sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: godot
Issue: Godot 4.2.1 crashes without error report possibly due to a GPU issue
Labels: bug,topic:editor,crash

### Tested versions

- Reproducible in: 4.2.1-stable, 3.5.3-stable

### System information

Godot v4.2.1.stable - Windows 10.0.19045 - Vulkan (Forward+) - dedicated NVIDIA GeForce GTX 1660 Ti with Max-Q Design (NVIDIA; 31.0.15.5123) - Intel(R) Core(TM) i5-10300H CPU @ 2.50GHz (8 Threads)

### Issue description

Recently Godot used to run completely fine since when i started using since 3.4, but over time since weeks ago Godot begins to crash or hang instantly indipendent of what thing i'm working on, including both the engine and the games that are made in it, no matter if 2D or 3D.

At first i believed it would have something to do with the program itself, but even if you don't do anything on it, after a long period of time of keeping Godot Engine opened (or any other application open), Godot mysteriously hangs, and can't open it anymore unless i restart my PC or keep my PC turned off for a while.

My GPU is a NVIDIA GeForce GTX 1660 Ti Max-Q, while my CPU is an Intel Core i5-10300H.

I also tried to run Godot from cmd using the --verbose flag, but it only prints this
<code>
TextServer: Added interface "Dummy"
TextServer: Added interface "ICU / HarfBuzz / Graphite (Built-in)"
Native OpenGL API detected: 3.3: NVIDIA - NVIDIA GeForce GTX 1660 Ti with Max-Q Design
NVAPI: Init OK!
NVAPI: Disabled OpenGL threaded optimization successfully
</code>
before hanging instantly.

I also tried to uninstall the drivers with DDU and reinstalling the latest NVIDIA drivers, but to no avail.

### Steps to reproduce

- Open Godot
- Open Chrome and/or Discord and keep it open.
- Enable "Update Continously" from Editor Settings.
- Doesn't require much action to do in the program. I think it simply happens depending on the runtime of either PC hardware or software.

### Minimal reproduction project (MRP)

N/A
Repository: pytorch
Issue: Too many spawn process
Labels: needs reproduction,module: multiprocessing,triaged

### 🐛 Describe the bug

Using a quite regular multiproces on multi gpu
```python
     mp.spawn(main_worker, nprocs=16, args=(cfg, args.amp))```

```python
def main_worker(gpu, cfg, enable_amp=True):
    # Initiate a training manager
    trainer = Trainer(rank=gpu, cfg=cfg, enable_amp=enable_amp)
    # Start Training
    trainer.sequential_training()
```

Inside each spawn  `Trainer`  there is a Dataloader with just 4 `num_workers`.

I see that are generated more then **550 processes** like
`/opt/conda/bin/python -c from multiprocessing.spawn import spawn_main; spawn_main(tracker_fd=55, pipe_handle=77) --multiprocessing-fork`

### Versions

pytorch nightly
pytorch 2.2.0

cc @VitalyFedyunin
Repository: godot
Issue: StyleBoxTexture don't support custom texture
Labels: topic:rendering,topic:gui,topic:2d

### Tested versions

4.3.dev3 and 4.2.stable

### System information

Godot v4.3.dev3 - Windows 10.0.22621 - Vulkan (Forward+) - dedicated NVIDIA GeForce GTX 1650 () - Intel(R) Core(TM) i5-10400F CPU @ 2.90GHz (12 Threads)

### Issue description

StyleBoxTexture can't use custom texture to render. When it uses custom texture, it renders as all white. Also haven ‘t output anything when use print(), seems don' t call the ProxyTexture2D.draw() method.

The custom ProxyTexture2D script:
![屏幕截图 2024-02-09 162602](https://github.com/godotengine/godot/assets/99076011/8139a9f4-19d7-4cff-83b1-d181415fceb5)

The scene:
![屏幕截图 2024-02-09 162538 拷贝](https://github.com/godotengine/godot/assets/99076011/a9c58b85-316f-4199-923e-ef80cdb5b383)
![屏幕截图 2024-02-09 162545](https://github.com/godotengine/godot/assets/99076011/0974bc72-175e-47d1-ba5b-d28ab55215cb)

Only render properly when use custom StyleBox like that:
![屏幕截图 2024-02-09 165123](https://github.com/godotengine/godot/assets/99076011/906ba967-66f3-49bc-b79c-46fa36a22abb)
![屏幕截图 2024-02-09 165229](https://github.com/godotengine/godot/assets/99076011/1e302ae6-5b25-4c51-9313-5381b38ea96f)

So, it should make StyleBoxTexture to call Texture.draw() properly?

### Steps to reproduce

1. Write a custom 2d texture script.
2. Make a scene and add a Panel control to the scene.
3. Set the Panel control's "theme overrides/panel" property to a StyleBoxTexture.
4. Set the StyleBoxTexture resource's texture to a custom 2d texture.
5. Run the project, then see the panel render as all white.

### Minimal reproduction project (MRP)

[StyleBoxTextureIssue.zip](https://github.com/godotengine/godot/files/14240495/StyleBoxTextureIssue.zip)

Open the scene and switch the Panel control's "theme overrides/panel" property to different style box resources in "res://resource/style_box/" folder. Run the project to see the result.
Repository: godot
Issue: `Array` functional API (`map()`, `filter()`, `all()`, etc.) should be `await`-aware
Labels: discussion,topic:core,documentation

### Tested versions

v4.2.2.rc1

### System information

Ubuntu 22.10, Vulkan, NVIDIA GeForce RTX™ 2060 / NVIDIA GeForce RTX™ 2060

### Issue description

Like the title says, functional API like `Array.map()` isn't aware of coroutines when used as array items. For example, I expect this code:

