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

Whisper speech recognition without conditioning on previous text.

As in https://github.com/openai/whisper/blob/7858aa9c08d98f75575035ecd6481f462d66ca27/whisper/transcribe.py#L278

Motivation

Whisper implementation is great however conditioning the decoding on previous text can cause significant hallucination and repetitive text, e.g.:

"Do you have malpractice? Do you have malpractice? Do you have malpractice? Do you have malpractice? Do you have malpractice? Do you have malpractice? Do you have malpractice? Do you have malpractice? Do you have malpractice? Do you have malpractice? Do you have malpractice? Do you have malpractice? Do you have malpractice? Do you have malpractice? Do you have malpractice? Do you have malpractice?"

Running openai's model with --condition_on_previous_text False drastically reduces hallucination

@ArthurZucker

Issue 2:
Hello,

I tried to import this:

from transformers import AdamW, get_linear_schedule_with_warmup

but got error : model not found

but when i did this, it worked:

from transformers import AdamW
from transformers import WarmupLinearSchedule as get_linear_schedule_with_warmup

however when I set the scheduler like this :

scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total)

I got this error :

__init__() got an unexpected keyword argument 'num_warmup_steps'
Issue 1:
Bug summary

I put a torch.Tensor in matplotlib.pyplot.hist() , but it draw a wrong graphic and take a long time.

Although transform to numpy, the function work well. But all the others function I used are work well on tensor. So I think its a bug.

Code for reproduction

import matplotlib.pyplot as plt
import torch

plt.hist(torch.randn(20))
plt.show()

Actual outcome

Expected outcome

Additional information

No response

Operating system

Windows

Matplotlib Version

3.5.1

Matplotlib Backend

module://ipykernel.pylab.backend_inline

Python version

3.7.13

Jupyter version

6.4.8

Installation

conda

Issue 2:
Bug report

Bug summary

Generating np.random.randn(1000) values, visualizing them with plt.hist(). Works fine with Numpy.

When I replace Numpy with tensorflow.experimental.numpy, Matplotlib 3.3.4 fails to display the histogram correctly. Matplotlib 3.2.2 works fine.

Code for reproduction

import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
import tensorflow.experimental.numpy as tnp

# bad image
labels1 = 15 + 2 * tnp.random.randn(1000)
_ = plt.hist(labels1)

# good image
labels2 = 15 + 2 * np.random.randn(1000)
_ = plt.hist(labels2)

Actual outcome

Expected outcome

Matplotlib version

Operating system: Windows 10

Matplotlib version (import matplotlib; print(matplotlib.__version__)): 3.3.4

Matplotlib backend (print(matplotlib.get_backend())): module://ipykernel.pylab.backend_inline

Python version: 3.8.7

Jupyter version (if applicable): see below

Other libraries: see below

TensorFlow 2.4.1

jupyter --version
jupyter core : 4.7.0
jupyter-notebook : 6.1.6
qtconsole : 5.0.1
ipython : 7.20.0
ipykernel : 5.4.2
jupyter client : 6.1.7
jupyter lab : not installed
nbconvert : 6.0.7
ipywidgets : 7.6.3
nbformat : 5.0.8
traitlets : 5.0.5

Python installed from python.org as an exe installer. Everything else is pip install --user

Bug opened with TensorFlow on this same issue:

tensorflow/tensorflow#46274
Issue 1:
https://github.com/kubernetes/kubernetes/blob/master/pkg/kubelet/container_bridge.go#L122-L143

container_bridge.go assumes that the virtual IP of services & pods will be in the 10. space.

I propose there is no reason to make this assumption.

As outlined in #15932, cluster admins may need to deploy to hosts in which 10. is reserved for the nodes. In such a case, Kubelets must support an alternative range.

Issue 2:
Today kubelet sets up an iptables MASQUERADE rule for any traffic destined for anything except 10.0.0.0/8. This is close, but not even correct on GCE, and certainly not right elsewhere.

First GCE. We probably want something like:

iptables -t nat -N KUBE-IPMASQ
iptables -t nat -A KUBE-IPMASQ -d 10.0.0.0/8 -j RETURN
iptables -t nat -A KUBE-IPMASQ -d 172.16.0.0/12 -j RETURN
iptables -t nat -A KUBE-IPMASQ -d 192.168.0.0/16 -j RETURN
iptables -t nat -A KUBE-IPMASQ -j MASQUERADE
iptables -t nat -I POSTROUTING -j KUBE-IPMASQ

This catches all traffic to RFC1918 ranges and masquerades it. We can probably optimize with CONNMARK or something so we only consider packets from containers. This is probably still imperfect, but better, for lack of project-wide NAT for egress.

For other environments, we really have no idea what the correct policy for this is. It is closer to "your nodes must handle this" than "we can handle this for you". It's debatable whether we should even try.

