sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: deno
Issue: Support type definition packages
Labels: suggestion,config

Let's say I install a package from npm by adding this to my deno.json imports (Any package, just as example)
```
"@types/yargs": "npm:@types/yargs",
"yargs": "npm:yargs"
```

Now in my code where I use the import I always have to add the types comment to it in order to have correct IDE complition and properly understand what method and what arguments and what types it takes
```
// @deno-types="@types/yargs"
import yargs from 'yargs'
```

My proposal is, may be to support something like that in the imports statements:
```
imports: {
   "yargs": {
       "types":  "npm:@types/yargs",
       "package": "npm:yargs"
   }
}
```

At least this)
ideally Deno should resolve the type definitions as Node js does, an not have to do all these workasounds
But at least something rather than adding comments in the code
Repository: pytorch
Issue: ONNX Export Fails with Dynamic Slicing on Data-Dependent Value
Labels: module: onnx,triaged

### 🐛 Describe the bug

Hello,

I am encountering an error when trying to export a PyTorch model to ONNX using torch.onnx.dynamo_export. The error suggests that dynamic slicing on a data-dependent value is not supported.

Environment:

Python version: 3.8
PyTorch version: 2.4.1

Error Trace:
<img width="766" alt="屏幕截图 2024-09-14 192450" src="https://github.com/user-attachments/assets/1ba07e41-3845-468f-b9c1-5fb8b2285de2">

The issue seems to arise when slicing with data-dependent values, specifically in the following line:
<img width="342" alt="屏幕截图 2024-09-14 192649" src="https://github.com/user-attachments/assets/834cf900-9771-456b-bf0e-204dc773cad6">


Any guidance on how to handle dynamic slicing or suggestions to resolve this would be greatly appreciated.

Let me know if you need any modifications or additional details!


### Versions

torch==2.4.1
python==3.8
Repository: PowerToys
Issue: I think Find My Mouse doesn't need on the pen/digitizer 
Labels: Needs-Triage

### Description of the new feature / enhancement

Find My Mouse also triggered by shaking the pen. I think it's not necessary behavior becuase the pen/digitizer points exactly to the screen we want to touch/hover/and any interact

### Scenario when this would be used?

using the pen or digitizer
especially drawing and painting. was annoying every time I use brush and did should exclude several apps every I meet it
I can't remember it triggered even I I'm toucing/clicking

### Supporting information

_No response_
Repository: rust
Issue: Tracking Issue for strict_provenance_lints
Labels: T-lang,C-tracking-issue,A-strict-provenance

This tracks the two lints associated with the [strict provenance](https://github.com/rust-lang/rust/issues/95228) feature:

- fuzzy_provenance_casts: detects an `as` cast from an integer to a pointer. It is better to use `with_exposed_provenance` instead to make explicit what happens.
- lossy_provenance_casts: detects an `as` cast from a pointer to an integer. It is better to use `expose_provenance` instead to make explicit what happens.

I am not sure if having two lints here is really justified, IMO they could be merged into one -- not sure what that one should be called, though. Other than that, this seems like a useful lint to ensure the codebase follows strict provenance (or opts-out explicitly, via the methods mentioned above).

I am also not sure if this shouldn't maybe be a clippy lint instead of a rustc lint?

Cc @rust-lang/opsem
Repository: godot
Issue: POT translation genaration ignores `TranslationServer.translate()`
Labels: enhancement,discussion,topic:gdscript,topic:gui

### Tested versions

Any 4.x

### System information

Windows 11

### Issue description

POT generator ignores strings that are translated via `TranslationServer.translate()` or `TranslationServer.translate_plural()`.

### Steps to reproduce

* Add following script to be translated:
```
extends Node
func _ready():
	print(tr("Hello"))
static func test():
	print(TranslationServer.translate("World"))
```
* Run the generator
* Inspect resulting file. There's no data point for "World".

### Minimal reproduction project (MRP)

[POT-MRP.zip](https://github.com/user-attachments/files/17002645/POT-MRP.zip)
Repository: opencv
Issue: change condition of CV_DbgAssert from _DEBUG to NDEBUG
Labels: feature,category: core,category: build/install

--- to make it's precondition same as `CV_DbgCheckXX`. Or further, replace all `#if defined _DEBUG` with `#if !defined NDEBUG`.

