WhereIsAI/github-issue-similarity huggingface dataset:
Issue 1:
I think this could be a very practical solutions for whole set of issues where aggregation needs to be done on nested documents but be able to group on its parent document properties. It i snot the same as using reverse nested because reverse nested changes document scope to groupp and then it will needs to be changed back to nested again introducing double-counting.

Also from ease of use it would be a significant step forward comparing to using reverse nested just to be able to access parent document properties (even if were equivalent)

Here is an example I used in another issue:

Imagine having Teams that handle Requests of certain Priority. Each request may have multiple teams assigned to it and each team request assignment has number of hours allocated for this team on this request

request:{
priority:1,
teams:[
{name:'team1', hours:10},
{name:'team2', hours:20},
]
}

I need a report that gives me number of hours by team by priority. Doing nested aggregation on teams.name and then reverse nested to group on priority and then nested to sum teams.hours double-counts hours because second nesting on teams knows nothing about upstream nesting as it is executed in context of request and as result it will lump hours for each team on request under the top level team aggregation

If i could access "../priority" from teams nested document context I would not have to use reverse nested and lose my aggregation context and everything would have worked like a charm

please see also #16380

Issue 2:
If multiple hosts are running on a single server hostname doesn't provide an accurate picture of the recovery process. Addition of node name, or at least the ports would differentiate between the different running processes.
Issue 1:
As @giordano and I are working on microarchitecture-specific tarballs in BinaryBuilder, we are currently importing a modified version of CpuId.jl, however if Base is going to be selecting artifacts based on the current CPU type, it makes sense that something with similar capabilities to this CpuId package would become a stdlib at the least, if not a part of Base.

We already have some CPUID code in src/processor*.cpp; should we export jl_test_cpu_feature() and simply maintain a Julia mapping of the flags? The way I see it, we have three options:

Export the C function, create a simple wrapper stdlib that has various flag definitions and invokes a ccall() to probe CPUID bits.

Leave the C code alone, duplicate the functionality in pure Julia (similar to CpuId.jl), but have it shipped by default with Julia.

Force all packages that want to use microarchitecture-specific artifacts to do the artifact selection themselves (this will only work for lazy artifacts; no Pkg.add()-time artifacts allowed)

I am in favor of 1 or 2, and between the two of them I'm in favor of (2) because it is most maintainable.

Issue 2:
Julia should have linear/cubic/spline interpolation functions in 1+ dimensions, along the line of Matlab's interp1 and friends.

This is a basic tool for numerical calculations, and shouldn't be too hard to implement.
Issue 1:
Chris Bonham opened SPR-7833 and commented

Sometimes it is necessary to strictly update the cache without retrieving the value first, for example when pre-loading a cache. I've created a patch that adds a boolean updateOnly flag to the @Cacheable annotation that is false by default. If set to true, the underlying cache is not consulted, but rather the return value is placed directly in the cache.

Affects: 3.1 M1

Attachments:

updateOnly.patch (4.83 kB)

3 votes, 2 watchers

Issue 2:
David Medinets opened SPR-1956 and commented

Given a bean definition like this:

<bean id="com.foo.BarRepository" class="com.foo.BarRepositoryJDO">

<constructor-arg><ref bean="javax.jdo.PersistenceManagerFactory" /></constructor-arg>

</bean>

If the beanFactory.getBean(Bar.class) signature were supported (ie, added to the BeanFactory interface), it would be possible to do:

BarRepository barRepository = (BarRepository)beanFactory.getBean(BarRepository.class);

This syntax is better than getBean("barRepository") because the Java compiler verifies the class name is spelled correctly. While advanced IDEs (ie, Eclipse) support does handle class name verification, not all people will be using such an IDE.

It may or may not be a good idea to require the Class to actually be an interface to ensure that 'best practices' are followed.

Issue Links:

#10200 Support BeanFactory#getBean(Class requiredType) ("duplicates")

1 votes, 1 watchers
Issue 1:
After doing

script/grunt install --installdir=/usr/local

there are no icons for the atom launcher.

The .desktop file /usr/local/share/applications/atom.desktop references the icon at /usr/local/share/atom/resources/app/resources/atom.png but the folder app/resources does not exist.

Issue 2:
When installing from source by following this guide the following .desktop entry is created in /usr/local/share/applications/atom.desktop:

[Desktop Entry]
Name=Atom
Comment=A hackable text editor for the 21st Century.
GenericName=Text Editor
Exec=/usr/local/share/atom/atom %U
Icon=/usr/local/share/atom/resources/app/resources/atom.png
Type=Application
StartupNotify=true
Categories=GNOME;GTK;Utility;TextEditor;Development;
MimeType=text/plain;

However, /usr/local/share/atom/resources/app/resources/atom.png does not exist.

