WhereIsAI/github-issue-similarity huggingface dataset:
Issue 1:
Hi, everyone

According to documentation, np.float16 should not be used in C code as it is right now in pandas/algos.pyx:38 (assigning np.NINF to a np.float16 container in "cython-eventually-converted-to-C" code

As a matter of fact, when this value/variable is exposed, it is exposed as 0.0, and not -inf, either in C/C++ while using gdb or even with python.

Since MINfloat6 it is not being used in all pandas code, I recommend eliminating this line and/or commentating the reason. I tried by using np.float16(np.NINF) but the problem is that a correct C -inf value is still being C-assigned to a please-don't-use-C-container.

Why is it that this is important to me/I got to find this error? I'm dealing with 3rd party code so I cannot get a configuration that allows me to do this assignment without raising SIGFPE.

No pull request? The two possible solutions to me is either using cython-kept-as-python code with just this variable (which would impact performance) or simply not using it at all. I think I don't have the authority for either.

Issue 2:
The code generated by Cython from pandas/algos.pyx contains an undefined behavior (invalid cast of floating-point number to integer):

easy_install -U Cython
git clone git://github.com/pydata/pandas.git
python setup.py install

The generated code for the line from pandas/algos.pyx

cdef np.float16_t MINfloat16 = np.NINF

in pandas/algos.c contains:

typedef npy_float16 __pyx_t_5numpy_float16_t;
//... more code
__pyx_t_5numpy_float16_t __pyx_t_10;
//... more code
__pyx_t_10 = __pyx_PyFloat_AsFloat(__pyx_t_1);

The file numpy-1.9.2-py2.7-linux-x86_64.egg/numpy/core/include/numpy/npy_common.h

defines npy_float16 as 16-bit integer type (I guess numpy implementation has a custom code that carefully represents these 16 bits as mantiss+exponent). Casting floating-point value -inf to a 16-bit integer type is an undefined behavior according to C standard.

I wasn't able to find any place in pandas code that uses MINfloat16 (and MAXfloat16). Is it safe to just delete them?
Issue 1:
vscode.window.showInputBox() lets me accept user input through the command palette, but it is a two step process. I have to first execute a command and then i have to show the input box. Ideally, I should be able to prompt and accept input in one command, much in the way the git branch prompt does:

here i'm prompted with "git checkout", if i have a set of branches they are shown in the list, and I can free form type in any text in order to create a new branch.

As an extension author I would love to be able to have a similar model. For example, let's say I want to have a list of tasks and each task can take a parameter.

myextension create thing1

myextension create thing2

in this case i don't want to select "myextension create thing1" and then have an input box. What i want is to be able to say "myextension thing1 thing1's description". This is also similar to the extension install experience where i can say ext install myextension where myextension is free form text.

Issue 2:
If I accept input using

window.showInputBox();

Is there any way I can listen to key change events? I am writing an extension and I want to accept an input from the user and I want to autocomplete when the user hits tab like most terminals. If this is not possible inside the inputbox is there any other control I can use to achieve this?
Issue 1:
On Python 3.4

======================================================================

FAIL: Doctest: model_selection.rst

----------------------------------------------------------------------

Traceback (most recent call last):

File "/home/travis/miniconda/envs/testenv/lib/python3.4/doctest.py", line 2193, in runTest

raise self.failureException(self.format_failure(new.getvalue()))

nose.proxy.AssertionError: Failed doctest test for model_selection.rst

File "/home/travis/build/scikit-learn/scikit-learn/doc/tutorial/statistical_inference/model_selection.rst", line 0

----------------------------------------------------------------------

File "/home/travis/build/scikit-learn/scikit-learn/doc/tutorial/statistical_inference/model_selection.rst", line 148, in model_selection.rst

Failed example:

clf.best_score_ # doctest: +ELLIPSIS

Expected:

0.924...

