Marshal(in)
	if err != nil {
		panic(fmt.Errorf("encoding %T as JSON: %v", in, err))
	}
	if err := json.Unmarshal(buffer, out); err != nil {
		panic(fmt.Errorf("decoding %T from JSON: %v", out, err))
	}
}
```



### Why is this needed?

Less code to be written manually when adding new types or fields.
Repository: stable-diffusion-webui
Issue: [Feature Request]: Migrate Web-UI to Accelerate
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 ?

[Migrating to accelerate ](https://huggingface.co/docs/accelerate/basic_tutorials/migration)could alleviate certain problems within the project:

1. Memory management
#234
#3250
#2082  
#3045

While optimizations like `--medvram` and `--lowvram` do their job, it would be much more efficient for the end user if this was handled without requiring a restart. *Especially when making this change requires editing webui-user.bat/sh (which also can be overwritten by updating/git pull)*. Accelerate internally uses [toma](https://github.com/BlackHC/toma), which does things like automatically lower batch size in the case of an OOM exception. 

---

2. Offload burden of device detection/specific support (such as with M1 Macs) from the project itself.  
#3427

Less time could be spent dealing with hardware specific bugs and more time spent on resolving hardware agnostic ones.

---

3. Allow for distributed computing

![distributed computing with pytorch example](https://pytorch.org/tutorials/_images/broadcast.png)
*https://pytorch.org/tutorials/intermediate/dist_tuto.html#collective-communication*

This would allow users to use physically separate machines working in tandem in order to generate outputs. It could be a game changer for those that have a lower spec device but might still have additional applicable hardware in a separate system. For that reason, I think it may be naive to think that distributed computing is reserved only for large companies or organizations. The average user might be able to appreciate the opportunity to turn their otherwise useless collection of old family computers into a stable diffusion compute cluster.


### Regardless
Even if Accelerate doesn't fit this project, I still think that the three subjects I listed above should be addressed.



*This is a FR and not a draft for a reason. I do not claim to currently have a very deep understanding of the already existing implementation of some of the project components listed above.*

### Proposed workflow

1. Use the webui as normal (according to user)

### Additional information

_No response_
Repository: transformers
Issue: Compact Transformer
Labels: New model

### Model description

# Escaping the Big Data Paradigm with Compact Transformers

Abstract :

> With the rise of Transformers as the standard for language processing, and their advancements in computer vision, there has been a corresponding growth in parameter size and amounts of training data. Many have come to believe that because of this, transformers are not suitable for small sets of data. This trend leads to concerns such as: limited availability of data in certain scientific domains and the exclusion of those with limited resource from research in the field. In this paper, we aim to present an approach for small-scale learning by introducing Compact Transformers. We show for the first time that with the right size, convolutional tokenization, transformers can avoid overfitting and outperform state-of-the-art CNNs on small datasets. Our models are flexible in terms of model size, and can have as little as 0.28M parameters while achieving competitive results. Our best model can reach 98% accuracy when training from scratch on CIFAR-10 with only 3.7M parameters, which is a significant improvement in data-efficiency over previous Transformer based models being over 10x smaller than other transformers and is 15% the size of ResNet50 while achieving similar performance. CCT also outperforms many modern CNN based approaches, and even some recent NAS-based approaches. Additionally, we obtain a new SOTA result on Flowers-102 with 99.76% top-1 accuracy, and improve upon the existing baseline on ImageNet (82.71% accuracy with 29% as many parameters as ViT), as well as NLP tasks. Our simple and compact design for transformers makes them more feasible to study for those with limited computing resources and/or dealing with small datasets, while extending existing research efforts in data efficient transformers.

