WhereIsAI/github-issue-similarity huggingface dataset:
Issue 1:
📝 Transparent background support on SVG explorer icons and previews

When viewing a SVG image with a transparent background in file explorer, file explorer should NOT fill in the transparent area of the icon with plain white. It should display just as the file explorer icon of a png image with a transparent background does.

If you'd like to see this feature implemented, add a 👍 reaction to this post.

Issue 2:
I've been using SVG Explorer Extension for several years to preview SVGs without opening them.

I've just discovered PowerToys because I received an email from Windows Insider Program and I noticed they have the same problem of this extension: Legibility on a white background.

As a Dark Theme user I solved the problem months ago, the gray background makes the light icons pop up and the blacks are not that bad.

It would be great to have a checked background (The classic white and grey used for alpha channel) option to activate from the settings panel, would be useful in PNGs too!

In my opinion previewing SVG without opening them should be a default in Windows, it's a popular format and i'm happy to see you're going in this direction!
Issue 1:
tf.nn.conv2d()'s behaviour on throwing the "data_format == FORMAT_NHWC" assertion seems odd. I cooked up a minimal example:

input_np = np.zeros([1,1,4,4], dtype=np.float32)
x = tf.constant(input_np, dtype=tf.float32)
filter = tf.ones([1,1,1,1], dtype=tf.float32)
out = tf.nn.conv2d(x, filter, [1,1,1,1], padding='SAME', data_format='NCHW')
sess= tf.Session()
sess.run(tf.global_variables_initializer())
sess.run(out)

Throws:

F tensorflow/core/kernels/conv_ops.cc:62] Check failed: data_format == FORMAT_NHWC
Generic conv implementation only supports NHWC tensor format for now.
Aborted (core dumped)

While this works fine, and it uses exactly the same variables, but now I am feeding a placeholder.

input_np = np.zeros([1,1,4,4], dtype=np.float32)
x = tf.placeholder("float", [None, 1,4,4])
filter = tf.ones([1,1,1,1], dtype=tf.float32)
out = tf.nn.conv2d(x, filter, [1,1,1,1], padding='SAME', data_format='NCHW')
sess= tf.Session()
sess.run(tf.global_variables_initializer())
sess.run(out, feed_dict={x:input_np})

Using 0.12.1, error is consistent across: OS-X as Ubuntu 14.04, regardless of CPU or GPU device placement.

Issue 2:
https://developer.nvidia.com/cuda-release-candidate-download
Issue 1:
🐛 Bug

"(base)_ C:\WINDOWS\system32>conda_ install pytorch torchvision cudatoolkit=10.0"

device="cuda:0"

`_#1`` pytorch1.2 subsequent_mask = torch.triu( torch.ones((len_s, len_s), device=seq.device, dtype=torch.uint8), diagonal=1)

KeyError: 'triu'

_#pytorch1.2 subsequent_mask = torch.triu( torch.ones((len_s, len_s), device=seq.device, dtype=torch.bool), diagonal=1)

RuntimeError: name not implemented for 'Bool'

RuntimeError: ONNX export failed: Could not export a broadcasted operation; ONNX likely does not support this form of broadcasting.

conda install pytoch=1.0.0 cuda100

_#pytorch1.0.0

D:\ProgramData\Anaconda3\lib\site-packages\torch\onnx\utils.py:501: UserWarning: ONNX export failed on ATen operator ne because torch.onnx.symbolic.ne does not exist

.format(op_name, op_name))

D:\ProgramData\Anaconda3\lib\site-packages\torch\onnx\utils.py:501: UserWarning: ONNX export failed on ATen operator triu because torch.onnx.symbolic.triu does not exist

.format(op_name, op_name))

To Reproduce

Steps to reproduce the behavior:

Expected behavior

Environment

Please copy and paste the output from our

environment collection script

(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):

OS (e.g., Linux):

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

Build command you used (if compiling from source):

Python version:

CUDA/cuDNN version:

GPU models and configuration:

Any other relevant information:

Additional context

Issue 2:
Models that contain torch.triu can not be converted to ONNX.