The C++ standard only mentions `NDEBUG` as the condition of `assert` macro, no `_DEBUG`.

https://github.com/opencv/opencv/blob/e1fec1562714eb41ae48f97310b254618a26d248/modules/core/include/opencv2/core/base.hpp#L389-L394

https://github.com/opencv/opencv/blob/e1fec1562714eb41ae48f97310b254618a26d248/modules/core/include/opencv2/core/check.hpp#L153-L169
Repository: ant-design
Issue: Collapse design token is bugged with ConfigProvider
Labels: 🐛 Bug,Inactive

### Reproduction link

[![Edit on StackBlitz](https://developer.stackblitz.com/img/open_in_stackblitz.svg)](https://stackblitz.com/edit/antd-reproduce-5x-sy1mss?file=demo.tsx)

### Steps to reproduce

1- Add ConfigProvider with custom theme for Collapse component.
2- Add Collapse component

### What is expected?

Header background should be #CCD2E3 while content background should be #fff

### What is actually happening?

The entire collapse component background is #CCD2E3

| Environment | Info |
| --- | --- |
| antd | 5.20.6 |
| React | 18.0.0 |
| System | Windows 10 |
| Browser | Chrome version 126.0.6478.127 |

<!-- generated by ant-design-issue-helper. DO NOT REMOVE -->
Repository: storybook
Issue: [Bug]: Rendering of MDX documentation fails when the imported stories file changes
Labels: bug,needs triage

### Describe the bug

When adding a new story to a stories file that is imported in a .mdx file, the following error is thrown:

```
Invalid value passed to the 'of' prop. The value was resolved to a 'component or unknown' type but the only types for this block are: story.
```

It happens when the imported story files is modified in other ways as well, but this is the most simple case to replicate this bug.

### Reproduction link

https://stackblitz.com/edit/github-unrkza-3cuztn?file=src%2Fstories%2FButton.stories.ts

### Reproduction steps

1. Go to the link above
2. Open "Buttons documentation" doc in the preview pane
3. Open `src/stories/Button.stories.tsx` file
4. Duplicate e.g. the `Secondary` story and rename it to e.g. `Tertiary`

Expected behavior:
The doc is still rendered correctly.

Actual behavior:
The error mentioned above it thrown.

### System

```bash
I'm not sure how to run `npx storybook@latest info` on StackBlitz, as the terminal does not seem to work for me. I am using the React 18 Webpack template, which uses Storybook 8.4.0-alpha.0.
```


### Additional context

It makes writing documentation using MDX files a bit frustrating ;)
Repository: godot
Issue: Inconsistent setter invocation in inherited scene without changed script and with overridden script
Labels: topic:gdscript,confirmed

### Tested versions

v4.3.stable.official [77dcf97d8]

### System information

Macos Sonoma 14.6.1 (23G93)

### Issue description

Instantiating the inherited scene (Test3), which inherits from the base scene (Test1), triggers the setter for an exported variable, even though it is not triggered in the inherited scene that doesn’t override the script (Test2). In the base scene (Test1), there is an exported variable with a setter that accesses a property of an object (e.g., val.name). When the script is overridden in the inherited scene (Test3), the setter is triggered, but in the inherited scene without the script override (Test2), the setter is not triggered. This leads to inconsistent behavior.

Expected result: The behavior should be the same in both inherited scenes (Test2 and Test3).

Actual result: In the inherited scene (Test3), the setter is triggered, whereas in the inherited scene without the script override (Test2), the setter is not triggered. This leads to inconsistent behavior in how setters are invoked across inherited scenes.

### Steps to reproduce


Create a base scene (Test1) with a class extending Node2D and a script:
```
extends Node2D
class_name Test1

@export var my_export_var: Resource set = _my_setter

func _my_setter(val):
    my_export_var = val
    print(val.name)
```
Create an inherited scene (Test2) that inherits from the base scene (Test1) without changing the script.
Create another inherited scene (Test3), also inheriting from the base scene (Test1), but with an overridden script:

`extends Test1`

Instantiate the inherited scene (Test3).

### Minimal reproduction project (MRP)

[godotbuginstantiateexportsetter.zip](https://github.com/user-attachments/files/17002963/godotbuginstantiateexportsetter.zip)
Repository: go
Issue: x/telemetry/internal/upload: TestRun_Basic failures
Labels: NeedsInvestigation,telemetry

```
#!watchflakes
default <- pkg == "golang.org/x/telemetry/internal/upload" && test == "TestRun_Basic"
```

Issue created automatically to collect these failures.