```gdscript
extends Node2D


func _ready() -> void:
	var fs: Array[Callable] = [func() -> int:
		await get_tree().create_timer(1.0).timeout
		return 0]
	print(fs.map(func(f: Callable) -> bool:
		print("BEFORE")
		var result: int = (await f.call()) + 1
		print("AFTER")
		return result))
```

To actually wait for the timer to `timeout` and print:

```sh
BEFORE
1
AFTER
```

Instead we immediately (well the first two lines) get:
```sh
BEFORE
[<GDScriptFunctionState#-9223372001589066428>]
AFTER
```

### Steps to reproduce

See above.

### Minimal reproduction project (MRP)

-
Repository: pytorch
Issue: CUDA Memory error while having a lot of free memory
Labels: module: cuda,module: memory usage,triaged,module: regression

### 🐛 Describe the bug

Hey,

I'm training a reinforcement learning agent on my device.
The models are rather small and the entire memory it takes (at peak usage) is around 870 MB.
My GPU is NVIDIA GeForce GTX 1650 TI with 4 GB memory.

I'm using CUDA V12.3
Torch V2.2.0 + cu118

My batch size is practically 1 since I'm iteratively sampling a transition, calculating the loss for it, and finally back propagating after accumulating enough transitions.
After processing the transition I'm moving it back to the CPU. 

I can't understand  why I'm getting the errors if the memory doesn't even get close to the capacity.

here are the errors that I'm getting and a snippet of the code that handles the loss calculations:
![error](https://github.com/pytorch/pytorch/assets/61480454/d31206de-c1a9-4c06-a0fe-aae18b89ac47)

![update_batch](https://github.com/pytorch/pytorch/assets/61480454/c4f56717-85af-4d2b-a1be-6d26badee025)

![update_loss](https://github.com/pytorch/pytorch/assets/61480454/61327b40-2cf0-4441-9ea8-e11cca1a68cb)

![backward](https://github.com/pytorch/pytorch/assets/61480454/015f597a-9c83-458f-ae82-78947ed467a6)

![parameters_update](https://github.com/pytorch/pytorch/assets/61480454/02c9a839-3bcd-443a-993f-abdd3a8e3660)

''' Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 has a total capacity of 4.00 GiB of which 2.35 GiB is free. Of the allocated memory 825.15 MiB is allocated by PyTorch, and 24.85 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management '''

### Versions

Collecting environment information...
PyTorch version: 2.2.0+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A
OS: Microsoft Windows 11 Home
GCC version: Could not collect
Clang version: 14.0.0
CMake version: Could not collect
Libc version: N/A
Python version: 3.10.11 (tags/v3.10.11:7d4cc5a, Apr  5 2023, 00:38:17) [MSC v.1929 64 bit (AMD64)] (64-bit runtime)
Python platform: Windows-10-10.0.22000-SP0
Is CUDA available: True
CUDA runtime version: 12.3.107
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1650 Ti
Nvidia driver version: 546.12
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Revision=
Versions of relevant libraries:
[pip3] numpy==1.24.1
[pip3] pytorch-lightning==2.1.3
[pip3] torch==2.2.0+cu118
[pip3] torchaudio==2.2.0+cu118
[pip3] torchmetrics==1.3.0.post0
[pip3] torchrl==0.2.1
[pip3] torchvision==0.17.0+cu118
[conda] Could not collect

cc @ptrblck
Repository: vscode
Issue: notebook.output.scrolling starts at the bottom
Labels: under-discussion,notebook-output

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Does this issue occur when all extensions are disabled?: Yes

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- VS Code Version: 1.86.1
- OS Version: macOS Sonoma

Steps to Reproduce:

1. Set 
    ```
    "notebook.output.textLineLimit": 10,
    "notebook.output.scrolling": true,
    ```
2. Create a new notebook
4. Run this in a cell
    ```
   import numpy as np
   np.linspace(0, 1, 1000)
   ```
5. Output will be scrolled to the bottom:
<img width="704" alt="image" src="https://github.com/microsoft/vscode/assets/2845433/2b557f72-128e-439f-b8d7-5f6577a48e31">

