sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: neovim
Issue: Incomplete Documentation for `vim.diagnostic.open_float` Options
Labels: bug,documentation

### Problem

The documentation for `vim.diagnostic.open_float` appears to be incomplete or incorrectly formatted.
The option descriptions seem to be truncated or missing some fields.

Additionally, some relevant options such as `focus_id` and `focusable` are missing but are present in the `vim.lsp.util.open_floating_preview`.
It is unclear whether these options are intended to be supported by `vim.diagnostic.open_float` or not.

Note: I found that we can call `vim.diagnostic.open_float({focusable=true})` and observe that it behaves similarly to `vim.lsp.buf.hover()`, allowing focus to be set on the floating window with a second call.

### Steps to reproduce

1. Open Neovim.
1. Execute :help vim.diagnostic.open_float to view the help documentation.
1. Observe the incomplete descriptions for the options.

### Expected behavior

We can understand right behaviors and options of `vim.diagnostic.open_float`.

### Neovim version (nvim -v)

NVIM v0.11.0-dev-78d3f47

### Vim (not Nvim) behaves the same?

no

### Operating system/version

Arch Linux

### Terminal name/version

WezTerm

### $TERM environment variable

xterm-256color

### Installation

build from repo
Repository: vscode
Issue: Proposal: make the existing feature "auto-escape highlighted text for regex when triggering find in regex mode" work even when highlighted text spans multiple lines
Labels: feature-request,editor-find

<!-- ⚠️⚠️ 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. -->

The feature in question was added in 2016 and is very convenient.

> Auto-escape highlighted text for search and replace regex mode
> https://github.com/Microsoft/vscode/issues/6149 

Currently it only works if the selected text is confined to a single line.

I'd like to propose a small improvement of this feature so it works even when selected text spans more than one line. Thus, if I highlight the following range:

![image](https://github.com/microsoft/vscode/assets/148152939/37f70525-7f5c-43bf-91dd-64641991b6d8)

in the editor, and press ctrl+f, and if my find is already in regex mode, the find input box should be populated by `abc\.\n123` (or `abc\.\r\n123`, depending on what character(s) actually lie between `abc.` and `123`)
Repository: stable-diffusion-webui
Issue: [Bug]:  The train tab is outdated
Labels: bug-report

### Checklist

- [ ] The issue exists after disabling all extensions
- [X] The issue exists on a clean installation of webui
- [ ] The issue is caused by an extension, but I believe it is caused by a bug in the webui
- [X] The issue exists in the current version of the webui
- [ ] The issue has not been reported before recently
- [X] The issue has been reported before but has not been fixed yet

### What happened?

cant create embedding

### Steps to reproduce the problem

its working only if --disable-safe-unpickle !!

### What should have happened?

the embedding name in the train tab has to be created like before

### What browsers do you use to access the UI ?

_No response_

### Sysinfo

system

### Console logs

```Shell
File "C:\stable-diffusion-webui\modules\textual_inversion\textual_inversion.py", line 203, in load_from_dir
        self.load_from_file(fullfn, fn)
      File "C:\stable-diffusion-webui\modules\textual_inversion\textual_inversion.py", line 184, in load_from_file
        embedding = create_embedding_from_data(data, name, filename=filename, filepath=path)
      File "C:\stable-diffusion-webui\modules\textual_inversion\textual_inversion.py", line 284, in create_embedding_from_data
```


### Additional information

Outdated train tab need an update , when it can be fixed
Repository: transformers
Issue: Seq TransfoRNA
Labels: New model

### Model description

The Seq TransfoRNA model is intended to map short RNA fragments (18 nt. to 30 nt.) back to their parental precursor. The model is trained in a supervised manner to classify ~2k subclasses belonging to 11 major classes.

### Open source status

- [X] The model implementation is available
- [X] The model weights are available

### Provide useful links for the implementation
https://github.com/gitHBDX/TransfoRNA/tree/master/transforna/src/model
@yak-hbdx @jje-hbdx @tos-hbdx
Repository: ollama
Issue: user is not in the sudoers file - add support to install on debian 12
Labels: bug

