sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: scrcpy
Issue: Hotkey for toggling alway-on-top
Labels: feature request

It would be good if there was a hotkey to toggle always-on-top.
Repository: vscode
Issue: Process explorer difficult / impossible to use via screen reader
Labels: bug,accessibility,workbench-diagnostics

I believe the issue is that the list is constantly refreshing. This causes the screen reader to continually speak the current item. THis makes reading items impossible.

If you bring up context menu to explore options (was hoping for something to turn off or decrease refresh rate), you can't read the menu because screen reader keeps repeating current item.

- need setting to set refresh rate
- many columns to read so either HTML table or way to rearrange and eliminate columns
Repository: pytorch
Issue: TorchScript breaks ("internal assert failed") on `torch.set_grad_enabled`
Labels: oncall: jit

## 🐛 Bug

TorchScript interal assertions fail on `torch.set_grad_enabled`, and since `torch.enable_grad()` is unsuported, there is, as far as I can tell, no way to enable grad in TorchScript, even though `no_grad()` is supported.

## To Reproduce

```python
In [1]: import torch

In [2]: @torch.jit.script
   ...: def f(x):
   ...:     with torch.set_grad_enabled(True):
   ...:         return x
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
torch/jit/_script.py in script(obj, optimize, _frames_up, _rcb)
    987         if _rcb is None:
    988             _rcb = _jit_internal.createResolutionCallbackFromClosure(obj)
--> 989         fn = torch._C._jit_script_compile(
    990             qualified_name, ast, _rcb, get_default_args(obj)
    991         )

RuntimeError: r INTERNAL ASSERT FAILED at "/opt/conda/conda-bld/pytorch_1614378072553/work/aten/src/ATen/core/jit_type_base.h":172, please report a bug to PyTorch.
```
The same error occurs with `torch.set_grad_enabled(False)`.

## Expected behavior

<!-- A clear and concise description of what you expected to happen. -->

## Environment

 - PyTorch Version (e.g., 1.0): 1.8.0
 - OS (e.g., Linux): Linux
 - How you installed PyTorch (`conda`, `pip`, source): conda
 - Build command you used (if compiling from source): n/a
 - Python version: 3.8.5
 - CUDA/cuDNN version: n/a
 - GPU models and configuration:n/a
 - Any other relevant information:n/a

## Additional context

`torch.enable_grad()` is not a workaround (unsupported):
```python
In [4]: @torch.jit.script
   ...: def f(x):
   ...:     with torch.enable_grad():
   ...:         return x
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Unknown builtin op: aten::enable_grad.
Could not find any similar ops to aten::enable_grad. This op may not exist or may not be currently supported in TorchScript.
:
  File "<ipython-input-4-56023061a529>", line 3
@torch.jit.script
def f(x):
    with torch.enable_grad():
         ~~~~~~~~~~~~~~~~~ <--- HERE
        return x
```

While `torch.no_grad()` works fine:
```python
In [5]: @torch.jit.script
   ...: def f(x):
   ...:     with torch.no_grad():
   ...:         return x
```



cc @gmagogsfm
Repository: kubernetes
Issue: Add online snapshot e2e
Labels: sig/storage,kind/feature,help wanted,needs-triage

<!-- Feature requests are unlikely to make progress as an issue.

Instead, please suggest enhancements by engaging with SIGs on slack and mailing lists.

A proposal that works through the design along with the implications of the change can be opened as a KEP:
https://git.k8s.io/enhancements/keps#kubernetes-enhancement-proposals-keps
-->

#### What would you like to be added:
Ref: https://github.com/kubernetes/kubernetes/pull/101280/files#r616971565

Currently our snapshot tests only test offline snapshots, taken when after the volume is unmounted. We are missing coverage for online snapshots, taken when the volume is mounted.

#### Why is this needed:
I think online snapshots is the more common scenario
Repository: PowerToys
Issue: [Run][Program plugin] clean up log entries
Labels: Product-PowerToys Run,Priority-2,Cost-Small,Run-Plugin

The `Program` plugin is a spam machine for the log, logging as exceptions things that are expected errors.

