sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: PowerToys
Issue: Pin-to-start / taskbar support for PowerToys modules
Labels: Idea-Enhancement,Area-Runner

Although we have shortcuts, I'm more of a point-and-click person :).

It would be nice if we could pin apps like Run and Color Picker to the start screen or task bar to access it quickly with the mouse.

---

If you'd like to see this feature implemented, add a 👍 reaction to this post.
Repository: flutter
Issue: MethodChannel invokeMethod does not work in Detached state
Labels: platform-android,framework,engine,has reproducible steps,P2,found in release: 3.7,found in release: 3.8,a: plugins,team-android,triaged-android

Steps to Reproduce

1. Create an app which uses a Dart Package using MethodChannel to call kotlin codes.
2. Add a Log.i statement in onMehodCall to verify that the kotlin codes are executed.
3 Use didChangeAppLifecycleState to monitor state changes.
4. Call the channleMethod in the following states: inactive, paused and detached.
5. Quit the app in Recent Apps.
6. You will observed kotlin codes (onMethodCall) are executed in inactive state but not in paused and detached states.

I need to be able to call channelMethods in detached state to unbind foreground service to prevent memory leak. I am using flutter_background_location package which has a foreground service. How would you unbind the service at detached state when channelMethods do not work?
Repository: pytorch
Issue: Error in the converting to pytorch mobile..
Labels: oncall: mobile

My torch and cuda is newest version..and i'm converting to pytorch mobile..
where net() is torch(Neural network)
```
# forward pass
    with torch.no_grad():
        y, feature = net(x)
        if tempval==0:
          detector2 = detector
          detector2.eval()
          traced_script_module = torch.jit.trace(detector2, x)
          traced_script_module.save("model.pt")
          np.save("x2.npy", x.cpu(), allow_pickle=False)
          tempval=1
        else:
          pass
```


but I got a error....
what is that....? Maybe I'll share full colab later... thank you

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-83-d527bb344e90> in <module>()
----> 1 result = readtext("drive/My Drive/colab/easy_ocr_test/Capture1.PNG")

5 frames
/usr/local/lib/python3.6/dist-packages/torch/jit/__init__.py in trace_module(mod, inputs, optimize, check_trace, check_inputs, check_tolerance, strict, _force_outplace, _module_class, _compilation_unit)
   1107             func = mod if method_name == "forward" else getattr(mod, method_name)
   1108             example_inputs = make_tuple(example_inputs)
-> 1109             module._c._create_method_from_trace(method_name, func, example_inputs, var_lookup_fn, strict, _force_outplace)
   1110             check_trace_method = module._c._get_method(method_name)
   1111 

RuntimeError: 0 INTERNAL ASSERT FAILED at "/pytorch/torch/csrc/jit/ir/alias_analysis.cpp":465, please report a bug to PyTorch. We don't have an op for aten::to but it isn't a special case.  Argument types: Tensor, None, int, Device, bool, bool, bool, int, 


cc @jerryzh168 @jianyuh @dzhulgakov @raghuramank100 @jamesr66a @vkuzo
Repository: create-react-app
Issue: Convert react-scripts to TypeScript
Labels: issue: proposal,needs triage

### Is your proposal related to a problem?

<!--
  Provide a clear and concise description of what the problem is.
  For example, "I'm always frustrated when..."
-->

Recently, I started a project with `create-react-app  --template typescript` and ejected later on to customize the build scripts.
Specifically, I refer to the scripts `build.js`, `start.js` and `test.js`, but also utility scripts like `createJestConfig.js`.
Unfortunately, it is rather painful to modify those scripts because the rest of the project is in TypeScript.
In particular, all my linting and IDE configuration is unusable for those scripts.

### Describe the solution you'd like

Convert the entire `react-scripts` package from JS to TypeScript.
Try to be consistent with the TypeScript-template from `create-react-app --template typescript`.

### Describe alternatives you've considered

<!--
  Let us know about other solutions you've tried or researched.
-->

A complete rewrite of `react-scripts`.
Repository: go
Issue: x/build: consider pushing open backport issue milestones automatically at start of each month
Labels: Builders,NeedsInvestigation,FeatureRequest

Right now, the milestones of open backport issues that did not get included in a given minor release are automatically bumped to the next minor release milestone by `releasebot` at the end of a successful release stage:

https://github.com/golang/build/blob/301a72cb1ab6c95414686a16f34ebe2bef25b689/cmd/releasebot/main.go#L429

Since we generally don't try to include more fixes in a minor release after the start of a month (aside from ones that have release-blocker label, which must be considered), it should be more accurate and clear to push the milestone to the next one at the start of the month.