### Open source status

- [X] The model implementation is available
- [X] The model weights are available

### Provide useful links for the implementation

Paper : https://arxiv.org/pdf/2104.05704.pdf
Github repository : https://github.com/SHI-Labs/Compact-Transformers
Repository: rustdesk
Issue: mount local drive to remote (usb redirect)
Labels: enhancement,help wanted

hi.

RDP and maybe vnc i think has this mount drive feature,  you set a "mount this folder" and that gets shown to the remote machine as a local drive.

we can copy/paste to that redirected mount as if it is a local drive without having  to open file transfer window
Repository: godot
Issue: Text mesh gets displaced when loading it after the viewport window has been resized.
Labels: bug,topic:core,topic:rendering,topic:3d

### Godot version

4.0 Beta 4

### System information

Windows 10; AMD Ryzen 7 3700X 8-Core Processor 3.60 GHz

### Issue description

Loading a scene that has a text mesh in it after you've resized the window causes the textmesh to be displaced from it's collision shape.

### Steps to reproduce

1. Download sample project
2. Turn on visible collision shapes
3. Launch the game. click load map. Observe the placement of the collision shape relative to the text mesh
4. Launch the game again, but this time, before clicking on load map, make your game window fullscreen. Then click on load map and observe as the shape is out of place.

### Minimal reproduction project

[TextMeshBug.zip](https://github.com/godotengine/godot/files/9969811/TextMeshBug.zip)
Repository: react-native
Issue: Baseline alignment does not respect numberOfLines text property
Labels: Needs: Triage :mag:,Never gets stale

### Description

This issue is Android specific.

Whenever using `alignItems: 'baseline'` on a container which has a text element in it with `numberOfRows` specified, if that text does get truncated, then the container will be as large as the (partly invisible) text is.

### Version

0.70.5

### Output of `npx react-native info`

System:
    OS: macOS 12.5.1
    CPU: (12) x64 Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz
    Memory: 168.17 MB / 16.00 GB
    Shell: 3.3.1 - /usr/local/bin/fish
  Binaries:
    Node: 16.14.2 - /usr/local/bin/node
    Yarn: 1.22.10 - /usr/local/bin/yarn
    npm: 8.5.3 - /usr/local/bin/npm
    Watchman: 2022.10.17.00 - /usr/local/bin/watchman
  Managers:
    CocoaPods: 1.11.3 - /usr/local/bin/pod
  SDKs:
    iOS SDK:
      Platforms: DriverKit 22.1, iOS 16.1, macOS 13.0, tvOS 16.1, watchOS 9.1
    Android SDK: Not Found
  IDEs:
    Android Studio: 2020.3 AI-203.7717.56.2031.7583922
    Xcode: 14.1/14B47b - /usr/bin/xcodebuild
  Languages:
    Java: 11.0.11 - /usr/bin/javac
  npmPackages:
    @react-native-community/cli: Not Found
    react: 18.1.0 => 18.1.0
    react-native: 0.70.5 => 0.70.5
    react-native-macos: Not Found
  npmGlobalPackages:
    *react-native*: Not Found

### Steps to reproduce

Wrap a `Text` component with a `View` component which has `flexDirection` set to `row` and `alignItems` set to `baseline`. Make sure the text contains enough text to at least be rendered on 2 lines. Add `numberOfLines` with the value of `1` on that text. Notice that the container is larger than you'd expect. You'd expect it to be only 1 line high.

### Snack, code example, screenshot, or link to a repository

https://snack.expo.dev/LNZyYKCWg
Repository: pytorch
Issue: RuntimeError: method '__torch__.___torch_mangle_0.MyModule.sin' already defined.
Labels: oncall: package/deploy

### 🐛 Describe the bug

The following program runs into the following error. It appears that the script and trace somehow collide on their use of the global compilation unit? Adding `_compilation_unit=torch._C.CompilationUnit()` doesn't seem to help though.