$ ls /usr/local/share/atom/resources/app/
apm atom.sh

I solved it by simply copying the atom icon in atom/resources/atom.png over to the expected path.

cd atom
sudo cp --parents resources/atom.png /usr/local/share/atom/resources/app/

I didn't find this bug last week when installing release 0.192.0. Maybe this was introduced on 0.193.0 because of the asar archive file.
Issue 1:
npmBug.zip

Current Behavior:

When npm i --production is called, all project files referenced in devDependencies are deleted

Expected Behavior:

npm doesn't delete our project files

Steps To Reproduce:

Run npm i --production in the main directory of the attached zip.

The dependencies folder will be deleted as a result of step 1

Environment:

OS: MacOS Catalina 10.15.7

Node: 14.7.0

npm: 7.6.3

Issue 2:
Current Behavior:

We have a local development dependency defined like this:

"devDependencies": {
"our-local-dependency": "../../lib/our-local-dependency"
}

When running npm prune --production, npm does not only delete the softlink but also the directory it is pointing to.

Expected Behavior:

My assumption was that only the softlink would be deleted, but not the actual dependency itself

Steps To Reproduce:

Create a project with a local development dependency

run npm install

run npm prune --production

Environment:

OS: Debian bullseye/sid

Node: v12.16.3 (nvm)

npm: 7.6.3 (I was able to reproduce this issue with 6.x.x as well)
Issue 1:
loader.load( this.properties.geometrySrc , this.properties.materialSrc)

The only warning i got is

THREE.MeshPhongMaterial: 'diffuse' is not a property of this material.

This is the mtl file that we load

newmtl material_0
Ka 1.000000 0.000000 0.000000
Kd 1.000000 1.000000 1.000000
Ks 0.000000 0.000000 0.000000
Tr 1.000000
illum 0
Ns 0.000000
map_Kd model_texture.jpg

Issue 2:
Description of the problem

three.min.js r88 stopped rendering the canvas for some of my work with the update from macOS 10.13.1 and Safari 11.0.1 to macOS 10.13.2 and Safari 11.0.2 on iMac 14.2. RayCaster, or at least its methods setFromCamera and intersectsObjects, still works. Everything worked with macOS 10.13.1 and Safari 11.0.1. It still works with macOS 10.13.2 and Mozilla Firefox 57.0.1. My partly corresponding raw WebGL work still works with macOS 10.13.2 and Safari 11.0.2. I also re-tested my work with macOS 10.13.1 and Safari 11.0.1 on MacBook Pro 15"/2012 with NVIDIA GeForce GT 650M, and all works there.

three.js has always thrown the warning below but still worked OK, so I don't know if it can be related. No other errors or warnings reported.

[Warning] THREE.WebGLShader: gl.getShaderInfoLog() (3) (three.min.js, line 77)

"fragment"

"WARNING: 0:1: extension 'GL_ARB_gpu_shader5' is not supported

"

a 1001 line dump

I checked some of the examples on the three.js website, and some render the canvas, others not:

OK: https://threejs.org/examples/#webgl_animation_cloth

No: https://threejs.org/examples/#webgl_animation_keyframes_json

No: https://threejs.org/examples/#webgl_animation_scene

No: https://threejs.org/examples/#webgl_animation_skinning_blending

No: https://threejs.org/examples/#webgl_animation_skinning_morph

OK: https://threejs.org/examples/#webgl_camera

OK: https://threejs.org/examples/#webgl_camera_array

Three.js version

Dev

r88

...

Browser

All of them

Chrome

Firefox

Internet Explorer

Safari

OS

All of them

Windows

macOS

Linux

Android

iOS

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

iMac 14.2 (Intel Core i7, 3,5 GHz, 16 GB, Boot ROM IM142.0123.B00)

GPU: NVIDIA GeForce GTX 780M, ROM 3782
Issue 1:
🐛 Bug

Pillow just released 7.0.0. This breaks torchvision.transforms as the import fails:

