WhereIsAI/github-issue-similarity huggingface dataset:
Issue 1:
Feature request

A related conversation to this feature happened here #9524 (comment)

if i use a headless CMS (even wordpress or graphcms or whatever), the asset url is used in the static html.

There are two preferences here - that asset links be used as such.

But more likely - download the asset, build the html (link to it locally) and then layer a CDN in front. This is the far more acceptable practice.

This also ties in very well with using deployment systems like Netlify - which have a far, far more suitable globally available infrastructure than something like DatoCMS or Graphcms. So if im using Netlify as deployment, i would want everything to be served from Netlify domain and let it work its magic.

We understand that this increases build times. This is ok for our usecase where we use a CMS just for the sake of usability, but deployment has to follow guidelines of compliance (geographic restriction of assets, etc) so we would like the entire build to be inside the export folder. This would mean that CMS that have CDN in front of it (e.g. datocms and www.datocms-assets.com which is Imgix) may not be ideal.

There are tons of similar requests.

https://spectrum.chat/next-js/general/how-would-you-handle-importing-remote-images-on-nextjs-static-export~30b2ba84-bc27-4da7-9ec8-21e4d5d287a3

on gatsby side as well - gatsbyjs/gatsby#14076

https://spectrum.chat/gatsby-js/general/adding-remote-images-during-node-creation~e704e6fb-24b2-46c6-b1fc-93189d2e28a4

https://github.com/njosefbeck/gatsby-source-stripe/#downloading-files

Issue 2:
I found this wiki page and have the following code to redirect in getInitialProps:

static async getInitialProps({res}) {
res.writeHead(301, {
Location: 'http://example.com'
})
res.end()
res.finished = true
return {}
}

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

Expected Behavior

The page should redirect to http://example.com before rendering.

Current Behavior

When I browse to the URL (the page is at http://localhost:3000/dashboard), the request just hangs. Here's what it looks like in the Chrome dev tools:

screenshot.

Context

I swear this was working before and I have no clue what changed. I'm going to try playing with different versions of Express. Thanks for your help!

Your Environment

Tech
Version

next
3.0.6

node
8.1.1

OS
Mac Sierra (10.12.5)

browser
Chrome

express
4.15.3
Issue 1:
As suggested in #6254, creating an issue to support recording sent and received WebSocket frames in the HAR.

Issue 2:
Context:

Playwright Version: 1.25.2

Operating System: linux

Node.js version: using python (pytest-playwright)

Browser: chrome

Extra: [any specific details about your environment]

System:

OS: Linux 5.19 Ubuntu 22.04.1 LTS 22.04.1 LTS (Jammy Jellyfish)

Memory: 51.81 GB / 62.53 GB

Container: Yes

Binaries:

Node: 16.14.0 - ~/.nvm/versions/node/v16.14.0/bin/node

Yarn: 1.22.17 - ~/.nvm/versions/node/v16.14.0/bin/yarn

npm: 8.3.1 - ~/.nvm/versions/node/v16.14.0/bin/npm

Languages:

Bash: 5.1.16 - /usr/bin/bash

Code Snippet

Help us help you! Put down a short code snippet that illustrates your bug and

that we can run and debug locally. For example:

str_start_time = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
har_path = f"{request_hars}/{str_start_time}_{request.node.originalname}.har"
page = await browser.new_page(
no_viewport=True,
record_video_dir=f"./{videos}",
record_video_size={"width": 1920, "height": 1080},
record_har_path=har_path,
record_har_mode="full",
)

Describe the bug

I turned on har recording with record_har_path and record_har_mode = full . I still cant see any wesocket request's inside my har. Where as if I save the har using chrome dev tools I see websocket requests in har
Issue 1:
await does not works on method of super class

class Animal {
constructor() {
}

async walk() {
console.log('animal walk')
}
}

class Dog extends Animal {
constructor() {
super();
}

async walk() {
await super.walk();
console.log('dog walk');
}
}

I suppose this piece of code should work, but It doesn't, as method walk of Dog will be transformed to

class Dog extends Animal {
...

walk() {
return __awaiter(this, void 0, Promise, function* () {
yield super.walk();
console.log('dog walk');
});
}
}

but the specification says keyword 'super' is only permitted in a constructor, method call and member accessor in derived class.