Error message:

UserWarning: ONNX export failed on ATen operator triu because torch.onnx.symbolic_opset9.triu does not exist

Simple reproducer is here.

import torch
import torch.nn as nn

class SimpleModel(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(10, 10, 3, padding=1, bias=False)

def forward(self, x):
x = self.conv(x)
mask = torch.triu(torch.ones_like(x))
x = x * mask
return x

model = SimpleModel()
var = torch.ones(1, 10, 120, 120)

torch.onnx.export(
model,
(var,),
"simple_model.onnx",
input_names=["x"],
output_names=["out"]
)

cc @houseroad @spandantiwari @lara-hdr @BowenBao @neginraoof
Issue 1:
I experienced weird response when it comes to FMA on Julia 1.4 .

First started Julia from the command line, without any flags (downloaded from julialang.org). Then I checked mulladd with code_native and find it's not using FMA CPU instructions and Base.Math.FMA_NATIVE also showed false... that was strange since as long as I've used julia this was always true and FMA always used native instruction.

Then I started Julia with -O3. Check thing again... muladd is correctly using native FMA, but Base.Math.FMA_NATIVE is false (makes sense since it's a precompile constant, but should be fixed regardless).

Then the strange behavior shows up. I then check julia again (without -O3 flag) and native FMA is working correctly in muladd (Base.Math.FMA_NATIVE still false).

Issue 2:
We currently check for support for FMA (fused multiply add) by doing

const FMA_NATIVE = muladd(nextfloat(1.0),nextfloat(1.0),-nextfloat(1.0,2)) != 0

From #33010 (comment) @yuyichao said:

This test is wrong. Llvm has all the freedom it want to return either values for muladd no matter if fma is available. Fwiw, arm / aarch 64 even have fast muladd instruction with intermediate rounding.
Issue 1:
I cannot find a way currently to check if the keyboard focus is within devtools of a window or not. My use case is that I want to treat some actions from the menu specially, when focus is in dev tools.

Issue 2:
Preflight Checklist

I have read the Contributing Guidelines for this project.

I agree to follow the Code of Conduct that this project adheres to.

I have searched the issue tracker for an issue that matches the one I want to file, without success.

Issue Details

Electron Version:

4.1.1

Operating System:

windows7

Last Known Working Electron version::

none

Expected Behavior

load flash plugin and no exception

Actual Behavior

To Reproduce

in win7

const flashPath = app.getPath('pepperFlashSystemPlugin'); // error
if (flashPath && ['dll', 'so', 'plugin'].forEach(item => flashPath.endsWith(item))) {
app.commandLine.appendSwitch('ppapi-flash-path', flashPath);
}

Screenshots

Additional Information

none
Issue 1:
I am trying to make aggregations on time series to get fixed-duration buckets of time.

I need to search based on timestamp (ts field) but never on values (value field) so my mapping specifies to skip indexing on value.

However, when I aggregate, it seesms that ES does not find the value field and use count zero instead.

To reproduce (marvel syntax):

define a mapping template as:

PUT _template/test
{
"template": "test",
"mappings": {
"metric" : {
"properties": {
"ts": { "type": "date"},
"value": { "type": "double", "index": "no"}
}
}
}
}

index one document:

POST test/metric
{
"ts": 4,
"value": 42
}

aggregate:

GET test/metric/_search?search_type=count
{
"aggs": {
"sum_indexed_field": { // non sense, only here to show aggregation work on indexed field
"sum": {
"field" : "ts"
}
},
"sum_nonindexed_field" : {
"sum" : {
"field": "value"
}
}
}
}

Ouput is rather disappointing:

{
"took": 240,
"timed_out": false,
"_shards": {
"total": 4,
"successful": 4,
"failed": 0
},
"hits": {
"total": 1,
"max_score": 0,
"hits": []
},
"aggregations": {
"sum_indexed_field": {
"value": 4 // ok
},
"sum_nonindexed_field": {
"value": 0 // should get 42 here
}
}
}

Am I doing something wrong?

Is this due to the way aggregations work?

Note 1 : using not_analyzed instead of no makes it work.

Note 2 : adding "doc_values": true makes it work as well.

But I'd like to understand.

Issue 2:
I am using _source field in my sort script to sort docs based on complex logic involving multi level nested fields. But accessing _source from disk for each matched document is performing poorly, giving me ~ 3 sec response times.

It would me very efficient to have a cache for this field, so that costly disk accesses can be avoided on every request. It would cost more memory, but would solve performance problem for many advanced sorting use cases.
Issue 1:
Hi. I apologize if this was asked; I could not find duplicate(s).

