WhereIsAI/github-issue-similarity huggingface dataset:
Issue 1:
http://kubekins.dls.corp.google.com/view/Critical%20Builds/job/kubernetes-e2e-gce-parallel/7142/

/go/src/k8s.io/kubernetes/_output/dockerized/go/src/k8s.io/kubernetes/test/e2e/service.go:278
Expected error:
<*errors.errorString | 0xc20802bf70>: {
s: "timed out waiting for the condition",
}
timed out waiting for the condition
not to have occurred

Issue 2:
"Services" e2e test seems to be broken (flaky) on head. See:

http://kubekins.dls.corp.google.com/view/Critical%20Builds/job/kubernetes-e2e-gce/8170/console

Summarizing 3 Failures:

[Fail] Services [It] should be able to create a functioning external load balancer

/go/src/k8s.io/kubernetes/_output/dockerized/go/src/k8s.io/kubernetes/test/e2e/service.go:1238

[Fail] Services [It] should be able to change the type and nodeport settings of a service

/go/src/k8s.io/kubernetes/_output/dockerized/go/src/k8s.io/kubernetes/test/e2e/service.go:1238

[Fail] Services [It] should release the load balancer when Type goes from LoadBalancer -> NodePort

/go/src/k8s.io/kubernetes/_output/dockerized/go/src/k8s.io/kubernetes/test/e2e/service.go:1238
Issue 1:
Environment

Windows build number: 18362.175
Windows Terminal version (if applicable): 0.2.1715.0

Steps to reproduce

Change settings an put Cascadia Code or another missing font

Expected behavior

If the font is missing or wrong, keep Consoles by default, but don't crash

Maybe a hint or a message error, but don't crash

Actual behavior

Crash and don't start until fix change the font

Issue 2:
I added a new profile to the profiles.json specifying a font that is not installed on my system. When attempting to start the profile the app crashes with the following:

The program '[15980] WindowsTerminal.exe' has exited with code -1073741819 (0xc0000005) 'Access violation'.

Specifying a font that is installed causes the problem to not occur.

The profile definition looks like this:

{
"guid": "{e2f9d7d6-369b-4841-9429-61f5b38474cf}",
"name": "Bash",
"colorscheme": "Campbell",
"historySize": 9001,
"snapOnInput": true,
"cursorColor": "#FFFFFF",
"cursorShape": "bar",
"commandline": "bash.exe",
"fontFace": "Fira Code",
"fontSize": 12,
"acrylicOpacity": 0.5,
"useAcrylic": false,
"closeOnExit": true,
"padding": "0, 0, 0, 0"
}
Issue 1:
Hello everyone,

I got the code from tensorflow website for this tutorial. It processed for a long while and got the following errors. Installing tensorflow from sources also did not work for me as it gave me many errors. so I deleted everything installed tensorflow on my Ubuntu 16.04 LTS. Please please help me in resolving these errors as it is an emergency. I am a beginner to tensorflow and its been 2 months already I have been seeing this error

Thanks in advance

Issue 2:
Hello everyone,

I got the code from tensorflow website for this tutorial. It processed for a long while and got the following errors. Installing tensorflow from sources also did not work for me as it gave me many errors. so I deleted everything installed tensorflow on my Ubuntu 16.04 LTS. Please please help me in resolving these errors as it is an emergency. I am a beginner to tensorflow and its been 2 months already I have been seeing this error

Thanks in advance
Issue 1:
Let us know what functionality you'd like to see in Playwright and what is your use case.

A special locator to visually locate an element.

In taiko, browser automation library by thoughtwork, you could locate element visually like the following:

click(checkbox(near("Username")))

Taiko calls it proximity locator

Refer: https://docs.taiko.dev/#smart-selectors

This locator will reduce the flakiness of the script and the maintenance cost because the locator doesn't rely on html structure that may be changed from time to time. Even we can assign data-testid attribute but this is very useful in some cases as well.

Do you think others might benefit from this as well?

Yes, it is.

Issue 2:
System info

Playwright Version: v1.32.3

Operating System: macOS 13.3.1

Browser: All

Other info: pnpm v7 as package manager on Node v16

Source code

I provided exact source code that allows reproducing the issue locally.

Link to the GitHub repository with the repro

https://github.com/segevfiner/playwright-core-version-conflict

Steps

Run pnpm install

Try running pnpm playwright test in the apps/a and apps/b directories.