This is wrong, often it forces to scroll up in order to make sense of the output, most things are intended to be read from the top to the bottom and not the other way around.
Repository: stable-diffusion-webui
Issue: [Bug]: The performance of Inpaint is not ideal in version 1.7.0
Labels: bug-report

### Checklist

- [X] The issue exists after disabling all extensions
- [ ] 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
- [ ] The issue exists in the current version of the webui
- [x] The issue has not been reported before recently
- [ ] The issue has been reported before but has not been fixed yet

### What happened?

The performance of Inpaint is not ideal in version 1.7.0

### Steps to reproduce the problem

start webui.bat

### What should have happened?

I have been using the old version for a long time and just recently upgraded to the latest version. I don’t know if the usage of "Inpaint" has changed in the new version. When I use the previous method to repair the picture in the new version, blur will appear.

Please tell me how to use the new version "Inpaint" or how to downgrade to the old version?

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

Google Chrome

### Sysinfo

N/A

### Console logs

```Shell
N/A
```


### Additional information

_No response_
Repository: three.js
Issue: Scene: Lighting does not work if environment map has dimensions < 64 pixels
Labels: Enhancement

### Description

Lighting does not work at all when setting an environment map smaller than 64x64 to `Scene.environment`. Creating a small map on the fly is useful when generating basic, soft gradient lighting. I suspect this may be related to PMREMGenerator. The texture still works if it's set as a background.

| 64x64 | 63x63 |
|---|---|
| <img width="350" alt="image" src="https://github.com/mrdoob/three.js/assets/734200/696debb9-3290-4916-ae92-8560460c8648"> | <img width="350" alt="image" src="https://github.com/mrdoob/three.js/assets/734200/e07a3d8a-75a9-418d-bdec-b9a0656cd3e3"> |



### Reproduction steps

1. Create a DataTexture with dimensions of 63x63 with white data
2. Set mapping to "EquirectangularReflectionMapping"
3. Lighting does not work


### Code

```js
const dim = 64; // NOTE: Lighting does not work if this value is changed to < 64
const tex = new THREE.DataTexture( new Uint8Array( dim * dim * 4 ).fill( 255 ), dim, dim );
tex.mapping = THREE.EquirectangularReflectionMapping;
tex.needsUpdate = true;
scene.environment = tex;
```


### Live example

https://jsfiddle.net/y48Lqt1m/

### Screenshots

_No response_

### Version

r161

### Device

Desktop

### Browser

Chrome

### OS

MacOS
Repository: godot
Issue: unexpected behaviour when using tile_map.set_cells_terrain_connect
Labels: bug,topic:gdscript,topic:2d

### Tested versions

4.3.dev3

### System information

Godot v4.3.dev3 - Windows 10.0.19045 - Vulkan (Forward+) - dedicated NVIDIA GeForce RTX 3080 (NVIDIA; 31.0.15.3713) - AMD Ryzen 9 5900X 12-Core Processor (24 Threads)

### Issue description
I am working on a system for procedural level generation for a 2d top down game. I need to place terrain sets through code.
I have set up a wang terrain set using this bitmask in the "match corners" mode.

![grafik](https://github.com/godotengine/godot/assets/81755541/4731fc15-6c39-4935-9f23-a9344732dd41)

When I paint the terrain set on the tilemap, it works exactly as expected. 
![Godot_v4 3-dev3_win64_MR55hmaYT7](https://github.com/godotengine/godot/assets/81755541/42cf9043-5e6a-453c-a108-e5ffb479e223)