See:
https://github.com/microsoft/PowerToys/blob/7043e9672f1732dfc87c9b47c524134549344656/src/modules/launcher/Plugins/Microsoft.Plugin.Program/Logger/ProgramLogger.cs#L30-L34
Repository: yt-dlp
Issue: [Feature Request]Ability to use a separate file which stores names of cookie files to use and either extractor/domain
Labels: enhancement,core:cookies,core:extractor

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- [x] I'm reporting a feature request
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## Description

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Basically I want to stop having to change the cookie file to use in the command line every time I want to use a different site so I'd ideally like to list all the cookie files I'd like to use in a single file. Either by extractor (like netrc) or domain.
Repository: pytorch
Issue: [package] Module name reported in error message does not always match what is needed to extern/mock it
Labels: triaged

## 🐛 Bug
The module name as printed in the packaging error messages is not always the name with which it can be successfully externed or mocked.

## To Reproduce
```
import torch
import io

model = torch.hub.load('nicolalandro/ntsnet-cub200', 'ntsnet', pretrained=True, **{'topN': 6, 'device':'cpu', 'num_classes': 200})
model.eval()

with torch.package.PackageExporter(io.BytesIO()) as exp:
    exp.extern([
        "sys",
        "io",
        "PIL.**",
        "_queue",
        "urllib3.**",
    ])
    exp.save_pickle("ntsnet", "model.pkl", model)
```
This code produces the following error:
```
ValueError: cannot save source for module "mklinit" because its source file "/home/meghanl/local/miniconda3/envs/tutorial/lib/python3.8/site-packages/mkl/_mklinit.cpython-38-x86_64-linux-gnu.so" could not be found. See the dependency graph for more info:
```

## Expected Outcome
`exp.extern("mklinit")` externs this module.

## Actual Outcome
`exp.extern("mklinit")` does not extern this module; the same error is produced. `exp.extern("mkl.**")` does extern this module.
Repository: TypeScript
Issue: Can't constrain a generic parameter to have string keys?
Labels: Bug

# Bug Report

Search Terms: `Type 'X' cannot be used to index type 'Y'`, constrain generic parameter to have string keys
- This is the behavior in every version I tried

### Code

[Playground link](https://www.typescriptlang.org/play?ts=4.2.3#code/LAKALgngDgpgBAUQB4EMC2UA2MA8BZAPjgF5Q44BvOAbQGk4BLAOzgGsYIB7AMzjwF0A-AC5KaGAGcJKAOYxReOvwC+1fnGVwAZGUo12ERiwlgATsxn9RFcVNny4AVyasmnAO5NV65QG5QASAA9EFw7pymrBJwALQxYfCuHnBgABYoYHAARNycnFlwKEwAJnCcaTCmcCbmTDLRKKbwAG4omAylBhKgAMacTCZlAEYAVqLI6Fi4FLmcojUWykTElH6gnKPUOXlZ6ivUNpLScqJZqQxZyvz+IBsjW0ONuyQ0h3Yn2UiX14Hczj1gBj9FKSMD4OAwJBgGAlaIUagGIzVMwWKxOFxuTxLAAUAEpKLo+gNMndxqgMNh8MtVjdyCFqqlOI5MKVwpFYvEYM0YYxeO54MV+gByTJJdx8LazXYAGgSDKZLLYmJS6VFHB4fEY0QWdV0d0lOz2NB+IHI+qyj1Mz32JuUgXpAAEwBIYpDYADXaZTBEOQkhdzqigOpq0I5BpDoSVKAiOEidZZRM4xV5QNCTDgqAAWYRMRxoIaVDQEPE3IA)

This type is meant to map another type's values to arrays of `{message: V}` but also allow `{message:unknown}` for other string keys.

```ts
type Example<M> =
  { [K in keyof M]?: {message: M[K]}[] } &
  { [key in string]: {message: unknown}[] };
```

For example, the intent is that `Example<{ foo: string }>` should become
```ts
{
  foo: {message: string}[];
  [key: string]: {message: unknown}[];
}
```

And this works: we know that "foo" and other strings are valid keys.
```ts
const obj: Example<{foo: string}> = {};
obj["foo"] = [{message: "hi"}];
obj["bar"] = [{message: "x"}];
```

But trying to use `Example` **with a generic parameter** gives errors. Even if we don't know M["foo"], we should know that `keyof M` is `string`.
```ts
// attempt to constrain M's keys to be strings
function test<M extends {[key in string]: unknown}>() {
  const obj: Example<M> = {};
  obj["foo"] = [];  // error: Type '"foo"' cannot be used to index type 'Example<M>'.
  obj["bar"] = [];  // error: Type '"foo"' cannot be used to index type 'Example<M>'.
}
// should fail, but doesn't -- we've tried to constrain M to have string keys
test<{ 4: string }>();
```

### 🙁 Actual behavior

`Example<M>` cannot be indexed using string when `M` is a generic parameter.