On of them will fail with an error message of this sort:

╔═════════════════════════════════════════════════════════════════════════════════════════════════════════════╗
║ Playwright Test integrity check failed: ║
║ You have @playwright/test version '1.32.3' and 'playwright-core' version '1.32.2' installed! ║
║ You probably added 'playwright-core' into your package.json by accident, remove it and re-run 'npm install' ║
╚═════════════════════════════════════════════════════════════════════════════════════════════════════════════╝

Expected

For the tests of both packages the work.

Actual

A Playwright test integrity check failed error.

Analysis

This is caused by the way pnpm hoists dependencies. Since playwright-core doesn't declare that @playwright/test as a peer dependency, pnpm isn't going to create separate trees for it in the virtual store with the actual @playwright/test version that is required by each package, rather there will only be one playwright-core used by both, which then when importing @playwright/test will get an arbitrary version of @playwright/test that happens to be the one that was hoisted.

I suggested a fix here #22595, but it was rejected by @pavelfeldman, which requested I open an issue instead.

Note that the behavior with dependencies, peer dependencies, and CLI's (AKA bin) is very subtle, as it stands both playwright-core and @playwright/test declare the bin, which means one of them wins arbitrary and will be what gets invoked by the package manager. Also note that @playwright/experimental-ct-vue and the like also do the same. So just moving the CLI to one of those packages might not be as trivial as they will fight each other.
Issue 1:
julia> s = UDPSocket()
Error showing value of type UDPSocket:
ERROR: type UDPSocket has no field buffer
in show at stream.jl:236
in anonymous at show.jl:1351
in with_output_limit at ./show.jl:1328
in showlimited at show.jl:1350
in writemime at replutil.jl:4
[inlined code] from expr.jl:8 (repeats 2 times)
in display at REPL.jl:114
[inlined code] from multimedia.jl:151
in display at multimedia.jl:163
in print_response at REPL.jl:134
in print_response at REPL.jl:121
in anonymous at REPL.jl:624
in run_interface at ./LineEdit.jl:1610
in run_frontend at ./REPL.jl:864
in run_repl at ./REPL.jl:167
in _start at ./client.jl:419

This appears to have broken since 0.3; it works there but not on 0.4 or master.

Issue 2:
it seams that show() method for UDPSocket type objects is broken.

julia> UDPSocket()
Error showing value of type UDPSocket:
ERROR: type UDPSocket has no field buffer
in show at stream.jl:237
in anonymous at show.jl:1278
in with_output_limit at ./show.jl:1255
in showlimited at show.jl:1277
in writemime at replutil.jl:4
in display at REPL.jl:114
in display at REPL.jl:117
in display at multimedia.jl:151
in print_response at REPL.jl:134
in print_response at REPL.jl:121
in anonymous at REPL.jl:624
in run_interface at ./LineEdit.jl:1610
in run_frontend at ./REPL.jl:863
in run_repl at ./REPL.jl:167
in _start at ./client.jl:453

julia> versioninfo()
Julia Version 0.4.0
Commit 0ff703b* (2015-10-08 06:20 UTC)
Platform Info:
System: Linux (x86_64-linux-gnu)
CPU: Intel(R) Core(TM) i7 CPU 950 @ 3.07GHz
WORD_SIZE: 64
BLAS: libopenblas (NO_LAPACK NO_LAPACKE DYNAMIC_ARCH NO_AFFINITY Nehalem)
LAPACK: liblapack.so.3
LIBM: libopenlibm
LLVM: libLLVM-3.3
Issue 1:
Description

I noticed that sklearn.metrics.pairwise.pairwise_distances function agrees with np.linalg.norm when using np.float64 arrays, but disagrees when using np.float32 arrays. See the code snippet below.

Steps/Code to Reproduce

import numpy as np
import scipy
import sklearn.metrics.pairwise

# create 64-bit vectors a and b that are very similar to each other
a_64 = np.array([61.221637725830078125, 71.60662841796875, -65.7512664794921875], dtype=np.float64)
b_64 = np.array([61.221637725830078125, 71.60894012451171875, -65.72847747802734375], dtype=np.float64)

# create 32-bit versions of a and b
a_32 = a_64.astype(np.float32)
b_32 = b_64.astype(np.float32)

# compute the distance from a to b using numpy, for both 64-bit and 32-bit
dist_64_np = np.array([np.linalg.norm(a_64 - b_64)], dtype=np.float64)
dist_32_np = np.array([np.linalg.norm(a_32 - b_32)], dtype=np.float32)

# compute the distance from a to b using sklearn, for both 64-bit and 32-bit
dist_64_sklearn = sklearn.metrics.pairwise.pairwise_distances([a_64], [b_64])
dist_32_sklearn = sklearn.metrics.pairwise.pairwise_distances([a_32], [b_32])