Got:

0.93561368209255535

----------------------------------------------------------------------

File "/home/travis/build/scikit-learn/scikit-learn/doc/tutorial/statistical_inference/model_selection.rst", line 150, in model_selection.rst

Failed example:

clf.best_estimator_.gamma == 1e-6

Expected:

True

Got:

False

>> raise self.failureException(self.format_failure(<_io.StringIO object at 0x2b02dcc8fee8>.getvalue()))

See for example https://travis-ci.org/scikit-learn/scikit-learn/jobs/47771718

Issue 2:
This was triggered on Travis by a completely unrelated commit.

It seems that the tutorial gets a better-than-expected result on digits :)

The Travis configuration that failed is DISTRIB="conda" PYTHON_VERSION="2.6" INSTALL_MKL="false" NUMPY_VERSION="1.6.2" SCIPY_VERSION="0.11.0"

I: Seeding RNGs with 565480497
[...]
======================================================================
FAIL: Doctest: model_selection.rst
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/travis/anaconda/envs/testenv/lib/python2.6/doctest.py", line 2163, in runTest
raise self.failureException(self.format_failure(new.getvalue()))
AssertionError: Failed doctest test for model_selection.rst
File "/home/travis/build/scikit-learn/scikit-learn/doc/tutorial/statistical_inference/model_selection.rst", line 0

----------------------------------------------------------------------
File "/home/travis/build/scikit-learn/scikit-learn/doc/tutorial/statistical_inference/model_selection.rst", line 148, in model_selection.rst
Failed example:
clf.best_score_ # doctest: +ELLIPSIS
Expected:
0.924...
Got:
0.93561368209255535
----------------------------------------------------------------------
File "/home/travis/build/scikit-learn/scikit-learn/doc/tutorial/statistical_inference/model_selection.rst", line 150, in model_selection.rst
Failed example:
clf.best_estimator_.gamma == 1e-6
Expected:
True
Got:
False
Issue 1:
I have searched the issues of this repository and believe that this is not a duplicate.

I have checked the FAQ of this repository and believe that this is not a duplicate.

Environment

Dubbo version: 2.7.2

Operating System version: mac

Java version: 1.8

Steps to reproduce this issue

xxx

xxx

xxx

Pls. provide [GitHub address] to reproduce this issue.

Expected Result

What do you expected from the above steps？

Actual Result

What actually happens?

If there is an exception, please attach the exception trace:

Just put your stack trace here!

Issue 2:
I have searched the issues of this repository and believe that this is not a duplicate.

I have checked the FAQ of this repository and believe that this is not a duplicate.

Environment

Dubbo version: 2.7.8

Operating System version: macos

Java version: 1.8

Steps to reproduce this issue

use the "dubbo-samples-zookeeper" project

add "dubbo.properties" in the spring directory

add dubbo.service.shutdown.wait in the dubbo.properties

4、kill -15 the project's process

5、the log print "Waiting 10000ms for registry to notify all consumers before unexport"。

always use the default value
Issue 1:
Hello,

I'm new trying the PowerToys. For now it works just fine.

I only have a sugestion about the contextual menu icon. I'm not sure if this request was made already, so here we go.

On the dark theme, the icon for PowerRename contextual menu is no clearly visible, as it is black, over a dark background. So I thought that you maybe can make it white, or some brighter color (like the Share icon).

Issue 2:
Recent change updated the power rename icon

See change: #4877
Issue 1:
Based on my knowledge regarding BTrees (I know how they are built, how they work and I also know what a B+ tree is), which I know is by far not the best, I think you might really want to use a B+ Tree](https://en.wikipedia.org/wiki/B%2B_tree) if you do not already.

That would make the predecessor and successor methods currently discussed here quite fast with considerable memory-overhead, also it would make implementing iterators (and cursors) and therefore the range API a lot easier and probably faster.

If I am missing something, please tell me.