Traceback (most recent call last):
File "examples/mnist.py", line 5, in <module>
from torchvision.transforms import (
File "/home/felix/.local/share/virtualenvs/repro-Cd9xy9KO/lib/python3.6/site-packages/torchvision/__init__.py", line 4, in <module>
from torchvision import datasets
File "/home/felix/.local/share/virtualenvs/repro-Cd9xy9KO/lib/python3.6/site-packages/torchvision/datasets/__init__.py", line 9, in <module>
from .fakedata import FakeData
File "/home/felix/.local/share/virtualenvs/repro-Cd9xy9KO/lib/python3.6/site-packages/torchvision/datasets/fakedata.py", line 3, in <module>
from .. import transforms
File "/home/felix/.local/share/virtualenvs/repro-Cd9xy9KO/lib/python3.6/site-packages/torchvision/transforms/__init__.py", line 1, in <module>
from .transforms import *
File "/home/felix/.local/share/virtualenvs/repro-Cd9xy9KO/lib/python3.6/site-packages/torchvision/transforms/transforms.py", line 17, in <module>
from . import functional as F
File "/home/felix/.local/share/virtualenvs/repro-Cd9xy9KO/lib/python3.6/site-packages/torchvision/transforms/functional.py", line 5, in <module>
from PIL import Image, ImageOps, ImageEnhance, PILLOW_VERSION
ImportError: cannot import name 'PILLOW_VERSION'

https://github.com/python-pillow/Pillow/releases

To Reproduce

run

from torchvision.transforms import Compose

Environment

torch==1.3.1

torchvision==0.4.2

torchvision==7.0.0

Fix

I pinned pillow to version 6.2.2. After this the import works again

Issue 2:
Hi, it looks like Pillow released version 7.0.0 so I can no longer import torchvision without getting this error: ImportError: cannot import name 'PILLOW_VERSION' from 'PIL'

Pinning to Pillow 6.2.1 fixes the issue.

I see that you fixed this for a future torchvision release (#1501). Do you know when this will be released? If it will be awhile, could the version of Pillow be pinned to be less than 7.0.0 in the meantime?

Thanks.

Versions:

torch: 1.3.1

torchvision: 0.4.2

Pillow: 7.0.0
Issue 1:
Just using the existing configuration framework, but with a file format like matplotlib uses... See how they do it here: http://matplotlib.org/users/customizing.html.

Plus, we can document all the config options in a single file.

related #2452, #3046

Issue 2:
Tested on 0.13.0, 0.13.1 and 0.14.0rc1:

from StringIO import StringIO
import pandas as pd

# this is correct
print(pd.DataFrame([[0, 1, 2], [3, 4, 5]], columns=["a", "b", "a"]))
# and this is fine as well
# (although, changing the column names to a,b,a.1)
print(pd.read_csv(StringIO("a,b,a\n0,1,2\n3,4,5")))
# but this is not correct
print(pd.read_csv(StringIO("0,1,2\n3,4,5"), names=["a", "b", "a"]))

The last one returns:

Out[5]:
a b a
0 2 1 2
1 5 4 5

I would expect all 3 methods to return the same DataFrame. I noticed this when I wanted to read csv file that had a separate file with a header (and a duplicated column in it). BTW is there a better way to do it than to read the header file first and pass the output into 'names' parameter of read_csv?
Issue 1:
Do you want to request a feature or report a bug?

Report a bug

What is the current behavior?

When using a number input that has a defaultValue, you cannot enter a decimal value. When attempting to type a decimal, the cursor is moved to the beginning of the input. This behavior is not observed when there is no defaultValue given.

If the current behavior is a bug, please provide the steps to reproduce and if possible a minimal demo of the problem via https://jsfiddle.net or similar (template: https://jsfiddle.net/reactjs/69z2wepo/).

https://jsfiddle.net/zc0x3egz/

What is the expected behavior?

The input should allow you to type a decimal.

Which versions of React, and which browser / OS are affected by this issue? Did this work in previous versions of React?

This is happening with React 15.3.2. It does not happen in 0.14.x

Issue 2:
This appears to have been introduced in a new Chrome version, but I can't find any reference to where.

Affected/Tested Browsers (OS X):

Chrome 51.0.2704.106 (64-bit)

Opera 39.0.2256.15

Unaffected Browsers:

Safari 9.1

Firefox 49

Backspacing in an input element with value or defaultValue set causes some very odd behavior. Once a decimal point is gone, it can't easily be re-added.

Example:

In this example, I simply backspaced twice. On the second backspace, when I expect 3. to be showing, the input instead reads 3 and the cursor has moved to the beginning. The next two jumps are my attempts to add another decimal point.

Fiddle: https://jsfiddle.net/kmqz6kw8/

Tested with React 15.2.