I basically have a hierarchical folder structure like a lot of people probably. The issue I'm having is that I'm getting errors like so: ERROR in multi components Module not found: Error: Cannot resolve 'file' or 'directory'. After delving deeper into the details it looks like webpack is looking for a charting.js or a charting/index.js kind of structure, but I basically want the folders to organize my files; not necessarily force what files should be in there as dictated by webpack.

|-- node_modules
|-- public
|-- components
|-- charting
|-- reporting
...

For example, another folder hierarchy is:

|-- public
|-- components
|-- routing
|-- views
|-- admin
|-- App.jsx

Webpack is requiring that I add an index.js or something like that to each nested folder, but that is something I want to avoid. Any ideas how I can improve this? Should I try and flatten? Thanks!

My config:

var webpack = require('webpack');
var path = require('path');
var glob = require('glob');

var config = require('./gulp/config.js');

var rawScriptsPath = path.resolve(__dirname + '/' + config.path.publicBase + '/scripts');

module.exports = {
context: rawScriptsPath,
entry: {
core: rawScriptsPath + '/core/application.js',
components: glob.sync(rawScriptsPath + '/components/**/!(sf\.)*'),
reporting: glob.sync(rawScriptsPath + '/reporting/**/!(\.clients)')
},
output: {
path: path.resolve(__dirname, 'public/dist/scripts'),
filename: '[name].js'
},
resolve: {
extensions: ['', '.js', '.jsx'],
modulesDirectories: [
'node_modules',
rawScriptsPath
],
root: rawScriptsPath
},
module: {
loaders: [
{
test: /\.js(x?)$/,
exclude: /node_modules/,
loader: 'babel',
query: {
stage: 0
// presets: ['es2015', 'stage-0', 'react']
}
},
{
test: /\.css$/,
exclude: /node_modules/,
loaders: [
'style',
'css'
// 'autoprefixer?browsers=last 3 versions',
// 'sass?outputStyle=expanded'
]
}
]
},
plugins: [
new webpack.NoErrorsPlugin(),
new webpack.IgnorePlugin(/\.svn$/)
]
};

Issue 2:
Feature request

What is the expected behavior?

Loading chunks with import()

What is motivation or use case for adding/changing the behavior?

Imports are much cleaner than jsonp and in the future more and more browsers and platforms are going to support it, making a unified approach

How should this be implemented in your opinion?

Just use import() to load the chunk

Are you willing to work on this yourself?

Depending on how easy it is and with some help
Issue 1:
when i close atom whitin working. and then reopen atom, i need atom can reopen my last workspace, but now, just opened a empty file....

Issue 2:
A feature I love about ST2 and iA Writer, which I'm missing in Atom, is when you quit the app with open windows (including unsaved documents), they all appear again when you reopen.

This has saved me countless times in my workflow to handle having to do a system restart for software update, etc and not have to take time to save all my unsaved docs, etc.

It's also nice because I generally have five or six open projects that I just keep minimized in the editor for ease of access. It would be great to have such a feature in Atom.
Issue 1:
Migrated issue, originally created by Anonymous

When creating a FK object, the column argument can be passed in as class.column, a string, or class.table.c.column. However, if the column the FK is created on is a primary key, using class.column raises an attribute error:

AttributeError: Neither 'InstrumentedAttribute' object nor 'Comparator' object has an attribute 'table'

The attached file shows a working FK using class.column (third class), and a defective fourth class that attempts to create a FK on a column that is also a PK using class.column. In the example, the PK is a composite of two columns, however this is not the issue, the code was tested and raised the same error without the second column.

In the example, changing class.column for one of the other options listed above resolved the problem. This is not a serious defect as the other mechanisms work fine, but if this is a relatively easy fix it is worth doing as it provides more elegant syntax which is already working for the most part.

Attachments: test.py

Issue 2:
Describe your question

I intend to insert a lot of rows with unique hash values, and want to skip duplicated records. Using "OR IGNORE" with sqlite3 works fine, but when using "ON CONFLICT DO NOTHING" with pgsql, the session.add passed, but failed at flush, showing flush error like below

sqlalchemy.orm.exc.FlushError: Instance <tracklets at 0x195ec22b580> has a NULL identity key. If this is an auto-generated value, check that the database table allows generation of new primary key values, and that the mapped Column object is configured to expect these generated values. Ensure also that this flush table allows generation of new primary key values, and that the mapped Column object is configured to expect these generated values. Ensure also that this flush() is not occurring at an inappropriate time, such as within a load() event.