# note that the 64-bit sklearn results agree exactly with numpy, but the 32-bit results disagree
np.set_printoptions(precision=200)

print(dist_64_np)
print(dist_32_np)
print(dist_64_sklearn)
print(dist_32_sklearn)

Expected Results

I expect that the results from sklearn.metrics.pairwise.pairwise_distances would agree with np.linalg.norm for both 64-bit and 32-bit. In other words, I expect the following output:

[ 0.0229059506440019884643266578905240749008953571319580078125]
[ 0.02290595136582851409912109375]
[[ 0.0229059506440019884643266578905240749008953571319580078125]]
[[ 0.02290595136582851409912109375]]

Actual Results

The code snippet above produces the following output for me:

[ 0.0229059506440019884643266578905240749008953571319580078125]
[ 0.02290595136582851409912109375]
[[ 0.0229059506440019884643266578905240749008953571319580078125]]
[[ 0.03125]]

Versions

Darwin-16.6.0-x86_64-i386-64bit
('Python', '2.7.11 | 64-bit | (default, Jun 11 2016, 03:41:56) \n[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)]')
('NumPy', '1.11.3')
('SciPy', '0.19.0')
('Scikit-Learn', '0.18.1')

Issue 2:
There is a new bug in master brunch, scikit-learn/sklearn/utils/fixes.py

in line 406: if np_version < (1, 12, 0): , an error occured, TypeError: unorderable types: str() < int().

It's because in the function :

def _parse_version(version_string):
version = []
for x in version_string.split('.'):
try:
version.append(int(x))
except ValueError:
# x may be of the form dev-1ea1592
version.append(x)
return tuple(version)

However， my numpy version is：1.12.0b1, this funciotn return a tuple(1,12,'0b1') , but in line 406, It is compared with integer type, so the error occured.

I just in a simple way to solve it. I change the code to if np_version < (1, 12, '0'): in line 406, just change the third tuple index to string type.

Of course, this is not a common method.
Issue 1:
Elasticsearch version: 2.4.0

JVM version: 1.8

OS version: Elastic Cloud

Description of the problem including expected versus actual behavior:

distanceInMiles() is not recognized when GeoPoints come from an array. Expected behavior is that the geo distance methods would operate on individual GeoPoints regardless of where they originate.

Error Message: No signature of method: org.elasticsearch.common.geo.GeoPoint.distanceInMiles() is applicable for argument types: (java.lang.Double, java.lang.Double)

Seems like elasticsearch is hoping for one of these: org.elasticsearch.index.fielddata.ScriptDocValues$GeoPoints

Works fine: "script": "distance = doc['home.coordinates'].distanceInMiles(lat,lon)"

Works fine: "script": "doc[favorite_parks.coordinates].each { coordinate -> sumOfDistances = 1 }; return 1 "

Doesn't work: "script": "doc[favorite_parks.coordinates].each { coordinate -> sumOfDistances = coordinate.distanceInMiles(lat,lon) }; return 1 "

Steps to reproduce:

https://gist.github.com/alexlitvak13/8146cc5a8ac0defd5ffcf77116d375d4

Related commentary at the bottom of this ticket: #16695

Issue 2:
It will be really confortable to allow call to arcDistance from GeoPoint. Indeed actually we can only call them from GeoPoints (and that will use only the first point).
Issue 1:
Bug Report

For English only, other languages will not accept.

Before report a bug, make sure you have:

Searched open and closed GitHub issues.

Read documentation: ShardingSphere Doc.

Please pay attention on issues you submitted, because we maybe need more details.

If no response anymore and we cannot reproduce it on current information, we will close it.

Please answer these questions before submitting your issue. Thanks!

Which version of ShardingSphere did you use?

5.1.0

Which project did you use? ShardingSphere-JDBC or ShardingSphere-Proxy?

ShardingSphere-Proxy

Expected behavior

apache flink cdc connector work together with apache shardingsphere

Actual behavior

when use apache ShardingSphere as cdc datasource,it will cause a error when cdc try to enum the database tables:

Reason analyze (If you can)

ShardingSphere is missing the information_schema database which provider the metadata information of the instance databases,may be that's the reason?