Issue 2:
Looking through the rustc source code, it seems like many potential areas for typestate predicates involve cases where a value has an enum type and is known to be built with one of a subset of that enum type's constructors. I'm especially noticing this now that I'm going through and making all alts exhaustive. For example:

enum foo {bar, quux, baz}

fn whee(x: foo) -> int {
alt x {
bar { 42 }
_ { fail "The impossible happened!"; }
}
}

We'd like to give whee a type that says x must be constructed with constructor bar -- in other words, a type that's a refinement on foo -- so that we don't need to write the fail case (because we can statically check that x is in the right subset of foo). We can do this right now with typestate predicates. However, datasort refinements seem to be a common enough case that it might be worth treating them separately. Also, datasort refinements can be implemented without needing to worry about any of the purity issues that have made typestate difficult, because once something of enum type is constructed, its tag is immutable.

The other reason to handle this separately from typestate is that the compiler never uses typestate information to eliminate any checks (since predicates may be unsound), whereas datasort refinements can be checked statically and used to eliminate unnecessary tests in alts.

Thoughts?
Issue 1:
Working with real world data, it is common to encounter poorly formatted files and such. One thing I find myself frequently doing is something like this in DataFrames:

julia> int(df["Age"])
ERROR: ArgumentError("'F' is not a valid digit (in \"F\")")
in parseint at string.jl:1209
in int at string.jl:1242
in map_to2 at abstractarray.jl:1450
in map at abstractarray.jl:1459
in int at /Users/viral/.julia/DataFrames/src/dataarray.jl:746

The Age column should been integers, but there is some bad data in the column, as a result of which readtable left the column as a string. When I try to use int, it is obvious that the data is corrupt, but I have no idea where it is corrupt.

I often find myself doing something like:

[ try int(df["Age"][i]) catch end for i in 1:nrow(df) ]

I think that an isconvertible function would be generally useful, which takes the same arguments as convert, but returns a boolean based on whether the conversion is possible or not.

Issue 2:
I've tried to run Julia (d4b825a) tests and it failed on linalg. The problem is in @test_approx_eq macro since epsilon is too small in this case.

julia> include("runtests.jl")
* core
* keywordargs
* numbers
* strings
* unicode
* collections
* hashing
* remote
* iobuffer
* arrayops
* linalg
exception on 1: ERROR: assertion failed: |:(\(W,v)) - iFv| <= 7.105427357601002e-10
:(\(W,v)) = -105.1618383308551
30.132386546280248
103.10478249210304
85.5026629379321
42.8100069344292

iFv = -105.16183834007155
30.132386548803957
103.10478250114792
85.50266294548928
42.81000693815962

difference = 9.21644982554426e-9 > 7.105427357601002e-10
in error at error.jl:22
in test_approx_eq at test.jl:68
in anonymous at no file:459
in runtests at /home/magistere/projects/git/julia/test/testdefs.jl:5
in anonymous at multi.jl:613
in run_work_thunk at multi.jl:575
in remotecall_fetch at multi.jl:647
in remotecall_fetch at multi.jl:662
in anonymous at multi.jl:1382
while loading linalg.jl, in expression starting on line 384
ERROR: assertion failed: |:(\(W,v)) - iFv| <= 7.105427357601002e-10
:(\(W,v)) = -105.1618383308551
30.132386546280248
103.10478249210304
85.5026629379321
42.8100069344292

iFv = -105.16183834007155
30.132386548803957
103.10478250114792
85.50266294548928
42.81000693815962

difference = 9.21644982554426e-9 > 7.105427357601002e-10
in error at error.jl:22
in test_approx_eq at test.jl:68
in anonymous at no file:459
in runtests at /home/magistere/projects/git/julia/test/testdefs.jl:5
in anonymous at multi.jl:613
in run_work_thunk at multi.jl:575
in remotecall_fetch at multi.jl:647
in remotecall_fetch at multi.jl:662
in anonymous at multi.jl:1382
while loading linalg.jl, in expression starting on line 384
while loading /home/magistere/projects/git/julia/test/runtests.jl, in expression starting on line 22
Issue 1:
It was a nice addition to the template, and since we are still able to use navbar-inverse I don't see why the btn is a different matter.