Issue 2:
Given the TypeScript:

class B extends A {
async method() {
super.superMethod();
}
}

We emit the following JavaScript:

var __awaiter = ...;
class B extends A {
method() {
return __awaiter(this, void 0, Promise, function* () {
super.superMethod(); // Error
});
}
}

However, super is not bound inside the generator function.

We may instead want to consider changing the emit to the following:

var __awaiter = ...;
class B extends A {
method() {
return __awaiter(this, void 0, Promise, B.prototype._method_async);
}
*_method_async() {
super.superMethod(); // No error
}
}

In this case the super binding in _method_async works as expected. Also, we pass B.prototype._method_async to __awaiter here, rather than this._method_async so that we will won't need to worry about collisions. Note, though, that we will need to take class expressions into consideration here, as they may not always have a reachable name.
Issue 1:
a = torch.rand(3, 3)

a.norm(dim = 1)
#TypeError: norm received an invalid combination of arguments - got (dim=int, ), but expected one of:
# * no arguments
# * (float p)
# didn't match because some of the keywords were incorrect: dim
# * (float p, int dim)
# * (float p, int dim, bool keepdim)

torch.norm(a, dim = 1)
#TypeError: torch.norm received an invalid combination of arguments - got (torch.FloatTensor, dim=int), but expected one of:
# * (torch.FloatTensor source)
# * (torch.FloatTensor source, float p)
# didn't match because some of the keywords were incorrect: dim
# * (torch.FloatTensor source, float p, int dim)
# * (torch.FloatTensor source, float p, int dim, bool keepdim)

torch.norm(a, p = 2, dim = 1) # works
a.norm(p = 2, dim = 1) # works
a.norm() # works

Issue 2:
torch.norm only accepts p, dim, giving only dim results in error

In [3]: torch.autograd.Variable(torch.randn(4,8)).norm(dim=1)
Out[3]:
Variable containing:
2.1210
3.0991
2.4054
2.1048
[torch.FloatTensor of size 4x1]

In [4]: torch.randn(4,8).norm(dim=1)

TypeError: norm received an invalid combination of arguments - got (dim=int, ), but expected one of:
* no arguments
* (float p)
didn't match because some of the keywords were incorrect: dim
* (float p, int dim)

In [8]: torch.randn(4,8).norm(p=2, dim=1)
Out[8]:

1.9636
2.1119
2.8933
4.0552
[torch.FloatTensor of size 4x1]
Issue 1:
I was doing a challenge and it keep saying I did not finish the challenge but I made no typos or misuse of code. I even asked it in the chat and even they said there was nothing wrong with it so I think there is a bug or something.

Challenge Name

https://www.freecodecamp.com/challenges/use-a-css-class-to-style-an-element

Issue Description

Browser Information

Browser Name, Version: Chrome (version 54.0.2840.85)

Operating System: android (CyanogenMod 12.1.1)

Mobile, Desktop, or Tablet: mobile (OnePlus One)

Your Code

<style>
.red-text {
color: red;
}
</style>

<h2 class="red-text">CatPhotoApp</h2>

<p>Kitty ipsum dolor sit amet, shed everywhere shed everywhere stretching attack your ankles chase the red dot, hairball run catnip eat the grass sniff.</p>

Screenshot

Issue 2:
Challenge Use a CSS Class to Style an Element has an issue.

User Agent is: Mozilla/5.0 (Linux; Android 4.4.2; SM-T231 Build/KOT49H) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/49.0.2623.105 Safari/537.36.

Please describe how to reproduce this issue, and include links to screenshots if possible.

My code:

<style>
.red-text {
color: red;
}
</style>

<h2 class="red-text">CatPhotoApp</h2>

<p>Kitty ipsum dolor sit amet, shed everywhere shed everywhere stretching attack your ankles chase the red dot, hairball run catnip eat the grass sniff.</p>
Issue 1:
The default placement of the Tool tip <div /> next to the titled element is causing .btn elements in a .btn-group to lose some styling. Specifically the last button loses it's border-radius and the middle one appears to either lose some negative margin or grows an extra border-right.

This is the mark-up in question

<div class="btn-group">
<a class="btn" href="/foo"><i class="icon-globe"></i> <span class="hidden-phone">View</span></a>
<a class="btn" href="/bar" title="Edit"><i class="icon-edit"></i><span class="hidden-phone">Edit</span></a>
<button class="btn" type="submit" title="Delete"><i class="icon-remove"></i> <span class="hidden-phone">Delete</span></button>
</div>

There is a workaround in specifying the container option in the .tooltip() function.