### 🙂 Expected behavior

It should always be possible to index with string, since the definition of Example includes `{ [key in string]: ... }`.
Repository: flutter
Issue: google_sign_in: expose `expires_in` and `session_state.extraQueryParams.authuser` in GoogleSignInTokenData / GoogleSignInAuthentication
Labels: c: new feature,p: google_sign_in,package,c: proposal,team-ecosystem,P3,triaged-ecosystem

This data is present in the replies from Google's auth API.

```json
{
 "token_type": "Bearer",
 "access_token": "redacted",
 "scope": "email profile https://www.googleapis.com/auth/youtube.readonly openid https://www.googleapis.com/auth/userinfo.email https://www.googleapis.com/auth/userinfo.profile",
 "login_hint": "redacted",
 "expires_in": 3599,
 "id_token": "redacted",
 "session_state": {
  "extraQueryParams": {
   "authuser": "0"
  }
 }
}
```

This will allow correct population of authHeaders

https://github.com/flutter/plugins/blob/9ac4fb87f0889d4244b8516d4ec4e4b5ae83ae77/packages/google_sign_in/google_sign_in/lib/google_sign_in.dart#L107-L113

And access token expiry:

https://github.com/flutter/packages/blob/ad3368b217ee314e93ec778294840583901b33c1/packages/extension_google_sign_in_as_googleapis_auth/lib/extension_google_sign_in_as_googleapis_auth.dart#L29-L34
Repository: godot
Issue: Custom resource doesn't save its data after being modified
Labels: bug,topic:editor

**Godot version:**
3.2.3

**Issue description:**
So I have this custom resource as a storage for an array of items, but every time I change into any scene the data stored in that custom resource somehow disappears.

**Steps to reproduce.**
 I have found a weird way to get around with it by having the custom resource being referenced to any singleton class.  This way, it still holds all values in the array after I added something into it whenever I change between scenes.

![Screenshot (28)](https://user-images.githubusercontent.com/63050183/115635475-fd1c0c00-a335-11eb-8c10-665c4d0862c1.png)
![Screenshot (29)](https://user-images.githubusercontent.com/63050183/115635478-fee5cf80-a335-11eb-8c6d-5148d75bbacd.png)
Repository: flutter
Issue: Analyze is failing, Warning: transitive closure contained non-allowlisted packages: [frontend_server_client]
Labels: team,tool,P2,team-tool,triaged-tool

dart --enable-asserts ./dev/bots/analyze.dart
```
▌17:09:37▐ Package Allowlist...
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Warning: transitive closure contained non-allowlisted packages:
[frontend_server_client]
See dev/bots/allowlist.dart for instructions on how to update the package allowlist.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
```

I see this on 07fc1a6a48 but I don't think this is related to a framework commit...
Repository: transformers
Issue: Megatron fused CUDA kernels to improve Hugging Face model classes' scalability
Labels: Performance,WIP

# 🚀 Feature request

Support for custom fused CUDA kernels with HF model classes.

## Motivation

It appears that Hugging Face model classes do not scale very well as-is unlike Megatron-LM, even when the latter is configured with a degree of model-parallelization = 1 for a "fair" performance comparison.

One of the presumed reasons for this is that Megatron-LM leverages custom fused CUDA kernels written by NVIDIA, specifically [these](https://github.com/NVIDIA/Megatron-LM/blob/aed2f75e209e525c842aec7c044af7acae2a4614/megatron/model/transformer.py#L26L27).

Could we get variants of existing HF classes (perhaps for `GPT2Model`, `GPT2LMHeadModel`, etc.) such that the variants leverage some/all of these fused CUDA kernels? All this while still ensuring that one can load the original pre-trained weights into these variant classes.