Steps to reproduce the behavior, such as: SQL to execute, sharding rule configuration, when exception occur etc.

reference issue:

ververica/flink-cdc-connectors#1148

Example codes for reproduce this issue (such as a github link).

reference issue:

ververica/flink-cdc-connectors#1148

Issue 2:
Preface

currently the agent module use maven shade plugin as the solution for dependency conflict, however this makes a lot of dependency duplication in different plugin modules, followings are the plugins :

the shaded guava are duplicated in all plugin artifact:

Missions

to refactor the agent packaging, we need to refactor followings :

add shaded dependency in bootstrap module

add exclusions in plugins to make the plugin artifact smaller

extract the metrics-core seperately

Shaded Dependencies

Reduce Size

before

after

the plugins reduced from 63.2MB to 12.1MB

extract metrics-core

Test

Prometheus

Zipkin
Issue 1:
Any use of => in a script inside an HTML file produces an error and says you have to set target:ES6 in jsconfig.json. But doing so does not make the error go away - it only applies to .js files

Issue 2:
I use Vue and webpack to build my webapp, which allows me to write a web component like this:

<style>
/*the component's css goes here*/
</style>
<template>
<!-- the component's html goes here-->
<div></div>
</template>
<script>
// the component's js goes here
import Xx from 'xx'
let xx = new Xx()
xx(yy => {
return 'Have a nice day'
})
</script>

but, the problem is even I created a jsconfig.json file which sets the target to ES6. It just didn't work.
Issue 1:
I was wondering if there was a technical reason to put these into one repo. It seems like it would be easier to maintain independent repos. If it is about going to one single location for everything, they could be under one organization, "DefinitelyTyped".

Issue 2:
I tried using the latest angularjs/angular-component-router.d.ts file in this repo and had problems.

I tried using the latest stable version of tsc. https://www.npmjs.com/package/typescript

I have a question that is inappropriate for StackOverflow. (Please ask any appropriate questions there).

I want to talk about angularjs/angular-component-router.d.ts.

The authors of that type definition are cc/ @davidreher @vvakame @squadwuschel @calebegg @Huzzle

Component router redeclares RouteDefinition and ComponentDefinition, which are already declared in angularjs/angular.d.ts. Including both definition files together results many errors like this:

error TS2300: Duplicate identifier 'component'.
Issue 1:
like cmder or other terminal,can set a shortcut key,this shortcut key can hide or show terminal window.

and if enable this setting ,the terminal do not show in taskbar.

: )

Issue 2:
The petty thing I miss most when I pop into Windows is having a separate hot key terminal for each of my three monitors. The closest i've ever been to believing in god was when I realized that was obtainable and if you could make that happen in Windows I just might have to kiss you.
Issue 1:
XMLHttpRequest is discouraged and will be more rare to be found in the new contexts like service worker.

Should axios use fetch when XMLHttpRequest is not available?

Issue 2:
Describe the bug

When specifying an expected response.data type to the post method it also sets that expected type to the request body. Also, when not specifying a type, the type for the response.data gets inferred to be the same as the type of the request body. This leads to typescript errors in VS Code and during compile time when the response and request bodies in code doesn't have the same type (which is usually the case). This was not an issue in v0.21.4 - which I reverted to in order to get around this issue.

To Reproduce

Install axios v0.22.0

Add a type for the response.data like in this example:

axios.post<ExpectedResponseBody>(`post url`, { foo: "bar" }, {});

Also look at the CodeSandbox example here

Expected behavior

When specifying the expected type of the response.data in the post call, the request body shouldn't have the same expected type. And the inferred type for the request body shouldn't automatically get applied to response.data.

Environment

Axios Version 0.22.0

Adapter N/A

Browser N/A

Browser Version N/A

Node.js Version: 14.17.3

OS: Win 10

Additional Library Versions: Typescript 4.4.3

Additional context/Screenshots

Type for post method in axios v0.21.4

Type for post method in axios v0.22.0. Note that T is also set to request data and config in this version - which is what I believe is the cause of the issue.
Issue 1:
The majority of unique code per resource type in our RESTStorage implementations deals with populating default values on an object and validating the object prior to persistence. The other 99% of the code is pretty common across all of our resource types and could be generalized. When we look at our tests, the majority of our rest_test.go files are just ensuring that validation occurs prior to create/update. There is a lot of duplicate effort where we could just mandate that the framework that calls the RESTStorage guarantees that the object is valid.

I would like to suggest that the resthandler.go logic enforces that prior to calling a RESTStorage Create and Update operation, it makes a call to something like the following:

type Validator interface {
// ValidateCreate will populate default values on the object, and ensure its valid for persistence
ValidateCreate(ctx api.Context, obj runtime.Object) error
// ValidateUpdate will validate the newObj and copy values required from oldObject prior to persistence
ValidateUpdate(ctx api.Context, newObj runtime.Object, oldObj runtime.Object) error
}

In addition, as we look to the future, there are some additional concepts that I would like the resthandler.go to enforce uniformly across all resources without adding more boilerplate into each RESTStorage implementation. Specifically, I do not want to invoke any Admission control style logic for invalid input, but I do not want to modify each RESTStorage implementation to have to make an explicit call-out to perform admission control as it just increases more boiler plate code.