$('[title]').tooltip({ container : 'body' });

Issue 2:
http://jsfiddle.net/JEBdk/5/

tooltip or popover on btn-group. the btn-group is not display correctly!

the last btn have not round corners and between the btn's is a space.

my suggestion, by generating the tip or popover on the end of document .
Issue 1:
The CSS snippets always show up in the auto-complete suggestions. This isn't ideal for CSS when completing values as they insert a bunch of properties, which isn't valid in that context. This isn't terrible as they're hidden at the end of the list, but in the latest code, they are included randomly, with one of them mostly (always?) showing first in the list:

See gradient, border and transition dispersed through the list, with border as the first completion

They should be last like the old behaviour, and ideally not show up for value completions in CSS (although perhaps there could be snippets that are valid to be inserted in that position?)

Ideally there would be some kind of weighting or ordering to the list of initial completions, as they seem random currently. They're not in defined order in the source file or alphabetical. In an ideal world, I’d like the CSS wide keywords to be kept together and listed last and property specific features first (they're added that way when generating the file)

Issue 2:
In 0.10.1 we sorted snippets at end. Until we have better mechanisms to know at what locations a snippet makes sense, we should not present them along with the other proposals

FYI @egamma
Issue 1:
Version

2.6.10

Reproduction link

https://codepen.io/avertes/pen/LYYpNRe

Steps to reproduce

Insert <input value="A &nbsp; B" /> inside Vue.js App html

What is expected?

Value "A B" is shown

What is actually happening?

Value "A &nbsp; B" is shown

Other escaped characters, for example > ( &gt; ) works as expected, but issue arises with any version of non-breakable space.

Issue 2:
Version

2.6.10

Reproduction link

https://codesandbox.io/embed/vue-template-ed9kk

Steps to reproduce

click button

What is expected?

Text "red" should be in red.

What is actually happening?

Text "red" is black.

class="foo" is needed to make the OLD and NEW vnode the same (in sameVnode), so that Vue will try to patch instead of creating a new element.
Issue 1:
By accident I came across an ICE from this code: (-1.0 as usize,). The ICE is triggered by an assertion while building the constant tuple. The field has a value of undef.

Looking into it, the two floating point to integer cast instructions, fptoui and fptosi have a very large surface for producing undefined values. Any time the result cannot fit into the destination type, the result is undefined. This means casting from a negative float to an unsigned integer is undefined and casting from a float with a value outside the range for the target integer type (signed or otherwise) is undefined.

Some possible solutions:

Treat it as an error always. This means generating an assertion before the cast, similar to how we generate assertions to check for divide-by-zero.

Clamp the value. Casting from outside the valid range will clamp to one end of the range. so -1.0 as u32 would produce 0.

A combination of both. Treat the case similarly to how we treat integer overflow and emit an assertion in debug builds, but omit them in release builds, falling back to the clamping behaviour.

I'm personally leaning towards 3. Most of the time an invalid cast of this nature will be unintentional, so protecting against it seems reasonable. The problem is also similar enough to overflowing that handling it similarly seems reasonable.

/cc @rust-lang/compiler @rust-lang/lang

Issue 2:
Status as of 2020-04-18

We intend to stabilize the saturating-float-casts behavior for as, and have stabilized unsafe library functions that handle the previous behavior. See #71269 for the latest discussion on that stabilization process.

Status as of 2018-11-05

A flag has been implemented in the compiler, -Zsaturating-float-casts, which will cause all float to integer casts have "saturating" behavior where if it's out of bounds it's clamped to the nearest bound. A call for benchmarking of this change went out awhile ago. Results, while positive in many projects, are quite negative for some projects and indicates that we're not done here.

The next steps are figuring out how to recover performance for these cases:

One option is to take today's as cast behavior (which is UB in some cases) and add unsafe functions for the relevant types and such.

Another is to wait for LLVM to add a freeze concept which means that we get a garbage bit pattern, but it's at least not UB

Another is to implement casts via inline assembly in LLVM IR, as the current codegen is not heavily optimized.

Old status

UPDATE (by @nikomatsakis): After much discussion, we've got the rudiments of a plan for how to address this problem. But we need some help with actually investigating the performance impact and working out the final details!

ORIGINAL ISSUE FOLLOWS:

If the value cannot fit in ty2, the results are undefined.