```python
import torch


class MyModule(torch.nn.Module):
    @torch.jit.export
    def sin(self, x):
        return torch.ops.aten.sin(x)

module = MyModule()
example_input = torch.ones(2, 3)
torch.jit.script(module)
torch.jit.trace_module(module, {"sin": example_input})
```

```
Traceback (most recent call last):
  File "/tmp/repro2.py", line 12, in <module>
    torch.jit.trace_module(module, {"sin": example_input})
  File "/usr/local/google/home/silvasean/.local/lib/python3.10/site-packages/torch/jit/_trace.py", line 1049, in trace_module
    module._c._create_method_from_trace(
RuntimeError: method '__torch__.___torch_mangle_0.MyModule.sin' already defined.
```

### Versions

Collecting environment information...
PyTorch version: 1.14.0.dev20221028+cpu
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: Debian GNU/Linux rodete (x86_64)
GCC version: (Debian 12.2.0-3) 12.2.0
Clang version: 14.0.6-2
CMake version: version 3.24.1
Libc version: glibc-2.35

Python version: 3.10.7 (main, Sep  8 2022, 14:34:29) [GCC 12.2.0] (64-bit runtime)
Python platform: Linux-5.18.16-1rodete1-amd64-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
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==1.23.3
[pip3] torch==1.14.0.dev20221028+cpu
[pip3] torchvision==0.15.0.dev20221028+cpu
[conda] Could not collect
Repository: opencv
Issue: Java wrapper evolution in relation with the Panama project
Labels: feature,category: java bindings

### Descripe the feature and motivation

This is not directly a request for new functionality but more a reflection on the evolution of OpenCV wrappers in other languages.

I just [submitted a proposal](https://github.com/opencv/opencv/issues/22260) to keep the Java build compatible with Java 18+. This solution seems to me to be transitory because the [Panama project ](https://openjdk.org/projects/panama/)allows to consider automating the Java wrapping.

Even if the Panama project is not yet very mature, I think it is interesting to consider the [new possibilities](https://foojay.io/today/category/jeps/project-panama/).
The [jextract tool](https://github.com/openjdk/jextract) allows to create automatically the wrapping glue without using JNI but directly from new Java classes.

For the moment the tool allows to generate automatically the wrapper only with C headers.

I tried with success from the interface modules/core/include/opencv2/core/core_c.h. Here is my build configuration:
```
-D CV_CXX11
-D _CXX0X_WARNING_H
-t opencv
-lopencv
-I /usr/include/c++/11/
-I /home/nicolas/Workspaces/git/opencv/modules/core/include/
-I /home/nicolas/Workspaces/git/jextract/samples/opencv/include/
-I /home/nicolas/Apps/java/19/llvm-project-14.0.6.src/build/lib/clang/14.0.6/include
/home/nicolas/Workspaces/git/opencv/modules/core/include/opencv2/core/core_c.h
```

According to this [post](https://answers.opencv.org/question/17546/opencv-will-drop-c-api-support-soon/) the OpenCV C API has been buried. Wouldn't it be useful to have a C API again to simplify wrapping? Apparently for [Rust wrapping](https://github.com/twistedfall/opencv-rust), it's a similar problem.

### Additional context

_No response_
Repository: opencv
Issue: COLOR_YUV420sp2BGR  give me a rgb image
Labels: bug,incomplete,needs reproducer

### System Information

// example for python user
OpenCV python version: 4.6.0.66
Operating System / Platform: Ubuntu 20.04
Python version: 3.9.6

### Detailed description

I use  COLOR_YUV420sp2BGR to change image ,it should return a bgr image, but i got  a rgb image , is there something wrong?

### Steps to reproduce

origin_image = cam.get_img(2, 1920, 1080)
origin_nv12 = np.frombuffer(origin_image, dtype=np.uint8).reshape(1620, 1920)
origin_bgr = cv2.cvtColor(origin_nv12, cv2.COLOR_YUV420SP2BGR)

### Issue submission checklist

- [X] I report the issue, it's not a question
- [X] I checked the problem with documentation, FAQ, open issues, forum.opencv.org, Stack Overflow, etc and have not found any solution
- [X] I updated to the latest OpenCV version and the issue is still there
- [X] There is reproducer code and related data files (videos, images, onnx, etc)
Repository: deno
Issue: Error with module resolution loading `sharp` native module as dependency via `npm:` specifier
Labels: bug,node compat,node native extension

Trying to get `payload` running on Deno unstable. Module resolution seems to get confused on the `sharp` module (specifically loading its native component).