However, when considering this, we need to be careful so that we don't accidentally land another fix after a month starts and forget to move the milestone of corresponding issue back to the correct previous one.

This issue is likely best to address as part of overall release automation, tracked in #40279 and #29205. /cc @toothrot

Thanks to @mundaym for suggesting this in https://groups.google.com/d/msg/golang-dev/6b-rdo-i8sA/auCA48IhAgAJ.

/cc @toothrot @cagedmantis @andybons
Repository: flutter
Issue: Running with a local engine affects generated files of unrelated platforms
Labels: c: new feature,tool,a: quality,P3,team-tool,triaged-tool

For example, in a Flutter project with iOS, android, and macOS, after doing a `flutter run --local-engine=android_debug_unopt`, I can see the following in the generated xcconfig files:

```
FLUTTER_FRAMEWORK_DIR=/Users/dnfield/src/flutter/engine/src/out/android_profile_unopt_arm64
```

I would expect this to never happen. It seems like the project file should only get updated for the actual target being run.

Flutter revision 6c1a29e54c
Repository: go
Issue: x/pkgsite: Stick to the preferred package version
Labels: NeedsInvestigation,pkgsite

When browsing packages at a particular version, for example master, the package site doesn't stick to the version.

For example, on https://pkg.go.dev/go.opentelemetry.io/otel@master/api/trace?tab=doc the links are not generated for the master branch. When I click on "subdirectories", it takes me to https://pkg.go.dev/go.opentelemetry.io/otel@v0.11.1-0.20200909181337-06689a01bd02/api/trace?tab=subdirectories.

This makes package site not a viable option during development. Please stick to the preferred version if possible to improve this issue.
Repository: vscode
Issue: No extension results returned for simple misspelling 
Labels: upstream,marketplace,upstream-issue-linked

Issue Type: <b>Bug</b>

**Repro**
1. In the extensions view, search `pyhton` 

**Bug**
No results returned

Yes I spelled it wrong but it would be nice if the marketplace understood simple misspellings like letter transpositions

VS Code version: Code - Insiders 1.49.0-insider (e790b931385d72cf5669fcefc51cdf65990efa5d, 2020-09-09T08:51:09.367Z)
OS version: Darwin x64 19.6.0

<details>
<summary>System Info</summary>

|Item|Value|
|---|---|
|CPUs|Intel(R) Core(TM) i9-9980HK CPU @ 2.40GHz (16 x 2400)|
|GPU Status|2d_canvas: enabled<br>flash_3d: enabled<br>flash_stage3d: enabled<br>flash_stage3d_baseline: enabled<br>gpu_compositing: enabled<br>metal: disabled_off<br>multiple_raster_threads: enabled_on<br>oop_rasterization: enabled<br>opengl: enabled_on<br>protected_video_decode: unavailable_off<br>rasterization: enabled<br>skia_renderer: disabled_off_ok<br>video_decode: enabled<br>webgl: enabled<br>webgl2: enabled|
|Load (avg)|2, 3, 3|
|Memory (System)|32.00GB (0.57GB free)|
|Process Argv|-psn_0_24602485 --crash-reporter-id cc7f0c9b-aa2b-4a45-8baa-81ffda32679e|
|Screen Reader|no|
|VM|13%|
</details>
<!-- generated by issue reporter -->
Repository: go
Issue: net: TestSendfilePipe failures with "Read did not time out"
Labels: NeedsInvestigation

```
--- FAIL: TestSendfilePipe (0.05s)
    sendfile_test.go:310: Read did not time out
FAIL
FAIL	net	9.705s
```

[2020-09-10T02:22:19-dfdc388/linux-arm64-aws](https://build.golang.org/log/4fac7f8abec1e9a7b4b0975a90df3317a5513cd2)
[2019-10-25T18:44:06-096126d/darwin-arm64-corellium](https://build.golang.org/log/340866477e677b5307983dbc55729fbc8415c9f5)
[2019-09-04T15:10:03-033299f/darwin-arm64-corellium](https://build.golang.org/log/d0303be867ce49764183bff4ea08a179e98e158f)
[2019-08-28T02:29:54-22355d6/darwin-arm64-corellium](https://build.golang.org/log/1e20cfd4d556220fd0921799ca8047ce7b78bb3c)
[2019-08-27T17:00:43-483d6d9/darwin-arm64-corellium](https://build.golang.org/log/b792c59b6940bfc107071ed467d4e1f31b635fee)

It's not obvious to me whether this is a bug in the test's expectations or setup, or the result of slower-than-expected execution on these builders.