### What is the issue?

Debian 12

```
$ curl -fsSL https://ollama.com/install.sh | sh
>>> Downloading ollama...
######################################################################## 100.0%######################################################################### 100.0%
>>> Installing ollama to /usr/local/bin...
[sudo] password for user: 
user is not in the sudoers file.
```

```
$ su
$ curl -fsSL https://ollama.com/install.sh | sh
>>> Downloading ollama (again)...
######################################################################## 100.0%######################################################################### 100.0%
>>> Installing ollama to /usr/local/bin...
>>> Creating ollama user...
sh: 88: useradd: not found
>>> The Ollama API is now available at 127.0.0.1:11434.
>>> Install complete. Run "ollama" from the command line.
$ systemctl status ollama
Unit ollama.service could not be found.
```

```
$ su -
$ curl -fsSL https://ollama.com/install.sh | sh
>>> Downloading ollama ... again ...
######################################################################## 100.0%######################################################################### 100.0%
>>> Installing ollama to /usr/local/bin...
>>> Creating ollama user...
>>> Adding ollama user to render group...
>>> Adding ollama user to video group...
>>> Adding current user to ollama group...
>>> Creating ollama systemd service...
>>> Enabling and starting ollama service...
Created symlink /etc/systemd/system/default.target.wants/ollama.service → /etc/systemd/system/ollama.service.
>>> NVIDIA GPU installed.
$ systemctl status ollama
● ollama.service - Ollama Service
     Loaded: loaded (/etc/systemd/system/ollama.service; enabled; preset: enabl>
     Active: active (running) since Mon 2024-06-10 12:30:24 CEST; 14s ago
   Main PID: 99053 (ollama)
      Tasks: 18 (limit: 38237)
     Memory: 590.0M
        CPU: 3.004s
     CGroup: /system.slice/ollama.service
             └─99053 /usr/local/bin/ollama serve
```

### OS

Linux

### GPU

Nvidia

### CPU

Intel

### Ollama version

latest
Repository: three.js
Issue: TSL: Some names for a TSL function break the syntax of translated WGSL or GLSL code
Labels: TSL

### Description

When a TSL code is translated into GLSL or WGSL some identifiers may break the code, because they collide with keywords or reserved words. For example, a TSL function named `fn` triggers the following error message:
```
Error while parsing WGSL: :17:4 error: expected identifier for function declaration
fn fn (  ) -> f32 {
   ^^
```
because `fn` is a [keyword in WGSL](https://www.w3.org/TR/WGSL/#keyword-summary).

#### Possible solutions:
* Throw error or show warning when a keywords or reserved word is used &ndash; the (b)locked identifiers for GLSL and WGSL are different
* Safeguard (all or only problematic) names by appending a prefix or suffix, e.g. `fn` may become `fn_tsl`
* Add a warning in the documentation for users to avoid such identifiers
* Leave it as it is

### Reproduction steps

#### Example with WGSL:

1. Open [WebGPU TSL Editor](https://threejs.org/examples/#webgpu_tsl_editor) and make sure the output in the right panel is set to `WGSL`
2. Copy the following code in the left panel:
```
const { tslFn, vec4 } = TSL;

const fn = tslFn( () => { return 1; } );

fn.setLayout( {
	name: 'fn',
	type: 'float',
	inputs: []
} );

output = vec4( 1, 1, 1, fn() );
```
3. Observe the translated code in the right panel. It contains:
```
// codes
fn fn (  ) -> f32 {
	return 1.0;
}
```
4. Check the JS console. It shows error message
```
Error while parsing WGSL: :17:4 error: expected identifier for function declaration
fn fn (  ) -> f32 {
   ^^

:42:48 error: expected expression for function call
  output.color = vec4<f32>( 1.0, 1.0, 1.0, f32( fn(  ) ) );
                                                ^^


 - While validating [ShaderModuleDescriptor "fragment"]
 - While calling [Device].CreateShaderModule([ShaderModuleDescriptor "fragment"]).
```

#### Example with GLSL:

1. Open [WebGPU TSL Editor](https://threejs.org/examples/#webgpu_tsl_editor) and make sure the output in the right panel is set to `GLSL`
2. Copy the following code in the left panel:
```
const { tslFn, vec4 } = TSL;

const main = tslFn( () => { return 1; } );

main.setLayout( {
	name: 'main',
	type: 'float',
	inputs: []
} );

output = vec4( 1, 1, 1, main() );
```
3. Observe the translated code in the right panel. It contains two definitions of `main`
4. Check the JS console. It shows error message
```
Compilation log for [Invalid ShaderModule "fragment"]:
1 error(s) generated while compiling the shader:
:29:1 error: redeclaration of 'main'
fn main(  ) -> OutputStruct {
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
```

### Code

See "Reproduction steps"

### Live example

See "Reproduction steps"


### Screenshots

![image](https://github.com/mrdoob/three.js/assets/70705048/361f3c4e-238c-432b-b3d6-625c643bb368)


### Version

r165

### Device

Desktop

### Browser

Chrome

### OS

Windows
Repository: PowerToys
Issue: [information] solution of sticky keys issue
Labels: Issue-Bug,Needs-Triage