```bash
deno run --unstable --allow-read --allow-env index.ts
```

```
error: Uncaught Error:
Something went wrong installing the "sharp" module

Cannot find module '../build/Release/sharp-darwin-x64.node'
Require stack:
- /Users/x/Library/Caches/deno/npm/registry.npmjs.org/sharp/0.31.2/lib/sharp.js
- /Users/x/Library/Caches/deno/npm/registry.npmjs.org/sharp/0.31.2/lib/constructor.js
- /Users/x/Library/Caches/deno/npm/registry.npmjs.org/sharp/0.31.2/lib/index.js
- /Users/x/Library/Caches/deno/npm/registry.npmjs.org/payload/1.1.21/dist/uploads/generateFileData.js
- /Users/x/Library/Caches/deno/npm/registry.npmjs.org/payload/1.1.21/dist/collections/operations/create.js
- /Users/x/Library/Caches/deno/npm/registry.npmjs.org/payload/1.1.21/dist/collections/operations/local/create.js
- /Users/x/Library/Caches/deno/npm/registry.npmjs.org/payload/1.1.21/dist/collections/operations/local/index.js
- /Users/x/Library/Caches/deno/npm/registry.npmjs.org/payload/1.1.21/dist/index.js
- /Users/x/Library/Caches/deno/npm/registry.npmjs.org/payload/1.1.21/dist/index.js

Possible solutions:
- Install with verbose logging and look for errors: "npm install --ignore-scripts=false --foreground-scripts --verbose sharp"
- Install for the current darwin-x64 runtime: "npm install --platform=darwin --arch=x64 sharp"
- Consult the installation documentation: https://sharp.pixelplumbing.com/install
    at Object.<anonymous> (file:///Users/x/Library/Caches/deno/npm/registry.npmjs.org/sharp/0.31.2/lib/sharp.js:34:9)
    at Object.<anonymous> (file:///Users/x/Library/Caches/deno/npm/registry.npmjs.org/sharp/0.31.2/lib/sharp.js:37:4)
    at Module._compile (deno:ext/node/02_require.js:717:36)
    at Object.Module._extensions..js (deno:ext/node/02_require.js:750:12)
    at Module.load (deno:ext/node/02_require.js:634:34)
    at Function.Module._load (deno:ext/node/02_require.js:491:14)
    at Module.require (deno:ext/node/02_require.js:656:21)
    at require (deno:ext/node/02_require.js:790:18)
    at Object.<anonymous> (file:///Users/x/Library/Caches/deno/npm/registry.npmjs.org/sharp/0.31.2/lib/constructor.js:8:1)
    at Object.<anonymous> (file:///Users/x/Library/Caches/deno/npm/registry.npmjs.org/sharp/0.31.2/lib/constructor.js:425:4)
```

Minimal example to reproduce:

```ts
import express from 'npm:express@4.18.2'
import payload from 'npm:payload@1.1.21'

const app = express();

payload.init({
  secret: process.env.PAYLOAD_SECRET,
  mongoURL: process.env.MONGO_URL,
  express: app,
});

app.listen(process.env.PAYLOAD_PORT, process.env.PAYLOAD_ADDR, async () => {
  console.log(`listening on http://${process.env.PAYLOAD_ADDR}:${process.env.PAYLOAD_PORT}/`);
});
```
Repository: next.js
Issue: Docs: Improve Dynamic Import page by including information about DynamicOptions
Labels: Lazy Loading

### What is the improvement or update you wish to see?

In [Advanced Features > Dynamic Imports](https://nextjs.org/docs/advanced-features/dynamic-import) there is a small section about disabling SSR, which is great, but there is no mention anywhere about `DynamicOptions` and how `loading:` could be used in conjunction with `ssr: false` to have a proper UX while dynamic component is being fetched.

While struggling with the issue (and not just me: #36636) I found that following accomplishes what I needed (unless I am completely wrong, as I'm just starting with Next & React):

```typescript
const HandsontableWrapper = dynamic(
    () => import('./handsontableWrapper'),
    { ssr: false, loading: () => <h1>loading...</h1> }
);
```

Only later I found that the [example code](https://github.com/vercel/next.js/blob/canary/examples/with-dynamic-import/pages/index.tsx) linked in docs actually include same approach 😑

### Is there any context that might help us understand?

This bug about not being able to use `Supsense` with `ssr: false` has all the details #36636 

Last question in that discussion, for a workaround, was left unanswered and the ticket was closed due to inactivity (same day that question was posted 😑). The answer however seems quite easy...

### Does the docs page already exist? Please link to it.

https://nextjs.org/docs/advanced-features/dynamic-import