But when I try to achieve the same result from code, it does not work as I would expect, given the behaviour in the editor. 
This code does not result in any tiles being drawn on the tilemap although it represents what I am doing in the editor.
```
	var floor_cells := {}
	
	for x in 3:
		floor_cells[Vector2i(x, 0)] = null
	
	for y in 3:
		floor_cells[Vector2i(0, y)] = null
	
	tile_map.set_cells_terrain_connect(0, floor_cells.keys(), 0, 0)
```
This is strange enough. Appearently, you need to include the neighbor cells to achieve the same result:
```
	var floor_cells := {}
	
	for x in 3:
		floor_cells[Vector2i(x, 0)] = null
	
	for y in 3:
		floor_cells[Vector2i(0, y)] = null
	
	const MOORE_NEIGHBORHOOD_INCLUSIVE: Array[Vector2i] = [
		Vector2i(0, 0),
		Vector2i(0, -1),
		Vector2i(1, -1),
		Vector2i(1, 0),
		Vector2i(1, 1),
		Vector2i(0, 1),
		Vector2i(-1, 1),
		Vector2i(-1, 0),
		Vector2i(-1, -1),
	]
	
	var moore_floor_cells := {}

	for floor_cell in floor_cells:
		for offset in MOORE_NEIGHBORHOOD_INCLUSIVE:
			var neighbor_cell = floor_cell + offset
			moore_floor_cells[neighbor_cell] = null
	tile_map.set_cells_terrain_connect(0, moore_floor_cells.keys(), 0, 0)
```
This works fine for straight lines but it does not work for corners. For some reason, it adds another tile to the tilemap that should not be there. I have no clue where it comes from.

![Godot_v4 3-dev3_win64_pcpYT3e9c8](https://github.com/godotengine/godot/assets/81755541/815c9305-7e51-42f9-8fe4-91203cdfea33)

The code works just as expected for tile_map.set_cell, though.

![image](https://github.com/godotengine/godot/assets/81755541/fafcb237-54d1-4ada-aca7-46771450e80a)


### Steps to reproduce

1. Create a terrain set in "match corners" mode for a wang tile set using the bitmask from above.
2. Paint the tilemap using the terrain set (works fine)
3. Try to achieve the same result via code (doesn't work).

### Minimal reproduction project (MRP)

[wang_tile_set_bug.zip](https://github.com/godotengine/godot/files/14222621/wang_tile_set_bug.zip)
Repository: pytorch
Issue: Inconsistent behavior when indexing a Tensor with a list of lists
Labels: triaged,module: advanced indexing

### 🐛 Describe the bug

Hi PyTorch dev team, I am using a list of lists for indexing a single-dimensional Tensor. When the lengths of the list drops below 32, an IndexError is raised. The same behavior has been reported at https://discuss.pytorch.org/t/solved-bug-inconsistent-behavior-for-tensor-list-indexing/137225 in Nov '21, but it appears that the inconsistent behaviour still remains.

```python
import torch

# Create a single dimensional time-series tensor with N elements
N = 1000
t = torch.randn(N) 

# Set window size
W = 10

# Generate a list of lists, containing overlapping window indices
indices = [range(i, i + W) for i in range(0, N - W)]

# Extract windows from time-series tensor
# While the number of windows is >= 32, no erros occur,
# But when the number is < 32, an IndexError is raised.
for i in [len(indices), 100, 32, 31]:
    print('Num of windows:', i)
    windowed_data = t[indices[:i]]      
    print('Windowed data shape:', windowed_data.shape)
```

The code prints out:
```
Num of windows: 990
Windowed data shape: torch.Size([990, 10])
Num of windows: 100
Windowed data shape: torch.Size([100, 10])
Num of windows: 32
Windowed data shape: torch.Size([32, 10])
Num of windows: 31
```

And then the following IndexError is raised:
```
Traceback (most recent call last):
  File "/Users/pacfan/Desktop/Torch_Tensor_List_Indexing_Bug.py", line 18, in <module>
    windowed_data = t[indices[:i]]      
IndexError: too many indices for tensor of dimension 1
```

The problem can be bypassed by changing `indices` from a list of lists to a Tensor with `indices = torch.tensor(indices)`.

### Versions

PyTorch version: 2.3.0.dev20240208
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: macOS 13.1 (arm64)
GCC version: Could not collect
Clang version: 14.0.3 (clang-1403.0.22.14.1)
CMake version: Could not collect
Libc version: N/A

Python version: 3.9.18 (main, Sep 11 2023, 08:25:10)  [Clang 14.0.6 ] (64-bit runtime)
Python platform: macOS-13.1-arm64-arm-64bit
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