Notes: This only occurs when value or defaultValue is set. If neither is set, the input behaves properly. We are currently working around this issue by (unfortunately) setting the input value on componentDidMount via a ref.
Issue 1:

Issue 2:
update etcd to 3.0.6, err still exists:

Checking for swagger spec that need updating... !!! Error in /home/zte/share/k8s-clone2/src/k8s.io/kubernetes/hack/lib/etcd.sh:35

'version=$(etcd --version | head -n 1 | cut -d " " -f 3)' exited with status 141

Call stack:

1: /home/zte/share/k8s-clone2/src/k8s.io/kubernetes/hack/lib/etcd.sh:35 kube::etcd::start(...)

2: /home/zte/share/k8s-clone2/src/k8s.io/kubernetes/hack/update-swagger-spec.sh:59 main(...)

Exiting with status 1

!!! Error in hack/verify-swagger-spec.sh:38

'"${KUBE_ROOT}/hack/update-swagger-spec.sh"' exited with status 1

Call stack:

1: hack/verify-swagger-spec.sh:38 main(...)

Exiting with status 1

ERROR!

Swagger spec needs to be updated.

To regenerate the spec, run:

hack/update-swagger-spec.sh

Aborting commit
Issue 1:
Hi, I built jax from source but when importing jax, I get this error:

/usr/local/lib/python3.8/dist-packages/jaxlib/cusolver.py in <module>
24 from jaxlib import cublas_kernels
25 for _name, _value in cublas_kernels.registrations().items():
---> 26 xla_client.register_custom_call_target(_name, _value, platform="CUDA")
27 except ImportError:
28 pass

/usr/local/lib/python3.8/dist-packages/jaxlib/xla_client.py in register_custom_call_target(name, fn, platform)
432 platform: the target platform.
433 """
--> 434 _xla.register_custom_call_target(name, fn, xla_platform_names[platform])
435
436

KeyError: 'CUDA'

After looking at the source code, it seems that line 26 and 34 in jaxlib/cusolver.py should be:

xla_client.register_custom_call_target(_name, _value, platform="gpu")

to match the xla_platform_names dict in xla_client. When fixed another error is raised:

File "/usr/local/lib/python3.8/dist-packages/jaxlib/cuda_prng.py", line 27, in <module>
xla_client.register_custom_call_target(_name, _value, platform="CUDA")
File "/usr/local/lib/python3.8/dist-packages/jaxlib/xla_client.py", line 434, in register_custom_call_target
_xla.register_custom_call_target(name, fn, xla_platform_names[platform])
KeyError: 'CUDA'

line 434 in jaxlib/xla_client.py should be:

xla_client.register_custom_call_target(_name, _value, platform="gpu")

Let me know if I'm right or if I missed something.

Issue 2:
Is there any way to create a jax.numpy.array on a specific device, instead of creating it on the default device then moving it via device_put? Also, is there any way to change the default device programmatically? Thanks in advance.
Issue 1:
Minor, but still:

type nn = number;
class A {
[s: nn]: number; // an error here, but it shouldn't be an error
}

Issue 2:
type Guid = string;
var foo: { [deviceId: Guid]: DateTime } = {};

Generates the error:

An index signature parameter type must be 'string' or 'number'.

It would be nice to be able to type indexes using string-aliases.
Issue 1:
Hi !

When the asset helper is used in a twig template to generate urls, it does not take into account the router.request_context.base_url parameter set in config.yml (or config_*.yml).

I've created a sample project (based on symfony/symfony-standard) to illustrate this.

The project defines a default base-url for all requests in config.yml.

Inside the project, i added a demonstration command mitch:generate-assets-list that ouputs 3 lists of urls.

a list generated with asset

a list generated using url

a list generated using path

The urls of list 2 & 3 all have the base-url part inside, the urls in list 1 do not.

The template looks like :

## assets urls

- {{ asset('bundles/app/img/logo.png') }}
- {{ asset('bundles/framework/images/logo_symfony.png') }}

## controller urls

- {{ url('homepage') }}
- {{ url('dummy_route') }}

## controller paths

- {{ path('homepage') }}
- {{ path('dummy_route') }}

The ouput generated by php bin/console mitch:generate-assets-list looks like :

## assets urls

- /bundles/app/img/logo.png
- /bundles/framework/images/logo_symfony.png

## controller urls

- http://localhost/subfolder/
- http://localhost/subfolder/dummy

## controller paths

- /subfolder/
- /subfolder/dummy

Thank you for your time,

Mitch.