### Microsoft PowerToys version

0.81.1

### Installation method

GitHub

### Running as admin

Yes

### Area(s) with issue?

Keyboard Manager

### Steps to reproduce

> **Keyboard Manager sticky keys issues are fixed by always run as administrator mode in Power Toy settings!**

ㅤ
If you use three combinations of keystrokes including a **control**, **alt**, **shift**, 
the sticky keys that have already been disabled in the system settings are secretly activated
I don't know why it's sticky. someone knows please explain

In my case,
I changed the windows key arrangement to use as a Mac key arrangement,
but there was a problem with the control and shift getting sticky
 
<img src="https://github.com/microsoft/PowerToys/assets/76217191/07978d38-68b4-484b-bd3f-12c056e0c2ff" width="400">

According to the Official user documentation
scenarios PowerToys will not work in is Intercepting certain types of keyboard strokes

so, solution of sticky keys is go to PowerToys Settings - General - Administrator mode and enable Always run as administrator 

hope you enjoy this tool happily

### ✔️ Expected Behavior

The changed key array, **alt**+**shift**+**ESC**, should normally deliver **control**+**shift**+**ESC** and open task manager

### ❌ Actual Behavior

It delivers successfully and the task manager opened, but the **control** or **shift** keys are sticky

### Other Software

_No response_
Repository: pytorch
Issue: UnsupportedOperatorError: Exporting the operator 'quantized::batch_norm2d' to ONNX opset version 14 is not supported.
Labels: module: onnx,triaged

### 🐛 Describe the bug

```
model = MultiTaskUnet(n_input_channels=n_input_channels, output_tasks=list(fields_to_predict.keys()),
                          autoencoder_task=False, init_features=32)
model.train()
torch.backends.quantized.engine = "qnnpack"
model.qconfig = torch.quantization.get_default_qconfig('qnnpack')

model_prepared = torch.quantization.prepare(model)

model_int8 = torch.quantization.convert(model_prepared)
model_int8.eval()
input_fp32 = torch.randn(1, n_input_channels, *target_size)
res = model_int8(input_fp32)


dummy_input = torch.zeros(1, 4, 224, 224)
dynamic_axes = {'input': {0: 'batch'}, 'output': {0: 'batch'}}
torch.onnx.export(model_int8, dummy_input, 'model_onnx.onnx', input_names=['input'], output_names=['output'],
                          dynamic_axes=dynamic_axes)

```

UnsupportedOperatorError: Exporting the operator 'quantized::batch_norm2d' to ONNX opset version 14 is not supported. 

### Versions

Apple M1 Pro

 flake8==7.0.0
 mypy==1.8.0
mypy-extensions==1.0.0
numpy==1.26.4
onnx==1.15.0
onnxruntime==1.12.1
pytorch-lightning==2.0.6
pytorch-memlab==0.3.0
sagemaker_pytorch_training==2.8.1
torch==2.0.1
torchdata==0.6.1
torchmetrics==0.7.3
torchtext==0.15.2
torchvision==0.15.2
Repository: PowerToys
Issue: [New feature] Overlay 
Labels: Idea-New PowerToy,Needs-Triage

### Description of the new feature / enhancement

Add a possibility to make custom overlay (like OBS for exemple) with text or image.

These overlay will be linked to one or all application and will let click through

### Scenario when this would be used?

I have one personal scenario case that can be applied to any other application.

I started play to Destiny 2, and I screw between all my special attack.

To avoid that, I've taken a screenshot of my interface and add which key is linked.

![17_50_29](https://github.com/microsoft/PowerToys/assets/8930930/3cf730e5-b373-4c1e-8851-e180b27e50b5)

I think that can also be used when you start using learn the shortcuts of a software.

Even with a dual screen, sometimes you need to have the data needed right in the vision area

### Supporting information

_No response_
Repository: pytorch
Issue: Cannot export onnx model with autograd.functional.jacobian
Labels: module: onnx,triaged,onnx-triaged,onnx-needs-info

### 🐛 Describe the bug

Good morning,

I have the following simple model:

```
import torch

class Model1(torch.nn.Module):
    def __init__(self):
         super(Model1, self).__init__()

    def forward(self, X):
        return X[:,0] ** 2 + X[:, 1]
        
class F(torch.nn.Module):
    def __init__(self, model: torch.nn.Module):
        super(F, self).__init__()
        self.model = model

    def forward(self, X):
        out = self.model(X)
        grad = torch.autograd.functional.jacobian(self.model, X)
        return out, torch.flatten(grad, 1)
    
f = F(Model1())
X = torch.arange(6).reshape(3, 2).to(torch.float32)
out, grad = f(X)
print("input:\n", X)
print("output:\n", out)
print("gradient:\n", grad)
``` 

I am trying to export it to onnx. 