Issue 2:
In the new 3 release btn-inverse has been dropped because no semantic excuse could be made for it. I've got one now: "advanced" or "technical" actions.

This is the button for when things get hardcore. Popping open dark backroom controls and analytics spreads. Smokey translucent divs with streams of log printouts. Carbon panels with master delete switches.

This is the button that transitions you from the bright light white site to the burnt out night theme of everyone's favorite text editor and Alien Blue.

TBH I'm biased, my heart aches to see that blackness missing from the button rainbow. But for reals, I would use this as the "let's get serious" button.
Issue 1:
I am trying to work on a connection plugin using guestfish to modify a disk image directly. And while testing on devel ( 07a0059 ), I stumbled on a few hurdles like this:

$ ansible -vvv all -c guestfs -i 'Fedora-Cloud-Atomic-23-20151215.x86_64.qcow2:/dev/sda1,' -m ping
Using /home/misc/.ansible.cfg as config file
Unexpected Exception: 'NoneType' object has no attribute 'split'
the full traceback was:

Traceback (most recent call last):
File "/home/misc/checkout/git/ansible/bin/ansible", line 85, in <module>
sys.exit(cli.run())
File "/home/misc/checkout/git/ansible/lib/ansible/cli/adhoc.py", line 187, in run
result = self._tqm.run(play)
File "/home/misc/checkout/git/ansible/lib/ansible/executor/task_queue_manager.py", line 237, in run
play_return = strategy.run(iterator, play_context)
File "/home/misc/checkout/git/ansible/lib/ansible/plugins/strategy/linear.py", line 217, in run
task_vars = self._variable_manager.get_vars(loader=self._loader, play=iterator._play, host=host, task=task)
File "/home/misc/checkout/git/ansible/lib/ansible/vars/__init__.py", line 245, in get_vars
all_vars = combine_vars(all_vars, host.get_vars())
File "/home/misc/checkout/git/ansible/lib/ansible/inventory/host.py", line 129, in get_vars
results['inventory_hostname_short'] = self.name.split('.')[0]
AttributeError: 'NoneType' object has no attribute 'split'

After digging, I found that this function https://github.com/ansible/ansible/blob/devel/lib/ansible/parsing/utils/addresses.py#L167 kinda filter all non standards address, and reutnr None,None unless I am careful into what I give for argument.

This also break connection plugins like chroot, jail and potentially the docker one.

Issue 2:
Issue Type:

Bug Report

Ansible Version:

1.9.0.1

Environment:

All

Summary:

The win_copy/win_file/win_template modules were added during earlier development and then reverted. However, win_copy.py and win_template.py remain in the actions_plugins dir.

If they're going to eventually be used, we can keep them - if the new version of the modules isn't going to use them, they should probably be removed.

Steps To Reproduce:

Look in the actions plugin directory.
Issue 1:
Description of the problem

I think this implementation of Vector3.prototype.projectOnVector:

projectOnVector = function ( vector ) {
var length = vector.length();
var scalar = this.dot(vector) / (length * length);
return vector.multiplyScalar(scalar);
};

would be significantly faster than the current implementation:

projectOnVector: function () {
var v1, dot;
return function projectOnVector( vector ) {
if ( v1 === undefined ) v1 = new THREE.Vector3();
v1.copy( vector ).normalize();
dot = this.dot( v1 );
return this.copy( v1 ).multiplyScalar( dot );
};
}()

The allocation of a temporary Vector3 is stripped away, and it uses two function calls less.

Three.js version

Dev

r77

r73

Browser

All of them

Chrome

Firefox

Internet Explorer

OS

All of them

Windows

Linux

Android

IOS

Hardware Requirements (graphics card, VR Device, ...)