1.04E+17 as u8
Issue 1:
[x] Request

Request to FIX OpenCv 2.4.13.6 cmake source to build OpenCv_World24136.DLL

minimum build x86 vc12,

because I can only build opencv_world with 3.x in vc14,vc15

Issue 2:
System information (version)

OpenCV => 3.4.3 and 4.0.1

Operating System / Platform =>Snapdragon 845

Compiler => Android NDK 18.1.5063045

Detailed description

I have a face recognition algorithm running on snapdragon 845 platform. This code was compiled by using the official release OpenCV 3.4.3 Android package (see here https://sourceforge.net/projects/opencvlibrary/files/opencv-android/3.4.3/opencv-3.4.3-android-sdk.zip/download). It takes around 500ms to recognize a face.

I don't specify any PreferableTarget and PreferableBackend then I guess by default, the code runs on CPU of snapdragon 845.

Today, I upgrade to OpenCV 4.0.1 (the official Android package can be found here https://sourceforge.net/projects/opencvlibrary/files/4.0.1/opencv-4.0.1-android-sdk.zip/download). I don't change anything in my Android studio. In low level of my C++ code, I have to change only an enum from CV_YUV2RGB_NV21 to cv::COLOR_YUV2RGB_NV21. (I think this change cannot be the cause of the problem). Now, it takes around 650ms to recognize a face.

Bref: the same Android studio configuration, the same NDK, 99% the same code, the same tested platform but there is a huge regression in terms of processing time between OpenCV 3.4.3 and OpenCV 4.0.1 !!

The network that I use to do face recognition is ArcFace (see here https://github.com/deepinsight/insightface). The authors provide a trained network under Caffe form.

Does anyone observe similar regression ?

Thanks
Issue 1:
Here is my code:

with tf.Session() as sess:
# with tf.device("/cpu:0"):
x = tf.ones(shape=[3, 3])
x_diag = tf.diag_part(x)
x_diag_matrix = tf.matrix_diag(x_diag)
print(sess.run(x_diag_matrix))

It works ok on a CPU but fails in a GPU mode with the following 'CUDA_ERROR_ILLEGAL_ADDRESS' error:

I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library cublas64_80.dll locally

I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library cudnn64_5.dll locally

I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library cufft64_80.dll locally

I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library nvcuda.dll locally

I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library curand64_80.dll locally

I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:885] Found device 0 with properties:

name: Tesla K40m

major: 3 minor: 5 memoryClockRate (GHz) 0.745

pciBusID 0000:27:00.0

Total memory: 11.16GiB

Free memory: 11.09GiB

I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:906] DMA: 0

I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:916] 0: Y

I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K40m, pci bus id: 0000:27:00.0)

E c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_device.cc:586] Could not identify NUMA node of /job:localhost/replica:0/task:0/gpu:0, defaulting to 0. Your kernel may not have been built with NUMA support.

E c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\cuda\cuda_event.cc:49] Error polling for event status: failed to query event: CUDA_ERROR_ILLEGAL_ADDRESS

F c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core\common_runtime\gpu\gpu_event_mgr.cc:198] Unexpected Event status: 1

I have tried

x = tf.ones(shape=[3, 3])
x_diag = tf.diag_part(x)

and x_diag_matrix = tf.matrix_diag([1., 1., 1.]) , Both work ok in a GPU mode. Maybe the tensor couldn't be input of tf.matrix_diag() in a Windows GPU mode?

Issue 2:
Currently, sparse tensors don't support indexing, and the tf.sparse_split() operation can't split a sparse tensor according to a given proportion. If I want to select a portion of the training set (which is a sparse tensor) as the validation set, is it possible?
Issue 1:
After specifying @link-color-hover in customization page the generated css still uses default 'darken' function instead of specified hover color.

Issue 2:
http://getbootstrap.com/customize/#variables-basics

Customize >> Colors >> @link-color-hover is not working properly.

Either it's not working properly when compiling .css

or

#10172 #issuecomment-23293444

it's been dropped in v.3 ( hover is only generated as darken(@link-color, 15%) ).

Then @link-color-hover should be removed from customizer form.
Issue 1:
sometimes vim create a file with ~ , as example something~, how to hide this kind of file name in atom ? i tried use in settings>Ignored Names and add *~ but it still showing in tree view

Issue 2:
I just posted in discuss.atom.io explaining in depth an ignoredNames inconsistency issue between tree-view, fuzzy-finder and find-and-replace

https://discuss.atom.io/t/ignorednames-issue/13299

In summary, minimatch style ignoredNames don't work in tree-view.