When I use old-school export

```
torch.onnx.export(f, X, 'test.onnx')
```

i get the following error
``` 
UnsupportedOperatorError: Exporting the operator 'aten::select_backward' to ONNX opset version 17 is not supported
```

when i try to use dynamo export as following:

```
dynamo_model = torch.onnx.dynamo_export(f, X)
``` 
i get the following error

``` 
NotImplementedError                       Traceback (most recent call last)
File [<redacted>/torch/_dynamo/utils.py:621](<redacted>/torch/_dynamo/utils.py#line=620), in proxy_args_kwargs(args, kwargs)
    620 try:
--> 621     proxy_args = tuple(arg.as_proxy() for arg in args)
    622     proxy_kwargs = {key: arg.as_proxy() for key, arg in kwargs.items()}

File [<redacted>/torch/_dynamo/utils.py:621](<redacted>/torch/_dynamo/utils.py#line=620), in <genexpr>(.0)
    620 try:
--> 621     proxy_args = tuple(arg.as_proxy() for arg in args)
    622     proxy_kwargs = {key: arg.as_proxy() for key, arg in kwargs.items()}

File [<redacted>/torch/_dynamo/variables/base.py:301](<redacted>/torch/_dynamo/variables/base.py#line=300), in VariableTracker.as_proxy(self)
    300 def as_proxy(self):
--> 301     raise NotImplementedError(str(self))

NotImplementedError: NNModuleVariable()

``` 

please advise on how to export this model to onnx. Any help would be greatly appreciated. 

### Versions

```
[pip3] onnx==1.16.0
[pip3] torch==2.3.1
```
Repository: kubernetes
Issue: kubectl delete a large number of objects taking too long
Labels: sig/api-machinery,kind/feature,sig/cli,triage/accepted

### What would you like to be added?

Make `kubectl delete` execute more efficiently. There are two options to make it happen:

1. implement `deletecollection` in kubectl instead of one-by-one deletions
2. Make one-by-one deletions concurrent

Some history digging found `deletioncollection` was not preferable to some extent https://github.com/kubernetes/kubernetes/issues/90743

### Why is this needed?

kubectl delete a large number of objects taking too long. For example, deleting about 650 events takes 3m49.077s whereas using `deletecollection` only takes 6s
Repository: godot
Issue: 2D editor canvas lines don't update after changing Viewport Width/Height in the Project Settings until you click the 2D editor viewport
Labels: bug,topic:editor,topic:2d

### Tested versions

- Reproducible in: 3.6.beta5, 4.2.2.stable, 4.3.beta1, 4.3.beta 5833f5978

### System information

Godot v4.3.beta (5833f5978) - Fedora Linux 39 (KDE Plasma) - X11 - Vulkan (Forward+) - dedicated NVIDIA GeForce RTX 4090 (nvidia; 550.78) - 13th Gen Intel(R) Core(TM) i9-13900K (32 Threads)

### Issue description

The purple 2D editor canvas lines don't update after changing Window Width/Height in the Project Settings until you click the 2D editor viewport.

https://github.com/godotengine/godot/assets/180032/0f68a0fd-0e05-4f54-a108-9cad84a0d045

Camera2D editor lines suffer a similar issue, but it's worse because they don't update until you reload the saved scene.


### Steps to reproduce

- Create a 2D scene with any node.
- In the project settings, change **Display > Window > Size > Viewport Width** while keeping the 2D editor viewport visible behind the dialog.
- Notice how the purple guidelines don't move until you click the 2D editor viewport (regardless of whether Project Settings are open or not).

### Minimal reproduction project (MRP)

N/A
Repository: godot
Issue: Area2Ds are slow-ish at detecting bodies
Labels: topic:physics,needs testing,topic:2d

### Tested versions

Reproducible in Godot v4.3.beta1

### System information

Windows 11 - Godot v4.3.beta1 - OpenGL (Compatibility)

### Issue description

I have a player for my Mario fan game, and i use staticbodies as blocks. To detect if the player is on the bottom side of a block, i use an area2d. The player can sprint; and when he hits the area2d's block on the edge in a specific pixel (probably) while sprinting, it doesn't bump.

If I don't sprint however, everything works just fine. So my theory is that the player hits and leaves the area so fast that the area2d below the block doesn't detect it immediately.

### Steps to reproduce

1. Make simple player for platfomer games (that can walk, jump, and run at least), and assign it to a group

2. Make the question block using a staticbody2d and add an area2d under the block so that the player can interact with it from the bottom side.