CC @mikioh @bradfitz @ianlancetaylor
Repository: rust
Issue: Tracking issue for RFC 2963: rustdoc JSON backend
Labels: T-rustdoc,B-unstable,C-tracking-issue,A-rustdoc-json

RFC PR: https://github.com/rust-lang/rfcs/pull/2963/
RFC: https://rust-lang.github.io/rfcs/2963-rustdoc-json.html
Documentation: https://doc.rust-lang.org/nightly/nightly-rustc/rustdoc_json_types/
Issues: https://github.com/rust-lang/rust/labels/A-rustdoc-json

Todo:
 - [x] Implement a JSON backend according to the rough spec in the RFC
   - [x] Initial implementation in https://github.com/rust-lang/rust/pull/79539
   - [x] Make the changes to the format suggested in https://github.com/rust-lang/rust/pull/75114#pullrequestreview-525840373 : https://github.com/rust-lang/rust/pull/79539
   - (Fill this in with sub-steps)
 - [ ] Experiment with the JSON backend, improving the design as necessary
 - [ ] Potentially open a new RFC with the final design, and stabilize it
Repository: three.js
Issue: ExtrudeGeometry produces incorrect faces with negative bevelOffset
Labels: Bug

##### Description of the problem

When importing an SVG and using ExtrudeGeometry to extrude it, the resulting mesh has errors in the top and bottom faces if there is a negative bevelOffset. In my case am using a negative bevelOffset that matches the positive bevelSize to ensure that the 2d footprint of the SVG is unchanged. I see negative values are [allowed in the example](https://threejs.org/docs/#api/en/geometries/ExtrudeBufferGeometry) so I assume this is supported. The erronious facets seem to span concave sections of both the shape's exterior and holes.

[Take a look at this CodePen](https://codepen.io/hertafeld/pen/NWNYLMV) to see what I mean. Specifically look at the top and bottom/mouth of the dog. Perhaps an issue with buildLidFaces() in ExtrudeGeometry, since the beveled sides seem correct.

In the codePen I use r120 but I also see this issue with dev and r111.

I have also tried:
Removing all curves and using an input SVG with only straight lines
Manually creating the shape object from the shape and holes from the SVG
Many different SVGs

And see the same artifacts.

Using zero or more bevelOffset gives the correct result, but I would like the ability to inset as shown in the extrusion example.

Since this is my first issue report to this repo I apologies in advance if I'm missing any info or have misused this form.

##### Three.js version

- [x] Dev
- [x] r120

##### Browser

- [ ] All of them
- [x] Chrome
- [x] Firefox
- [ ] Internet Explorer

##### OS

- [ ] All of them
- [ ] Windows
- [x] macOS
- [ ] Linux
- [ ] Android
- [ ] iOS

##### Hardware Requirements (graphics card, VR Device, ...)
MacOS 10.14.5 Mojave 
Intel Iris Pro 1536 MB
Repository: rust
Issue: Wishlist: MIR-level integer range analysis
Labels: T-compiler,A-mir-opt

This is a tracking issue for MIR-level integer range analysis.
I'm very new to this, and there's no associated PR (yet!) nor RFC, so apologies in advance for any issues or mistakes.

### Description
Integration of integer range analysis into the MIR will allow the compiler to, on a best effort basis, understand roughly the range of values any particular expression can take on. 
```rust
let x: u8 = ...; // VRange(0, 255)
let b = x / 2; // VRange(0, 127)
assert!(b < 128); // Elideable, rustc understands b cannot be larger than 128.
                  // Removal of assert can be done at a MIR level instead of by LLVM!
let c = b << 2; // VRange(0, 255). Could theoretically be recognized as VRange(0, 0) + VRange(4, 255) at the cost of extra complexity.
assert!(c != 3); // Theoretically elidable, more complicated in practice, but plenty doable.
```
### Steps
- [ ] Initial implementation (gated)
### Unresolved Questions
- How much should the compiler attempt to *do*? There is, theoretically, a very large number of situations it could recognize.
- Should there be some way to access this best effort range data? It has it's uses, but the fact that this information is best-effort and not consistent would need special emphasis. Either way, an intrinsic in core would be helpful for writing unit tests.

@oli-obk per request.
Repository: TypeScript
Issue: Correct declaration emit of commonjs exports of namespace-nested classes
Labels: Bug

Followup to #40228:

```js
var NS = {}
NS.K =class {
    values() {
        return new NS.K()
    }
}
exports.K = NS.K;
```

**Expected behavior:**

Should produce d.ts output:

```ts
declare namespace NS {
    class _K {
      values(): _K;
    }
    export {_K as K};
}
import _K = NS.K;
export {_K as K};
```

**Actual behavior:**

```ts
export var K: {
    new (): {
        values(): any;
    };
};
```

1. Inlines and destroys the namespace.
3. Only exports a value, not a class.
2. Doesn't serialise nested typed references to `K` properly.