Example ([log](https://ci.chromium.org/b/8736804516105437489)):

    === RUN   TestRun_Basic
        run_test.go:68: 18:38:27.762138 findwork.go:35: Finding work: mode on asof 2023-09-15 00:00:00 +0000 UTC
        run_test.go:68: 18:38:27.762675 findwork.go:50: Collecting count file upload.test@devel-devel-netbsd-arm64-2024-09-06.v1.count
        run_test.go:68: 18:38:27.763190 reports.go:34: Last week: , today: 2024-09-14
        run_test.go:68: 18:38:27.764060 reports.go:268: Created 2024-09-08.json, deleting 1 count files
        run_test.go:68: 18:38:27.764333 reports.go:68: Ready to upload: 2024-09-08.json
        run_test.go:68: 18:38:27.764372 run.go:172: Uploading 1 reports
        run_test.go:68: 18:38:27.766345 upload.go:115: Uploaded 2024-09-08.json to "http://127.0.0.1:55828/2024-09-08"
        run_test.go:153: Mismatching uploaded report:
            got:
    ...
            			"GOARCH": "arm64",
            			"Counters": {
            				"knownCounter": 1
            			},
            			"Stacks": {}
            		}
            	],
            	"Config": "v1.2.3"
            }
    --- FAIL: TestRun_Basic (0.07s)


— [watchflakes](https://go.dev/wiki/Watchflakes)
Repository: rust
Issue: rustdoc: tracking issue for improved path handling
Labels: T-rustdoc,C-tracking-issue

looking through old rustdoc issue, there's a common theme of wanting to display or act on an item's canonical path instead of just its name.

unfortunately, this information is not easily available to the librustdoc render logic.

* [ ] https://github.com/rust-lang/rust/issues/105307
* [ ] https://github.com/rust-lang/rust/issues/45260
* [ ] https://github.com/rust-lang/rust/issues/130189
* [ ] https://github.com/rust-lang/rust/issues/84367 (has more to do with canon names than paths, but still related)
Repository: pytorch
Issue: Have a way to mark that particular buffers can be reused for Inductor
Labels: triaged,oncall: pt2,module: inductor

### 🚀 The feature, motivation and pitch

Let's say we want to benchmark a function where the inputs are reused (say a pointwise op). This is quite annoying to do today in Inductor

For example, for this function, there's no easy way to make this function inplace.
```
import torch
from torch import Tensor
from torch import nn
from typing import Optional, List, Tuple, Union
torch.set_default_device('cuda')
from triton.testing import do_bench

class RMSNorm(nn.Module):
    """Root mean square normalization.

    Computes x -> w * x / sqrt(E[x^2] + eps) where w is the learned weight.
    Refer to https://arxiv.org/abs/1910.07467
    """

    def __init__(
        self,
        hidden_size: int,
        eps: float = 1e-6,
    ) -> None:
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps
        self.cuda = False

    def forward(
        self,
        x: torch.Tensor,
        residual: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
        """PyTorch-native implementation equivalent to forward()."""
        orig_dtype = x.dtype
        x = x.to(torch.float32)
        if residual is not None:
            x = x + residual.to(torch.float32)
            residual = x.to(orig_dtype)

        variance = x.pow(2).mean(dim=-1, keepdim=True)
        x = x * torch.rsqrt(variance + self.variance_epsilon)
        x = x.to(orig_dtype) * self.weight
        if residual is None:
            return x
        else:
            return x, residual

def bench_mod(mod, inp, residual):
    torch.cuda.reset_peak_memory_stats()
    with torch.no_grad():
        ms = do_bench(lambda: mod(inp, residual))
    print("ms: ", ms)
    print("Bandwidth: ", (1e3/ms) * inp.numel() * inp.dtype.itemsize * 3 / 1e9, " GB/s")
    print("peak mem: ", torch.cuda.max_memory_allocated()/1e9, "GB")
mod = RMSNorm(8192).to(dtype=torch.bfloat16)
inp = torch.randn(8192, 8192, dtype=torch.bfloat16)
residual = torch.randn(8192, 8192, dtype=torch.bfloat16)
bench_mod(torch.compile(mod), inp, residual)
```

### Alternatives

_No response_

### Additional context

_No response_

cc @ezyang @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire
Repository: rust
Issue: rustdoc::clean is missing glob re-exports
Labels: T-rustdoc,C-discussion,A-local-reexports

### Location

https://doc.rust-lang.org/nightly/nightly-rustc/rustdoc/clean/index.html

### Summary

items like `clean::Item` should show up here, but they do not, they only show up at their original definition site, `clean::types::Item`.

possibly related: https://github.com/rust-lang/rust/issues/53724
Repository: pytorch
Issue: Quantized model is way slower than regular model.
Labels: oncall: quantization