Issue 2:
Description of the problem

At the moment, the Object3D.updateMatrix method,

three.js/src/core/Object3D.js

Lines 592 to 598
in
f55b3d3

updateMatrix: function () {

this.matrix.compose( this.position, this.quaternion, this.scale );

this.matrixWorldNeedsUpdate = true;

},

always causes this.matrixWorldNeedsUpdate to be true.

Instead it can set that to true only if the resulting composition results in a changed local matrix.

Would detecting a change be cheaper than just always multiplying the parent world matrix? My gut feeling is that it would be.
Issue 1:
I have searched the issues of this repository and believe that this is not a duplicate.

I have checked the FAQ of this repository and believe that this is not a duplicate.

Environment

Dubbo version: 2.7.7

Java version: 1.8

Steps to reproduce this issue

In the process of switching Zookeeper to Nacos, we found that the startup speed of the application became slower, and a large number of Nacos threads were created. Looking at the source code, we found that every service registered in the application startup process would create a NamingService. We started to think that was the design. After a thorough review, we found that Dubbo had a static cache for the Nacos registry, and the cache key contained a dynamic parameter timestamp , every time a new service is registered, the latest value will be retrieved, and eventually the registry static cache will be invalid.

请大佬帮忙看看，多谢！

Just put your stack trace here!

Issue 2:
I have searched the issues of this repository and believe that this is not a duplicate.

I have checked the FAQ of this repository and believe that this is not a duplicate.

Environment

Dubbo version: 2.7.6

Operating System version: springboot 2.1.7，zookeeper 3.6.1

Java version: java8

Steps to reproduce this issue

设置接口DemoDubboService。

两个实现类DefaultDemoServiceImpl1，DefaultDemoServiceImpl2

分别设置@service(version = "1.0.0",group = "demo1"），@service(version = "1.0.0",group = "demo2"）

Pls. provide [GitHub address] to reproduce this issue.

Expected Result

demo1,demo2都在zk注册

Actual Result

只能在zk中找到demo1

If there is an exception, please attach the exception trace:

Just put your stack trace here!
Issue 1:
🐛 Bug

Inconsistent shape of index returned from F.max_pool2d_with_indices when used with CUDA tensor. This applies to 3-dimensional tensors.

To Reproduce

x = torch.randn(2, 2, 2).to('cuda')
pred, idx = F.max_pool2d_with_indices(x, 2)
print(pred.shape) # torch.Size([2, 1, 1])
print(idx.shape) # torch.Size([1, 2, 1, 1]) which is inconsistent

Interestingly, this doesn't happen to CPU's tensor.

x = torch.randn(2, 2, 2).to('cpu')
pred, idx = F.max_pool2d_with_indices(x, 2)
print(pred.shape) # torch.Size([2, 1, 1])
print(idx.shape) # torch.Size([2, 1, 1]), expected

Environment

PyTorch version: 1.5.0
Is debug build: No
CUDA used to build PyTorch: 10.1

OS: Ubuntu 18.04.4 LTS
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
CMake version: Could not collect

Python version: 3.7
Is CUDA available: Yes
CUDA runtime version: 10.1.105
GPU models and configuration: GPU 0: GeForce GTX 1060 6GB
Nvidia driver version: 440.44
cuDNN version: Could not collect

Versions of relevant libraries:
[pip] efficientnet-pytorch==0.6.3
[pip] msgpack-numpy==0.4.4.3
[pip] numpy==1.18.1
[pip] segmentation-models-pytorch==0.1.0
[pip] torch==1.5.0
[pip] torchtext==0.5.0
[pip] torchvision==0.6.0a0+82fd1c8
[conda] blas 1.0 mkl
[conda] efficientnet-pytorch 0.6.3 pypi_0 pypi
[conda] mkl 2020.0 166
[conda] mkl-service 2.3.0 py37he904b0f_0
[conda] mkl_fft 1.0.15 py37ha843d7b_0
[conda] mkl_random 1.1.0 py37hd6b4f25_0
[conda] pytorch 1.5.0 py3.7_cuda10.1.243_cudnn7.6.3_0 pytorch
[conda] segmentation-models-pytorch 0.1.0 dev_0 <develop>
[conda] torchtext 0.5.0 pypi_0 pypi
[conda] torchvision 0.6.0 py37_cu101 pytorch

Issue 2:
🐛 Bug

The parameter requires_grad of new_zeros method of tensors can't set the requires_grad attribute of the created tensor correctly.