Issue 2:
We're using Kubernetes 1.2.1 on AWS.

We've been experiencing duplicate UDP packets. It happens only on a very specific setup:

A pod (pod1) that exposes some UDP port

A service that directs traffic to that pod

Another pod (pod2) to send UDP packets (we used netcat for this)

The pods must be running on the same node

When pod2 sends a UDP message to pod1 via the service pod1 shows it received the message twice.

When we go from pod2 directly to the ip of pod1 the message is received once without duplications..

Also, when sending UDP messages from a pod that is running on another machine we don't get the duplication both to the service and pod ip.

We created a repository do make the problem re-produceable here: https://github.com/nanit/udp-server. It contains:

A kube directory with both the deployment and service definitions.

An app directory with the Dockerfile we used to run the udp listener (using tcpdump).

We used netcat in order to send UDP packets to the listener:

echo "some message" | nc -u udp-listen 8088

When we replaced udp-listen (the service name) with the explicit ip of the pod we didn't get the duplication.

Also, when sending UDP messages from a pod running on another node we didn't get the duplications.

I'll be happy to provide any further info if needed.
Issue 1:
When running

import pandas as pd
index = pd.date_range('1/1/2000', periods=9, freq='0.9S')
series = pd.Series(range(9), index=index)

>>> series
2000-01-01 00:00:00.000 0
2000-01-01 00:00:00.900 1
2000-01-01 00:00:01.800 2
2000-01-01 00:00:02.700 3
2000-01-01 00:00:03.600 4
2000-01-01 00:00:04.500 5
2000-01-01 00:00:05.400 6
2000-01-01 00:00:06.300 7
2000-01-01 00:00:07.200 8
Freq: 900L, dtype: int64

I get

>>> series.resample(rule='0.5S').head(100)
2000-01-01 00:00:00.000 0.0
2000-01-01 00:00:00.500 1.0
2000-01-01 00:00:01.000 NaN
2000-01-01 00:00:01.500 2.0
2000-01-01 00:00:02.000 NaN
2000-01-01 00:00:02.500 3.0
2000-01-01 00:00:03.000 NaN
2000-01-01 00:00:03.500 4.0
2000-01-01 00:00:04.000 NaN
2000-01-01 00:00:04.500 5.0
2000-01-01 00:00:05.000 6.0
2000-01-01 00:00:05.500 NaN
2000-01-01 00:00:06.000 7.0
2000-01-01 00:00:06.500 NaN
2000-01-01 00:00:07.000 8.0
Freq: 500L, dtype: float64

However I do not expect to get

>>> series.resample(rule='0.5S').interpolate(method='linear')
2000-01-01 00:00:00.000 0.000000
2000-01-01 00:00:00.500 0.555556
2000-01-01 00:00:01.000 1.111111
2000-01-01 00:00:01.500 1.666667
2000-01-01 00:00:02.000 2.222222
2000-01-01 00:00:02.500 2.777778
2000-01-01 00:00:03.000 3.333333
2000-01-01 00:00:03.500 3.888889
2000-01-01 00:00:04.000 4.444444
2000-01-01 00:00:04.500 5.000000
2000-01-01 00:00:05.000 5.000000
2000-01-01 00:00:05.500 5.000000
2000-01-01 00:00:06.000 5.000000
2000-01-01 00:00:06.500 5.000000
2000-01-01 00:00:07.000 5.000000
Freq: 500L, dtype: float64

instead I expect the last value to be still 8.0 and still 7.0 for the timestamp with 6.5 seconds.

I posted this on https://stackoverflow.com/q/46728152/4533188 and I was told this might be a bug - I got the same impression.

>>> pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-129-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: de_DE.UTF-8
LOCALE: de_DE.UTF-8
pandas: 0.20.3
pytest: 3.2.1
pip: 9.0.1
setuptools: 36.2.2.post20170724
Cython: 0.26
numpy: 1.13.3
scipy: 0.19.1
xarray: None
IPython: None
sphinx: 1.6.3
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: 0.9.8
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None

Issue 2:
from SO: http://stackoverflow.com/questions/22178642/assignment-modification-of-values-in-an-indexed-subframe-in-pandas

data = {'me':list('rttti'),'foo': list('aaade'), 'bar': arange(5)*1.34+2, 'bar2': arange(5)*-.34+2}
df = pd.DataFrame(data).set_index('me')

df.loc['r',['bar','bar2']]*=2.0
df.loc['t','bar']*=2.5
df.loc['t',['bar','bar2']]*=2.0