The declaration emitter just needs code to support this; right now it's hitting inaccurate fallback code.
Repository: pytorch
Issue: for_each doesn't support integer input to float output type promotion
Labels: triaged

Operators like true_divide and, in the very near future, div, promote integer inputs to float results. This is natural when the result of an operation is not representable in the input's dtype (another example would be sin(int)->float). The for_each operators on CUDA will cast the results of these operations back to the input dtype, however.

This is not an issue today but will be after https://github.com/pytorch/pytorch/pull/42907. These operations will work as expected on the CPU (as that PR demonstrates). 

cc @izdeby
Repository: godot
Issue: Multiple script entries in ui
Labels: bug,topic:editor,needs testing

After adding a script and selecting it I noticed in the lower right corner under the pause the section there was multiple script drop down controls. Restarting the engine it fixed itself but this seemed like bug. 





**Godot version:**
3.2.2


**OS/device including version:**
<!-- Specify GPU model, drivers, and the backend (GLES2, GLES3, Vulkan) if graphics-related. -->


**Issue description:**
<!-- What happened, and what was expected. -->


**Steps to reproduce:**
Not sure. After restart I was able to add a script and it was fine.


**Minimal reproduction project:**
<!-- A small Godot project which reproduces the issue. Drag and drop a zip archive to upload it. -->
Repository: pytorch
Issue: TestJitGeneratedModule.test_nn_CTCLoss_lengths_intlists fails if not skipped
Labels: oncall: jit

In https://github.com/pytorch/pytorch/commit/fa158c4ca60d7bcd1ec52bdfaf4cee2d8d9e923a, if I remove the `check_jit`s and run:

 `python test_jit.py TestJitGeneratedModule.test_nn_CTCLoss_lengths_intlists`

I get:
```
======================================================================
ERROR: test_nn_CTCLoss_lengths_intlists (__main__.TestJitGeneratedModule)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/data/users/gchanan/anaconda3/envs/__py37g11/lib/python3.7/site-packages/torch/testing/_internal/common_utils.py", line 528, in wrapper
    fn(*args, **kwargs)
  File "test_jit.py", line 15776, in do_test
    check_against_reference(self, create_script_module, create_nn_module, f_args_variable, no_grad=no_grad)
  File "test_jit.py", line 15380, in check_against_reference
    outputs_test = self.runAndSaveRNG(func, nograd_inputs, kwargs)
  File "/data/users/gchanan/anaconda3/envs/__py37g11/lib/python3.7/site-packages/torch/testing/_internal/jit_utils.py", line 583, in runAndSaveRNG
    results = func(*inputs, **kwargs)
  File "test_jit.py", line 15741, in create_script_module
    module = make_module(script)
  File "test_jit.py", line 15738, in make_module
    module.define(script)
  File "/data/users/gchanan/anaconda3/envs/__py37g11/lib/python3.7/site-packages/torch/jit/_script.py", line 311, in define
    return self._actual_script_module.define(src)
  File "/data/users/gchanan/anaconda3/envs/__py37g11/lib/python3.7/site-packages/torch/jit/_script.py", line 529, in define
    self._c._define(self._concrete_type, src, rcb)
RuntimeError: expected ident but found '[' here:
  File "<string>", line 2

def forward(self, i0, i1, [50, 50, 50], [30, 25, 20]):
                          ~ <--- HERE
    return self.submodule(i0, i1, [50, 50, 50], [30, 25, 20])
```

If I had to guess, it looks like a typing problem because CTCLoss in python supports int arrays, but the signature only says it supports Tensors.  But the error message is pretty incomprehensible, so this is a guess.

cc @gmagogsfm
Repository: pytorch
Issue: FP16 inference latency after sleeping
Labels: module: performance,triaged


[latency_test.py.txt](https://github.com/pytorch/pytorch/files/5204664/latency_test.py.txt)
## 🐛 Bug

Observing a latency variation of 3-4ms on half precision inference runs when we run inference intermittently (Sleeping 100ms between inference runs). We don't see this latency drop if inference is done from a tight loop. The issue can be reproduced with the attached python code (latency_test.py.txt). The attached python code uses resnet50 from torchvision library to reproduce the issue. 