Issue 2:
Symfony version(s) affected: 4.1.1

Description

When running in environment prod with display errors off, I'm seeing in my /var/log/nginx/error.log lots of out of memory exception. I'm having to resort to running in dev environment to see that the error (in this instance) is because a directory isn't writeable.

Why is an out of memory exception being reported in the logs, instead of something more sensible?

How to reproduce

Switch to prod mode, and try to write to a non-writeable directory, and you should see the out of memory exception error in the server log.
Issue 1:
java.lang.UnsatisfiedLinkError: dalvik.system.PathClassLoader[DexPathList[[zip file "/data/app/android.kindergartens.com-2/base.apk"],nativeLibraryDirectories=[/data/app/android.aa.com-2/lib/arm, /system/fake-libs, /data/app/android.aa.com-2/base.apk!/lib/armeabi-v7a, /system/lib, /vendor/lib]]] couldn't find "libflutter.so"

When I use abiFilters "armeabi-v7a", I found a mistake.

Issue 2:
When I want to debug the application in MacOs environment, it throw such error:

Multiple commands produce '/build/ios/Debug-iphonesimulator/Runner.app/Frameworks/Flutter.framework

Target 'Runner' has copy command from '/ios/Flutter/Flutter.framework' to '/build/ios/Debug-iphonesimulator/Runner.app/Frameworks/Flutter.framework'

That command depends on command in Target 'Runner': script phase “[CP] Embed Pods Frameworks”

warning: ignoring duplicated output file: '//build/ios/Debug-iphonesimulator/Runner.app/Frameworks/Flutter.framework' (in target 'Runner')

note: Using new build systemnote: Planning buildnote: Constructing build description

Mac 10.14 Beta

Xcode 10 beta

How to fix it? Thanks a lot
Issue 1:
numpy/numpy/__init__.pyi

Lines 1505 to 1506
in
4adc87d

@property

def __array_finalize__(self) -> None: ...

Currently, mypy does not allow users to define __array_finalize__, since the super class (ndarray) does not have an argument. So the following error is produced:

src/vector/_backends/numpy_.py:899: error: Signature of "__array_finalize__" incompatible with supertype "ndarray" [override]
src/vector/_backends/numpy_.py:899: note: Superclass:
src/vector/_backends/numpy_.py:899: note: def __array_finalize__(self) -> None
src/vector/_backends/numpy_.py:899: note: Subclass:
src/vector/_backends/numpy_.py:899: note: def __array_finalize__(self, obj: Any) -> None

This type stub should have a obj: Any argument.

Issue 2:
@melissawm pointed out that Broadcasting and Array Broadcasting in Numpy contain a lot of duplicate content. Array Broadcasting in Numpy is also an orphan doc(not in any TOC) and is referenced in only one other place (Tutorial: Linear algebra on n-dimensional arrays).

I think we can merge the unique parts of the orphan doc into the main Broadcasting doc. The plan would be to:

Add Vector quantization section and update internal links

Add SVGs in Broadcasting for examples and Vector quantization section (ref: Tracking Issue for Graphics)

Add corresponding captions/explanations and alt-text for each figure

Remove the corresponding images from the directory

Figures 1 and 5 from Array Broadcasting in Numpy can be recycled and converted to SVGs, I think (please let me know what you think @MarsBarLee).

Figures 2, 3, 4 seem to be wrong... So I think we can make new graphics for the corresponding examples.

@melissawm please let me know if you have any suggestions/corrections!
Issue 1:
TL;DR: I propose we create a simple guard around doubly-including React, which can cause issues if different blocks work with different instances of React.

I understand this invariant isn't going to matter to everyone, but I've at two points in so many weeks fixed a bug on a co-worker's project because they were accidentally including React twice in the browser: one from a Browserify bundle, and once from a standalone <script>.

Issue 2:
People lose hours of work debugging a simple issue: two versions of React being loaded at the same time.

gaearon/react-hot-loader#32 (comment)

KyleAMathews/coffee-react-quickstart#10 (comment)

gaearon/react-document-title#1 (comment)

clayallsopp/react.backbone#26

Because there is no warning right away when this happens, they usually discover the problem through invariant violations. Some of them are descriptive (e.g. Uncaught Error: Invariant Violation: The handler for Route "hello" must be a valid React component) and I think I even saw warning that said something like "check if two copies of React are loaded", but some are really cryptic: Invariant Violation: addComponentAsRefTo(...): Only a ReactOwner can have refs....

Is there a reason why we don't want to warn right away when two copies of React are loaded?