'global':
'core':
'ignoredNames': [
'**/project/static/**'
]

I think the best solution for this, is to move isPathIgnored to the API, so that any plugin can use the same method instead of re-implementing it.
Issue 1:
Migrated issue, originally created by i (@immerrr)

It is probably unexpected that it actually works, but MySQL can do that (and StackOverflow suggests that MS SQL can, too). Here's a script to reproduce:

import sqlalchemy as sa

metadata = sa.MetaData()

t1 = sa.Table(
't1', metadata,
sa.Column('key1', sa.Integer),
sa.Column('ref1', sa.Integer),
sa.Column('val1', sa.Integer),
sa.ForeignKeyConstraint(['ref1'], ['t2.key2']),
)

t2 = sa.Table(
't2', metadata,
sa.Column('key2', sa.Integer),
sa.Column('val2', sa.Integer),
)

engine = sa.create_engine('mysql+mysqlconnector://')

def to_str(stmt):
return str(stmt.compile(bind=engine,
compile_kwargs={'literal_binds': True}))

In the end I see

>>> to_str(
... sa.update(t1.join(t2),
... values={t1.c.val1: 'foo'},
... whereclause=(t2.c.val2 == 'foobar')))
...
'UPDATE t1 INNER JOIN t2 ON t2.key2 = t1.ref1, t2 SET t1.val1=%(val1)s WHERE t2.val2 = %(val2_1)s'

Note, that there's a second t2 table in the table_references part of the query.

This patch seems to have fixed my case, but I can't seem to run tests out of the box, so I don't know if it breaks* anything:

index 7b506f9..fe9f20b 100644
--- a/lib/sqlalchemy/sql/dml.py
+++ b/lib/sqlalchemy/sql/dml.py
@@ -766,7 +766,7 @@ class Update(ValuesBase):
# TODO: this could be made memoized
# if the memoization is reset on each generative call.
froms = []
- seen = set([self.table])
+ seen = set(_from_objects(self.table))

if self._whereclause is not None:
for item in _from_objects(self._whereclause):

Issue 2:
Migrated issue, originally created by Michael Bayer (@zzzeek)

this works in general, but fails on at least the Postgresql dialect because it is linking each expression to a column. See https://bitbucket.org/zzzeek/alembic/issue/222/support-functional-indexes-with for how alembic works around this.

from sqlalchemy import *
from sqlalchemy.schema import CreateIndex
from sqlalchemy.dialects import postgresql

m = MetaData()
t = Table('x', m)
idx = Index('foo', text("lower(c)"))
idx._set_parent(t)

print CreateIndex(idx).compile(dialect=postgresql.dialect())

output:

CREATE INDEX foo ON x ()

Index should be a lot more forgiving for indexes that have non-column expressions in it, and we should also provide for a "table" keyword.
Issue 1:
https://k8s-gubernator.appspot.com/build/kubernetes-jenkins/logs/kubernetes-e2e-gce-federation/4908/

Failed: Up {e2e.go}

error running up: exit status 1

Previous issues for this test: #33357 #33377

Issue 2:
https://k8s-gubernator.appspot.com/build/kubernetes-jenkins/logs/kubernetes-e2e-gce-federation/4526/

Failed: Up {e2e.go}

error running up: exit status 1

Previous issues for this test: #33357 #33377
Issue 1:
When the navbar is collapsed, clicking a child dropdown does not extend the height of the window; therefore, users are unable to see what is within the menu. Example: http://livingwithhm.com.

We're running v.2.2.2 on WordPress

Issue 2:
The default navbar (http://getbootstrap.com/components/#navbar) act a little bit weird when : I click dropdown (keep it opened) and then resize the browser until the navbar-toggle button appear, click it, open the dropdown and then maximize the browser. after those step, I can't open the dropdown on navbar default state..
Issue 1:
I would like to move/snap windows into fancyzones with a user-specified shorcut like Ctrl + Alt + 1 for zone 1 and Ctrl + Alt + 2 for zone 2 as addition to existing mouse drag+ shift combination.

I've never find useful windows' default key combination windows + arrow buttons. I would like to arrange windows with one hand not two.

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

Issue 2:
I'm not sure whether this should be reported as a bug or a feature that is just not implemented, but here it goes:

I have multiple virtual desktops, with each a different fancyzones layout. After rebooting Windows, however, all the virtual desktops have the same fancyzones layout again.

It would be great if this can be implemented/fixed. :)