CPU:
Apple M1 Pro

Versions of relevant libraries:
[pip3] numpy==1.26.0
[pip3] torch==2.3.0.dev20240208
[pip3] torchaudio==2.2.0.dev20240208
[pip3] torchvision==0.15.2a0
[conda] numpy                     1.26.0           py39h3b2db8e_0    anaconda
[conda] numpy-base                1.26.0           py39ha9811e2_0    anaconda
[conda] pytorch                   2.3.0.dev20240208         py3.9_0    pytorch-nightly
[conda] torchaudio                2.2.0.dev20240208        py39_cpu    pytorch-nightly
[conda] torchvision               0.15.2          cpu_py39h31aa045_0    anaconda
Repository: react-native
Issue: [iOS][Accessibility] VoiceOver will read aloud the text from image and even texts that overlaid on top of the image
Labels: Platform: iOS,Component: Image,Needs: Triage :mag:

### Description

**Actual:** 

**VoiceOver** will read aloud the text from image and even texts that overlaid on top of the image

if wrap an image component with `accessible={true}` (_see video below_) & code here: https://github.com/retyui/react-native-voice-over-issue/blob/main/App.tsx

I tried to hide element from accessibility using, but it' doesn't help

```tsx
  accessible: false,
  accessibilityRole:'none',
  accessibilityElementsHidden:true, // ios only
```

**Expected:**  I'm expecting only a `accessibilityLabel` to be read aloud of `<Pressable accessible={true} />`


### Steps to reproduce

1. Create a new Project
2. add  `<Pressable accessible={true} />`
3. inside `Pressable` put an `Image` component with and use any image with a text as source
4. Put float text on the image
5. Enable VoiceOver with `Text Recognition` enabled 
  ![image](https://github.com/facebook/react-native/assets/4661784/438fc142-a410-4968-b495-d4cc7228e2aa)


### React Native Version

0.73.4

### Affected Platforms

Runtime - iOS

### Output of `npx react-native info`

```text
System:
  OS: macOS 14.2.1
  CPU: (10) arm64 Apple M1 Pro
  Memory: 236.81 MB / 32.00 GB
  Shell:
    version: 3.6.1
    path: /opt/homebrew/bin/fish
Binaries:
  Node:
    version: 20.11.0
    path: /usr/local/bin/node
  Yarn:
    version: 1.22.19
    path: /opt/homebrew/bin/yarn
  npm:
    version: 9.7.2
    path: /opt/homebrew/bin/npm
  Watchman:
    version: 2023.09.04.00
    path: /opt/homebrew/bin/watchman
Managers:
  CocoaPods:
    version: 1.13.0
    path: /opt/homebrew/bin/pod
SDKs:
  iOS SDK:
    Platforms:
      - DriverKit 23.2
      - iOS 17.2
      - macOS 14.2
      - tvOS 17.2
      - visionOS 1.0
      - watchOS 10.2
  Android SDK: Not Found
IDEs:
  Android Studio: Not Found
  Xcode:
    version: 15.2/15C500b
    path: /usr/bin/xcodebuild
Languages:
  Java:
    version: 17.0.8
    path: /usr/bin/javac
  Ruby:
    version: 2.6.10
    path: /usr/bin/ruby
npmPackages:
  "@react-native-community/cli": Not Found
  react: Not Found
  react-native: Not Found
  react-native-macos: Not Found
npmGlobalPackages:
  "*react-native*": Not Found
Android:
  hermesEnabled: Not found
  newArchEnabled: Not found
iOS:
  hermesEnabled: Not found
  newArchEnabled: Not found
```


### Stacktrace or Logs

```text
no logs...
```


### Reproducer

https://github.com/retyui/react-native-voice-over-issue

### Screenshots and Videos


https://github.com/facebook/react-native/assets/4661784/27e5e745-67b3-47ed-a8a0-0642e6170e9a
Repository: pytorch
Issue: Way of mocking collectives, especially data-dependent ones like all2all_single
Labels: oncall: distributed,triaged,oncall: pt2,module: dynamic shapes,module: minifier

### 🐛 Describe the bug

Something that perennially gives me fits is what to do about error programs that have distributed collectives in them. To test the collective directly, I have to actually run the script in a distributed environment, which is a huge pain (like, think about the minifier, how are you going to run that?!) So it would nice to be able to stub out these collectives with mocked versions, which don't actually require being run in a distributed context.

Actually, fake PG gets you most of the way for many collectives; here, we don't bother doing any communication and just return some uninitialized memory, IIRC. However, for collectives of data-dependent flavor like all2all_single, these are typically being used to communicate things like tensor splits, and so producing garbage data here isn't going to work. So you actually want some of way of attaching REAL int tensor data, so that you get something real for your subsequent split.

### Versions

main

cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu @penguinwu @fegin @XilunWu @wanchaol @fduwjj @wz337 @tianyu-l @wconstab @yf225 @msaroufim @bdhirsh @anijain2305 @zou3519