To Reproduce

import torch
a = torch.tensor((1.0,))
print(a.new_ones((1,), requires_grad=True).requires_grad)
print(a.new_zeros((1,), requires_grad=True).requires_grad)

Expected behavior

The output of both print statements should be True. However, while the first is True, the second is False.

Environment

PyTorch Version (e.g., 1.0): 1.4.0

OS (e.g., Linux): Ubuntu 18.04.2 LTS

How you installed PyTorch (conda, pip, source): conda

Build command you used (if compiling from source):

Python version: 3.6

CUDA/cuDNN version: 10.1/7.6.1

GPU models and configuration: GeForce RTX 2080 Ti

Additional context

The requires_grad parameter of new_empty and new_full methods also doesn't work.

cc @ezyang @gchanan @zou3519
Issue 1:
Glide Version:

4.1

Integration libraries:

Device/Android Version:

Issue details / Repro steps / Use case background:

@sjudd

It seems 9d10097 does not take in account the fact that a request is completed. So restarting a request that is completed will actually do nothing.

Maybe this is on purpose but this implies a side effect of preventing a force reload when needed and does not trigger any listener.

For example in my case when doing transition my fetcher only load the image from cache to avoid slowing down the transition. Then after the transition start a full download by restarting the glide query. (I'm pretty sure it would be the same for any app that would for example restart the requests after network is connected when first one occurred without network).

With 3 and 4.0 it works correctly, but with 4.1 the subsequent query is not restarted preventing the actual load to happens and do not trigger any listener to warn the application.

Cheating with different models could work, but then when the image is actually already cached it would trigger a real reload and not a memory hit cache as Glide think it's different items.

I understand the purpose of the optimization but impact are not neutral if the completed state is not taken in account.

Maybe by default if state is completed then the request should restart, and an option to always prevent restart provided. Or add an option to force the reload. Or a new Listener event so app can cheat (But for this one impact may be huge on API clients).

Can PR something but need your choice and target. (And maybe an hint for the detection of completed state)

Default should maybe work like actual, or maybe add something in the documentation, as the total ignore of the request may trouble clients to actually understand what is happening.

Issue 2:
Glide Version/Integration library (if any): 3.5.2

Device/Android Version: Sony Xperia T / 4.1, Samsung S3 mini / 4.2

Issue details/Repro steps:

Loading .webp images results in black image loaded. Images are loaded from Internet

using thumbor. If they are loaded from local file all works fine.

I am using Pollexor to create thumbor urls. Here is example code:

final String format = Build.VERSION.SDK_INT >= Build.VERSION_CODES.ICE_CREAM_SANDWICH ? "webp" : "jpeg";

final String thumborUrl = Thumbor
.create(ConstantsApi.THUMBOR_URL)
.buildImage(imageUrl)
.filter(String.format(Locale.US, "format(%s)", format))
.filter(String.format(Locale.US, "quality(%d)", 50))
.resize(downloadWidth, downloadHeight)
.fitIn()
.toUrl();

Glide.with(this)
.load(thumborUrl)
.into(imageView);

The problem only occurs on pre 4.4.2 devices.

Here is a sample thumbor image to test:

http://img.jafolders.com/unsafe/fit-in/1057x1500/filters:format(webp):quality(100)/http://jafolders.com/uploads/pages/551c0648733100679823e70a/normal_image_1427899988.jpg

I've tested it same way but using Picasso and it works.

I hope for quick response as it is very important bug for me that needs to be quickly repaired.