What should I do and is there a universal method for cross platform insertion with ignore errors, such as an argument of add.

Versions

OS: Windows 10 x64

Python: 3.8.3

SQLAlchemy: 1.3.20

Database: Postgresql 12

DBAPI: psycopg 2.8.6

Have a nice day!
Issue 1:
This Plunker

http://plnkr.co/edit/vTsxIOfepOd7BCZxueQt

In previous Angular 2 versions I could use something like this in my template

<div style="background-image: url({{background}});"></div>

This broke in RC1. If I put the double curly in the id attribute (for instance), it does work. So it might has something to do with the style attribute.

Upon investigating further, I found that I could (should?) be writing this as:

<div [style.backgroundImage]="'url('+background+')'"></div>

This does not work.

Even used with a string, this does not work:

<div [style.backgroundImage]="'url(data:image/gif;base64,R0lGODlhAQABAIAAAAUEBAAAACwAAAAAAQABAAACAkQBADs=)'"></div>

Trying the same thing on style.backgoundColor with a string 'red' as value for instance does work! So I'm not sure what's going on here.

Issue 2:
Steps to reproduce and a minimal demo of the problem

Both of the plunks below will work if you go to config.js and switch var ngVer = '@2.0.0-rc.1'; to var ngVer = '@2.0.0-rc.0';

Plunk showing [style.background-image] failing:

http://plnkr.co/edit/rRKupFRs2i4YoAU7Ayi1?p=preview

Plunk showing [style.transform] failing

http://plnkr.co/edit/fP12KXNfL8TxXmuydxCx?p=preview

Current behavior

Some style bindings like background-image and transform are not working in RC.1 however. the style binding for opacity does work.

Expected/desired behavior

For the style bindings to reflect the property given to them

Other information

We've tested only a small set of the selectors, there may be more that do and do not work.

All of the bindings work in RC0
Issue 1:
Users are confused by this:

if not index._verify_integrity():
dups = index.get_duplicates()
err_msg = 'Tried columns 1-X as index but found duplicates %s'
raise Exception(err_msg % str(dups))

this was added as I recall to give a somewhat better error message when INFERENCE (fewer column labels than columns of data) but this error message is also getting raised when there are duplicates in an explicitly specified index_col

see: http://stackoverflow.com/questions/9556892/pandas-dataframe-desired-index-has-duplicate-values

Issue 2:
tslib.get_value_box seems out of place. The only place it is used is in base.index. Meanwhile _libs.index.get_value_at looks very similar but is never used. (It also looks similar to a chunk of code in IndexEngine.get_value

Is it possible that index.get_value_at was intended to replace tslib.get_value_box?
Issue 1:
TL;DR: The versioned API types should be self-contained package. For example, the file "pkg/api/v1beta3/types.go" is not self contained and contains tendrils to other internal types (see below). This is somewhat cruddy for other clients wanting to import minimal source from k8s just to call its APIs. It'd be nice to have a single package to import to get the API object definitions (i.e. for json unmarshalling, etc).

Observed outside of package depencies in "pkg/api/v1beta3/types.go":

"pkg/runtime" - The "List" type contains a field "Items []runtime.RawExtension". "RawExtension" is a trivial struct containing only a "[]byte".

"pkg/types" - Various places uses the "types.UID" struct, which is an alias to "string".

"pkg/util" - Two types from this package are used: "util.Time" and "util.IntOrString". The former is a wrapper on Go's Time object, with extra functions for custom JSON serialization (RFC3339 compliant) in "pkg/util/time.go". "IntOrString" is the dual-mode struct holding an int value or a string ("pkg/util/util.go").

"pkg/api/resource" - The "ResourceList" object is a map whose keys are "resource.Quantity" objects. The "Quantity" object contains a field to "inf.Dec", which is an infinite precision decimal package imported from "speter.net/go/exp/math/dec/inf". This is the only package which is external to k8s.

I know that some of the json annotations added are to support swagger, but the go->swagger->go thing seems like an unnecessary complexity.

Issue 2:
Hi all

The logs on the gci master 1.3 beta image are not being rolled. Just lost my master because /var/log was filled up :( Are we using logrotate? Because it is not configured.

The cluster is a multi az cluster running on gce, using the default master. Cluster was created using kube-up.sh.