## Issue description
Quantized model is way slower than regular model.

Log:
> model: LSTM(
  (lstm): LSTM(1, 50, batch_first=True)
  (fc): Linear(in_features=50, out_features=1, bias=True)
)
quantized_model: LSTM(
  (lstm): DynamicQuantizedLSTM(1, 50, batch_first=True)
  (fc): DynamicQuantizedLinear(in_features=50, out_features=1, dtype=torch.qint8, qscheme=torch.per_tensor_affine)
)
Size (MB): 0.04497
Size (MB): 0.015388 (quantized)
loss: 0.0001180
elapsed time (seconds): 10.2846
loss: 0.0001596
elapsed time (seconds): 33.8518 (quantized)
`

## Code example
[link](https://gist.github.com/Eugene29/71428f93fce97ad9af00f8c59e8d2eb7)

## System Info
Pytorch: 2.3.1


cc @jerryzh168 @jianyuh @raghuramank100 @jamesr66a @vkuzo @jgong5 @Xia-Weiwen @leslie-fang-intel @msaroufim
Repository: transformers
Issue: add `private` parameter to trainer.push_to_hub
Labels: Feature request

### Feature request

Adding another parameter to the `trainer.push_to_hub` called `private`

### Motivation

a user at https://discuss.huggingface.co/t/how-to-push-trained-models-and-save-it-privately-on-hub/25538/10 has requested this, and although setting `hub_private_repo` training argument to True is a good alternative, but I agree with them on the basis that we need a unified `push_to_hub` method.

### Your contribution

I can submit a PR
Repository: pytorch
Issue: `int`, `float` and `complex` type with `return_indices` of `nn.MaxPool1d()` also work
Labels: module: nn,triaged

### 🐛 Describe the bug

The doc of [nn.MaxPool1d()](https://pytorch.org/docs/stable/generated/torch.nn.MaxPool1d.html) says `return_indices` is `bool` as shown below:

> - return_indices ([bool](https://docs.python.org/3/library/functions.html#bool)) – If True, will return the argmax along with the max values. Useful for [torch.nn.MaxUnpool1d](https://pytorch.org/docs/stable/generated/torch.nn.MaxUnpool1d.html#torch.nn.MaxUnpool1d) later

But `int`, `float` and `complex` type with `return_indices` also work as shown below. *It also happens with [nn.MaxPool2d()](https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html) and [nn.MaxPool2d()](https://pytorch.org/docs/stable/generated/torch.nn.MaxPool3d.html):

```python
import torch
from torch import nn

my_tensor = torch.tensor([[8., -3., 0., 1., 5., -2.]])
                                                     # ↓
maxpool1d = nn.MaxPool1d(kernel_size=1, return_indices=5)
maxpool1d(input=my_tensor)
# (tensor([[8.,-3., 0., 1., 5., -2.]]), tensor([[0, 1, 2, 3, 4, 5]]))
                                                     # ↓↓
maxpool1d = nn.MaxPool1d(kernel_size=1, return_indices=5.)
maxpool1d(input=my_tensor)
# (tensor([[8., -3., 0., 1., 5., -2.]]), tensor([[0, 1, 2, 3, 4, 5]]))
                                                     # ↓↓↓↓↓↓
maxpool1d = nn.MaxPool1d(kernel_size=1, return_indices=5.+0.j)
maxpool1d(input=my_tensor)
# (tensor([[8., -3., 0., 1., 5., -2.]]), tensor([[0, 1, 2, 3, 4, 5]]))
```

In addition, the doc of [nn.MaxPool1d()](https://pytorch.org/docs/stable/generated/torch.nn.MaxPool1d.html) says `ceil_mode` is `bool` as shown below:

> - ceil_mode ([bool](https://docs.python.org/3/library/functions.html#bool)) – If True, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.

Then, `int`, `float` and `complex` type with `return_indices` get the errors properly as shown below:

```python
import torch
from torch import nn

my_tensor = torch.tensor([[8., -3., 0., 1., 5., -2.]])
                                                # ↓
maxpool1d = nn.MaxPool1d(kernel_size=1, ceil_mode=5)
maxpool1d(input=my_tensor) # Error
                                                # ↓↓
maxpool1d = nn.MaxPool1d(kernel_size=1, ceil_mode=5.)
maxpool1d(input=my_tensor) # Error
                                                # ↓↓↓↓↓↓
maxpool1d = nn.MaxPool1d(kernel_size=1, ceil_mode=5.+0.j)
maxpool1d(input=my_tensor) # Error
```