## To Reproduce

Steps to reproduce the behavior:

1. Rename latency_test.py.txt to latency_test.py
2. python latency_test.py
3. Observe the prints
                Resnet50 FP16 tight loop:22.886486053466797 ms
                Resnet50 FP16 with delay:25.635948181152344 ms
                Resnet50 FP32 tight loop:46.42632484436035 ms
                Resnet50 FP32 with delay:45.696444511413574 ms


## Expected behavior

Latency variation shouldn't be this high for intermittent runs. Don't see this much variation in FP32 inference.

## Environment

Please copy and paste the output from our
[environment collection script](https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py)
(or fill out the checklist below manually).

You can get the script and run it with:
```
wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py
# For security purposes, please check the contents of collect_env.py before running it.
python collect_env.py
```

 - PyTorch Version (e.g., 1.0): 1.5.0+cu101
 - OS (e.g., Linux): Ubuntu 16.04
 - How you installed PyTorch (`conda`, `pip`, source):
 - Build command you used (if compiling from source):
 - Python version: 
 - CUDA/cuDNN version: 10.1, 7603
 - GPU models and configuration: GeForce RTX 2080 Ti
 - Any other relevant information:

## Additional context

<!-- Add any other context about the problem here. -->


cc @VitalyFedyunin @ngimel
Repository: pytorch
Issue: LR scheduler throws warning when using scaler.step instead of optimizer.step, and when saving optimizer state
Labels: triaged,module: amp (automated mixed precision)

## 🐛 Bug

The documentation provides [a code snippet](https://pytorch.org/docs/stable/amp.html#torch.cuda.amp.GradScaler.unscale_) to use the automatic scaling and AMP along side gradient clipping. If we also add a scheduler step into the mix, after the optimizer step, PyTorch throws a UserWarning which I do not think is correct:

> UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate
>   warnings.warn("Detected call of `lr_scheduler.step()` before `optimizer.step()`. "

## To Reproduce

Steps to reproduce the behavior (I also tried swapping the scaler.update and lr_scheduler.step to no avail):

```python
scaler.scale(loss).backward()
# Unscales the gradients of optimizer's assigned params in-place
scaler.unscale_(optimizer)
# clip in-place
clip_grad_norm_(self.model.parameters(), 1.0)
scaler.step(optimizer)
lr_scheduler.step()
scaler.update()
```

---

I also just found that when saving, I still get a warning even though I do save the LR state:

> /home/bram/.local/share/virtualenvs/dutch-simplification-NcpPZtDF/lib/python3.8/site-packages/torch/optim/lr_scheduler.py:200: UserWarning: Please also save or load the state of the optimzer when saving or loading the scheduler. warnings.warn(SAVE_STATE_WARNING, UserWarning)

But I save everything like this:

```python
    def save_model(self, epoch, loss):
        """ Saves current model as well as additional information. """
        info_dict = {
            "model_state_dict": self.model.state_dict(),
            "optimizer_state_dict": self.optimizer.state_dict(),
            "lr_scheduler_state_dict": self.lr_scheduler.state_dict(),
            "last_epoch": epoch,
            "loss": loss
        }

        torch.save(info_dict, f"checkpoint_{epoch}.pth")
```

**Bonus question**: should I also save/load the scaler's state dict? What is saved in the scaler's state?
    

<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->

## Expected behavior

I would expect no warning here because I would expect that scaler.step calls the optimizer.step. In addition I would not expect a warning during saving.

## Environment

 - PyTorch Version (e.g., 1.0): 1.6
 - OS (e.g., Linux): Windows
 - How you installed PyTorch (`conda`, `pip`, source): pip
 - Python version: 3.8
 - CUDA/cuDNN version: 10.1
 - GPU models and configuration: RTX 2080TI



cc @mcarilli
Repository: go
Issue: x/tools/gopls: creates many /tmp/go-build* directories
Labels: gopls,Tools

This is a dup of #37368, it was never fixed for me, I still end up with a few (*mostly* empty) `/tmp/go-build*` folders.

I can reproduce with current release, master and my ghetto rebased master on top of dev.go2go.

```sh
$ ls /tmp/go-build* | wc -l
2874
$ find /tmp/go-build* -type f | wc -l
261
$ ● find /tmp/go-build* -type f | tail -n 6
/tmp/go-build969177635/b001/exe/a.out
/tmp/go-build969177635/b001/importcfg.link
/tmp/go-build992265488/b001/exe/a.out
/tmp/go-build992265488/b001/importcfg.link
/tmp/go-build996176431/b001/exe/a.out
/tmp/go-build996176431/b001/importcfg.link
```