Master version info.

clove@kubernetes-master /etc $ cat /proc/version
Linux version 4.4.4+ (chrome-bot@build297-m2) (gcc version 4.9.x 20150123 (prerelease) (4.9.2_cos_gg_a2bc87a_4.9.2-r128) ) #1 SMP Fri Jun 17 14:27:45 PDT 2016

K8s Version info:

Client Version: version.Info{Major:"1", Minor:"3+", GitVersion:"v1.3.0-beta.1", GitCommit:"1c7855093eb999c8f3cedb19d3467cd7b691e35e", GitTreeState:"clean", BuildDate:"2016-06-17T00:58:51Z", GoVersion:"go1.6.2", Compiler:"gc", Platform:"linux/amd64"}
Server Version: version.Info{Major:"1", Minor:"3+", GitVersion:"v1.3.0-beta.1", GitCommit:"1c7855093eb999c8f3cedb19d3467cd7b691e35e", GitTreeState:"clean", BuildDate:"2016-06-17T00:53:24Z", GoVersion:"go1.6.2", Compiler:"gc", Platform:"linux/amd64"}

cc: @bgrant0607 @andyzheng0831 @kubernetes/goog-image
Issue 1:
System info

Playwright Version: [v1.34]

Programming Language: Java

Operating System: Mac os 12.2

Browser: Chromium 114.0.5735.133 (Official Build) (arm64)

Other info:

Steps

Launch the Chromium instance through playwright

Launch any site(google.com) or open Developer Tools

Expected

Launched Site or Developer Tools should open quickly as like as opening in normal chrome browser

Actual

Compared to normal chrome instance. Page load time is 5X slower in playwright chromium instance and also developer tools takes so much time to load.

Note: We are facing this issue only in MAC. Working fine in Windows and LINUX

Steps Already checked

I have deleted the old ms.playwright folder from caches folder and updated the playwright version to 1.34. With the new driver also facing the same slowness

Issue 2:
System info

Playwright Version: [1.31.2]

Operating System: [Windows 11]

Browser: [Chromium, Firefox,]

node.js versions [16.14.2, 18.11.0, 19.7.0]

Source code

I can't share source code. It is an subject of confidantial.

My tests runs until playwright version 1.28.2. In version 1.29.1 I hat to set the viewport in configuration to null. In version 1.31.2 this is not possible.

this.context=await this.browser.newContext({

viewport:null

})

With webkit my tests run in all playwright versions. In chromium und firefox they dosn't run headed correctly.

In attached files you can see the difference between webkit and chromium. It contains config-files for webkit and chromium, a video for webkit and chromium and a screenshot for chromium.

BugReport.zip
Issue 1:
Currently, Deno only sorts the imported and exported names from import and export specifiers. The goal is to also sort the import and export specifiers in a certain sort function.

Usually, I sort those specifiers in Node in this way,

import 'style.css' // might be side effects module if JS
import fs from 'fs' // default import
import * as path from 'path' // namespace import
import { ref } from 'vue' // normal named import

// typed imports
import type TypeC from 'abc'
import type * as TypeC from 'abc'
import type { TypeC } from 'abc'

Exports are also sorted in that way as well.

For Deno, I am thinking to sort like,

import 'style.css' // might be side effects module if JS
import fs from 'fs' // default import
import * as path from 'path' // namespace import
import { ref } from 'vue' // normal named import

// url imports go into one section
import fs from 'https://deno.land/std/node/fs.ts'
/// ... the rest of imports

// typed imports
import type TypeC from 'abc'
import type * as TypeC from 'abc'
import type { TypeC } from 'abc'

// type imports from url here
import type { Colors } from 'https://deno.land/std/colors.ts'

I have checked that dprint can sort the import and export specifiers. But, Deno hasn't enabled them. I am not familiar with the code base, so I will let the core team decide the preferred way of sorting.

Some unresolved questions are how should deno fmt handle side effect imports? Should they be left as it is and let the users sort (since they will become one section after other imports are sorted)?

Thanks!

Issue 2:
import { x, y } from "./alphabet.ts"
import { foo, bar } from "std/foo/bar.ts"
import { z } from "./blah.ts"

imo would look better as

import { x, y } from "./alphabet.ts"
import { z } from "./blah.ts"
import { foo, bar } from "std/foo/bar.ts"