> TypeError: max_pool1d(): argument 'ceil_mode' (position 6) must be bool, not int

> TypeError: max_pool1d(): argument 'ceil_mode' (position 6) must be bool, not float

> TypeError: max_pool1d(): argument 'ceil_mode' (position 6) must be bool, not complex

### Versions

```python
import torch

torch.__version__ # '2.3.0'
```

cc @albanD @mruberry @jbschlosser @walterddr @mikaylagawarecki
Repository: ollama
Issue: Slow setup download from Pakistan
Labels: ollama.com,networking

### What is the issue?

The ollama exe and curl both are downloading super slow. My internet is find becuase I download 2gb file in parallel and it downloaded very fast. 

### OS

Windows

### GPU

Nvidia

### CPU

Intel

### Ollama version

_No response_
Repository: PowerToys
Issue: [Enhancement Request] Keyboard Manager: Disable Individual Remaps + Add "Target app" for "Remap a Key"
Labels: Needs-Triage

### Description of the new feature / enhancement

- Add On/Off toggle to each remap under "Remap a key" and "Remap a shortcut"

- Add the same "Target app" setting as "Remap a shortcut" to "Remap a key"

### Scenario when this would be used?

- Add On/Off toggle to each remap under "Remap a key" and "Remap a shortcut"
When wanting to disable remaps without having to completely disable Keyboard Manager or deleting remaps.

- Add the same "Target app" setting as "Remap a shortcut" to "Remap a key"
When wanting to set key remaps to only work on specific apps, instead of the whole system.

### Supporting information

Mockup:
![image](https://github.com/user-attachments/assets/3d1eb298-caad-4b68-b95f-75b0cbae552f)
Repository: godot
Issue: [4.3 stable] Animation player editor: Keys get inserted at wrong timeline positions
Labels: bug,discussion,topic:editor,confirmed,usability,topic:animation

### Tested versions

Reproducable in 4.3 stable.
Redproducable in 4.4-dev2

### System information

Godot v4.3.stable - Windows 10.0.19045 - GLES3 (Compatibility) - NVIDIA GeForce RTX 3070 (NVIDIA; 31.0.15.3623) - Intel(R) Core(TM) i5-4670K CPU @ 3.40GHz (4 Threads)

### Issue description

I have an animation with keys every 0.15 seconds. I've noticed that when I click in the animation editor to insert new keys, they don't get added at the right position. Instead of 0.15, 0.3, 0.45 etc. they are always bit off, 0.1429, 0.2857, 0.4286 etc.

I've tried some other snap values. 0.1 works, but for instance 0.6 does not. I click at 0.6 in the timeline and right click "Insert key here", and it is inserted at 0.5.


### Steps to reproduce

Create a new project. Add an AnimationPlayer, create an animation track. Set snap length to value 0.15 and click in the editor to add keys. Notice how they are not at the right positions.

### Minimal reproduction project (MRP)

Not necessary.
Repository: pytorch
Issue: Flex Attention: Calculates Gradients Even if Input Has requires_grad=False
Labels: triaged,oncall: pt2,module: higher order operators,module: pt2-dispatcher,module: flex attention

### 🐛 Describe the bug

This is a feature request for flex attention to not calculate the gradient of a input if it has `requires_grad=False` I've looking into the kernel and seems like flex attention might actually might make this quite simple compared to other implementations like flash attention, it just might require an additional kernel option.

Let me know you'd like help with this and I can take a stab at it.

It seems like it might be easier to start by not calculating the query gradient and then later add support for calculating key and value gradient separately.

### Versions

2.5.0.dev20240912+cu124

cc @ezyang @chauhang @penguinwu @zou3519 @ydwu4 @bdhirsh @Chillee @drisspg @yanboliang @BoyuanFeng
Repository: rust
Issue: Tracking Issue for `str::as_str()`
Labels: T-libs-api,C-tracking-issue

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Feature gate: `#![feature(str_as_str)]`

This is a tracking issue for the `as_str()` method on the `str` type itself, so that `Box<str>`, `Rc<str>`, `Arc<str>` & others can have the same things as `String` has.

<!--
Include a short description of the feature.
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### Public API


```rust
impl str {
    pub const fn as_str(&self) -> &Self;
}
```

### Steps / History

- [ ] Implementation: #129550
- [ ] Final comment period (FCP)[^1]
- [ ] Stabilization PR

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### Unresolved Questions

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- None yet.

[^1]: https://std-dev-guide.rust-lang.org/feature-lifecycle/stabilization.html