3. connect the area2d's _body_entered(body:Node2D) signal to the block script, and then write this:
```
    for player in get_tree().get_nodes_in_group("player_group_name"):
      if body == player:
        print("bump);
```

4. Play the game and test it, by jumping to the edge of the block a few times until it doesn't print anything.

Here's a project i made rapidly that showcases the issue, i also added a footage inside the file so you can see how to achieve the problem.

[area2d-issue.zip](https://github.com/user-attachments/files/15774271/area2d-issue.zip)
Repository: tensorflow
Issue: Aborted (core dumped) in `tf.experimental.numpy.diag/tf.compat.v1.linalg.diag/tf.experimental.numpy.diagflat/tf.keras.ops.diag`
Labels: stat:awaiting tensorflower,type:bug,comp:ops,TF 2.16

### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

tf 2.16

### Custom code

Yes

### OS platform and distribution

Linux Ubuntu 22.04.3 LTS (x86_64)

### Mobile device

_No response_

### Python version

3.9.13

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

Under specific input, tf.experimental.numpy.diag/tf.compat.v1.linalg.diag/tf.experimental.numpy.diagflat/tf.keras.ops.diag encounters "Aborted (core dumped)".

### Standalone code to reproduce the issue

```shell
import tensorflow as tf

input_dict = {
    'diagonal': tf.constant([1,2], dtype=tf.int32),
    'k': tf.constant([1, 2], dtype=tf.int32)
}

# crash
tf.experimental.numpy.diag(
    v=tf.constant([1,2], dtype=tf.int32), k=tf.constant([1, 2], dtype=tf.int32)
)

# crash
# tf.compat.v1.linalg.diag(
#     diagonal=tf.constant([1,2], dtype=tf.int32),
#     name='diag',
#     k= tf.constant([1, 2], dtype=tf.int32),
#     num_rows=-1,
#     num_cols=-1,
#     padding_value=0,
#     align='RIGHT_LEFT'
# )

# crash
# tf.experimental.numpy.diagflat(
#     v=tf.constant([1,2], dtype=tf.int32), k=tf.constant([1, 2], dtype=tf.int32)
# )

# crash
# tf.keras.ops.diag(
#     x=tf.constant([1,2], dtype=tf.int32), k=tf.constant([1, 2], dtype=tf.int32)
# )
```


### Relevant log output

```shell
2024-06-10 13:23:09.021306: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-10 13:23:10.029041: F tensorflow/core/framework/tensor_shape.cc:356] Check failed: d >= 0 (0 vs. -1)
Aborted (core dumped)
```
Repository: neovim
Issue: Which plugins use :UpdateRemotePlugins ?
Labels: enhancement,api,plugin,project-management,remote-plugin

## User feedback request

Your feedback is requested to help us understand what "remote plugins" you are using.

Which plugins do you use that require `:UpdateRemotePlugins` ? 

Please comment below with a URL to the plugin(s). Or upvote 👍 comments that already mention plugins that you are using.

This will help us migrate existing "remote plugins" to the new model proposed in https://github.com/neovim/neovim/issues/27949
Repository: transformers
Issue: Object Detection Pipeline only outputs first element when batching
Labels: Core: Pipeline,Good First Issue,Vision

### System Info

- `transformers` version: 4.41.2
- Platform: Linux-5.4.0-182-generic-x86_64-with-glibc2.31
- Python version: 3.11.8
- Huggingface_hub version: 0.23.0
- Safetensors version: 0.4.2
- Accelerate version: 0.30.1
- Accelerate config:    not found
- PyTorch version (GPU?): 2.2.2+cu121 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>


When running the ObjectDetectionPipeline in a batch, the output will only be the bounding boxes of the first input image due to ObjectDetectionPipeline.py accessing element [0] in postprocessing  and not looping over all outputs. 

https://github.com/huggingface/transformers/blob/a4e1a1d02894b8d801f5d0182e1979b55daaeaa4/src/transformers/pipelines/object_detection.py#L150


This accesses only and always the first element, instead of looping over all outputs. 

Only the first element is accessed in postprocessing

### Who can help?

@Narsil 

### Information

- [ ] The official example scripts
- [ ] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

1. Create object detection Pipeline

`    pipe = pipeline("object-detection", model=model_name, image_processor=preprocessor_name, device=device)
`

2. use batch inference with batch_size > 2.
`
 for out in tqdm(pipe(dataset, batch_size=batch_size)):
`





### Expected behavior

Expected Output: 2 Elements with each x items (bboxes).
 Actual Output, only bboxes of the first input element.