Any guidance/low-level thoughts towards making this happen would also be greatly useful!

@thomwolf @patrickvonplaten @LysandreJik @stas00
Repository: pytorch
Issue: consistency jit tests gives error for gradient operator
Labels: oncall: jit

In the following Pull Request -  #54617  we get the following error :

```
======================================================================
ERROR: test_variant_consistency_jit_gradient_cpu_float32 (__main__.TestCommonCPU)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/fsx/users/iramazanli/pytorch/torch/testing/_internal/common_device_type.py", line 297, in instantiated_test
    raise rte
  File "/fsx/users/iramazanli/pytorch/torch/testing/_internal/common_device_type.py", line 292, in instantiated_test
    result = test_fn(self, *args)
  File "/fsx/users/iramazanli/pytorch/torch/testing/_internal/common_device_type.py", line 266, in test_wrapper
    return test(*args, **kwargs)
  File "test/test_ops.py", line 379, in test_variant_consistency_jit
    check_against_reference(self,
  File "/fsx/users/iramazanli/pytorch/torch/testing/_internal/common_jit.py", line 84, in check_against_reference
    outputs_test = self.runAndSaveRNG(func, nograd_inputs, kwargs)
  File "/fsx/users/iramazanli/pytorch/torch/testing/_internal/common_jit.py", line 154, in runAndSaveRNG
    results = func(*inputs, **kwargs)
  File "/fsx/users/iramazanli/pytorch/torch/testing/_internal/jit_metaprogramming_utils.py", line 307, in script_fn
    fn, tensors = gen_script_fn_and_args(method_name, func_type, *args, **kwargs)
  File "/fsx/users/iramazanli/pytorch/torch/testing/_internal/jit_metaprogramming_utils.py", line 298, in gen_script_fn_and_args
    CU = torch.jit.CompilationUnit(script)
RuntimeError: 
undefined value tensor:
  File "<string>", line 3

def the_method(i0):
    return torch.gradient(i0, dim=(0, -1), spacing=[tensor([1., 2., 3.]), tensor([4., 5., 6.])])
                                                   ~~~~~~ <--- HERE


```

cc @gmagogsfm
Repository: vscode
Issue: Absolute Path for Full Path Snippet Variables 
Labels: feature-request,snippets

<!-- ⚠️⚠️ 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. -->

Snippets containing paths on Linux have unexpanded home directories. i.e. for `TM_DIRECTORY` `~/folder/this_stuff/that_stuff/`. 

I request that there exists `_ABSOLUTE` versions for all snippets. pertaining to full paths i.e. `TM_DIRECTORY_ABSOLUTE` `/home/my_user_name/this_stuff/that_stuff`

I am unaware of the behavior on other platforms.
Repository: pytorch
Issue: {h / v / d}split methods are missing
Labels: triaged,module: numpy

Currently hsplit, vsplit and dsplit operators are implemented as functions in #53536. Method implementations are not provided and it would be nice to implement them as well.

cc @mruberry @rgommers @heitorschueroff
Repository: youtube-dl
Issue: [DoodStream] dood.so
Labels: site-support-request

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- [ ] I'm reporting a new site support request
- [ ] I've verified that I'm running youtube-dl version **2021.04.17**
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- [ ] I've checked that none of provided URLs violate any copyrights
- [ ] I've searched the bugtracker for similar site support requests including closed ones


## Example URLs

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-
- Single video (embed): https://dood.so/e/qymkx5bxxmr1
- Single Video(protected link):https://dood.so/d/un3bdi7e7j3fxcseixuorjy4go6wkuv9

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WRITE DESCRIPTION HERE
Repository: neovim
Issue: Neovim Should Remember the Last Window Size and Position of a File
Labels: enhancement

The window geometry and position for Neovim should be remembered for a specific file. A new file  should use a default.

It's somewhat annoying to have to always resize the editor window whenever I open a file, so If I have resized a window for a file, the next time I open that file,  Neovim  should go back to its previous window size.

As I am on Ubuntu, this may be an issue related to ubuntu or gnome-terminal itself and not neovim. Please let me know.
Repository: flutter
Issue: Proposal : MaterialApp - Make MediaQuery be available while constructing app Theme
Labels: c: new feature,framework,f: material design,a: quality,c: proposal,team-design,triaged-design

MaterialApp theme builder have no possibility to use MediaQuery - so it could not be used in real-life apps without ugly workarounds.
App themes depends on current screen width / system theme type and etc.
I found workarounds that have own issues:
## 1.
```dart
MaterialApp(
        home: child,
        builder: (context, child) => AnimatedTheme(
          data: appTheme(context),
          child: child!,
```
but it duplicate `AnimatedTheme(data: ThemeData.light()` creation in the MaterialApp 




## 2.
```dart
MediaQuery(
  data: MediaQueryData.fromWindow(window),
  child: MaterialApp(
        home: child,
        theme: appTheme(context),
```
but it duplicate MediaQuery creation (or alternative widget as sample above has no MediaQuery changes notifications for the theme) in the MaterialApp
Is there any simple way to disable duplicate AnimatedTheme creation for 1.? Could it be MaterialApp parameter or etc?
Repository: material-ui
Issue: [Tooltip] displaying twice when used with SVG icon
Labels: accessibility,component: tooltip

In order to have both a styled tooltip and an accessibility friendly icon I am using the `Tooltip` component along with setting `titleAccess` on the `Icon` component, but now two tooltips appear when hovering over. I know that by removing `titleAccess` from the icon component it will prevent this but then you have a poor accessibility experience. 

```jsx
import React from "react";
import CheckIcon from "@material-ui/icons/CheckCircle";
import { Tooltip } from "@material-ui/core";

export default class Demo extends React.Component {
  render() {
    return (
      <Tooltip title="My tooltip">
        <CheckIcon titleAccess="My tooltip" />
      </Tooltip>
    );
  }
}
```

<!-- Checked checkbox should look like this: [x] -->

- [x] The issue is present in the latest release.
- [x] I have searched the [issues](https://github.com/mui-org/material-ui/issues) of this repository and believe that this is not a duplicate.

## Current Behavior 😯

Two tooltips appear when hovering over

## Expected Behavior 🤔

Only one should appear

## Steps to Reproduce 🕹

https://codesandbox.io/s/material-ui-tooltip-icon-forked-ox00e?file=/demo.js

Steps:

1. Hover over icon for a few seconds

## Context 🔦

Trying to have both a styled tooltip and an accessibility friendly icon so screen readers know what the icon represents.

## Your Environment 🌎

See reproduction
Repository: TypeScript
Issue: Option for auto-import to prefer direct exports over re-exports
Labels: Suggestion,Awaiting More Feedback,Domain: Auto-import

# Suggestion

## 🔍 Search Terms

vscode, autoimport, importModuleSpecifier, "re-export", barrel, tree-shaking

## ✅ Viability Checklist

My suggestion meets these guidelines:

* [X] This wouldn't be a breaking change in existing TypeScript/JavaScript code
* [X] This wouldn't change the runtime behavior of existing JavaScript code
* [X] This could be implemented without emitting different JS based on the types of the expressions
* [X] This isn't a runtime feature (e.g. library functionality, non-ECMAScript syntax with JavaScript output, new syntax sugar for JS, etc.)
* [X] This feature would agree with the rest of [TypeScript's Design Goals](https://github.com/Microsoft/TypeScript/wiki/TypeScript-Design-Goals).


## ⭐ Suggestion

Some libraries provide the ability to import modules using sub-path module specifiers (`import {SubModule} from "@scope/library/submodule"`, as well as re-exporting the module at top level (`import {SubModule} from "@scope/library"`).  In some circumstances, it is desirable to prefer the sub-path specifier over the top level ("barrel") re-export.  I don't see any way to do that using the current `importModuleSpecifier` options.

Ideally, the mode would resolve the preferred specifier, then walk up any `export from` statements and use the original source.  If that's not possible, perhaps an MRU option could be implemented, so that if I manually pick a specifier path for a given module once, that same path is used for future auto-imports.

## 💻 Use Cases

I normally stick to the default "shortest" preference, which prefers the top-level re-export (if present).  This makes the build tooling work harder to prove that a given module is tree-shakable, and in rare cases can lead to circular reference issues, as the load order of interdependent barrel exports becomes too convoluted to untangle automatically.  Importing only the sub-module being used can avoid these problems.