Either:

a) We teach kubelet a lot more and let people pass flags to nearly=arbitrarily configure this

b) We tell people to configure this as part of their node setup

This popped up when I realized GKE allows users to set up 172.* clusters - any traffic between containers in one of these will get masqueraded - not correct behavior!! This is not a huge deal right now because kube-proxy has the same effect when traversing services. As we fix kube-proxy in the wake of 1.0, masquerade will be a bigger deal, especially for micro-segmenting.

Additional considerations: VPNs have bizarre and very custom needs. Every such thing has an as yet unmeasured perf implication. This also pops up in our GCE firewall thing that @ArtfulCoder is working on.

@dchen1107 @alex-mohr
Issue 1:
.valid would apply the normal ("successful") input/textarea/select styles when applied to those input types while within a .control-group.error.

The use case for this is a .control-group section with a "multi-part" input (ex: time entry with hour and minute fields). If minutes was valid, but hour was not the .control-group should still have .error applied.

This would give users a more obvious indication of what was left to fix--since only input/textarea/select's without the .valid class would be marked in the .error styles.

.valid could inherit direction form the default input/textarea/select styles.

Issue 2:
It would be useful to provide column push and pull classes for all three different grids.

This means that .col-push-* and .col-pull-* only affect the mobile grid and are joined by .col-push-sm-*, .col-pull-sm-* and .col-push-lg-*, .col-pull-lg-* to control the column order differently depending on the grid in use.

Summary: Allow for different column reordering in different grids.
Issue 1:
I love the Acrylic effect in the new terminal. Really gives it a modern feel. But at the moment, whenever you focus on a different program, i.e click away from the terminal, it loses the Acrylic effect and goes to a solid color. I have two monitors so most of the time, I have my terminal visible on the second monitor while working in the first and most of the time the terminal is just a solid black color. Would it be possible to make it so that it keeps the Acrylic effect or is that a limitation of the UI toolkit?

Issue 2:
Using Windows Store version 0.5.2762.0

Zipped DMP file attached.

conhost.exe.21732.dmp.zip
Issue 1:
I have searched the issues of this repository and believe that this is not a duplicate.

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

Environment

Dubbo version: 2.7.0

Operating System version: Mac OS

Java version: 1.8

Steps to reproduce this issue

#2031 have fixed the writePlace stackOverflow issue. The code have been merged into hessian-lite, but not in dubbo.

Right now, the issue still there in dubbo 2.7.0. But we are not able to embed hessian-lite, as we are not able to remove hessian from dubbo(conflicts as below).

Found in:
org.apache.dubbo:dubbo:jar:2.7.0:compile
com.alibaba:hessian-lite:jar:3.2.5:compile
Duplicate classes:
com/alibaba/com/caucho/hessian/io/HessianDebugState$DateState.class
com/alibaba/com/caucho/hessian/io/ThrowableSerializer.class

Define a class with a writeReplace method return this

public class WriteReplaceReturningItself implements Serializable {

private static final long serialVersionUID = 1L;

private String name;

WriteReplaceReturningItself(String name) {
this.name = name;
}

public String getName() {
return name;
}

/**
* Some object may return itself for wrapReplace, e.g.
* https://github.com/FasterXML/jackson-databind/blob/master/src/main/java/com/fasterxml/jackson/databind/JsonMappingException.java#L173
*/
Object writeReplace() {
//do some extra things

return this;
}
}

Use Hessian2Output to serialize it

ByteArrayOutputStream bout = new ByteArrayOutputStream();
Hessian2Output out = new Hessian2Output(bout);

out.writeObject(data);
out.flush();

Error occurs

java.lang.StackOverflowError
at com.alibaba.com.caucho.hessian.io.SerializerFactory.getSerializer(SerializerFactory.java:302)
at com.alibaba.com.caucho.hessian.io.Hessian2Output.writeObject(Hessian2Output.java:381)
at com.alibaba.com.caucho.hessian.io.JavaSerializer.writeObject(JavaSerializer.java:226)
at com.alibaba.com.caucho.hessian.io.Hessian2Output.writeObject(Hessian2Output.java:383)
at com.alibaba.com.caucho.hessian.io.JavaSerializer.writeObject(JavaSerializer.java:226)

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

Expected Result

The serialization process should complete with no exception or error.

Actual Result

java.lang.StackOverflowError
at com.alibaba.com.caucho.hessian.io.SerializerFactory.getSerializer(SerializerFactory.java:302)
at com.alibaba.com.caucho.hessian.io.Hessian2Output.writeObject(Hessian2Output.java:381)
at com.alibaba.com.caucho.hessian.io.JavaSerializer.writeObject(JavaSerializer.java:226)
at com.alibaba.com.caucho.hessian.io.Hessian2Output.writeObject(Hessian2Output.java:383)
at com.alibaba.com.caucho.hessian.io.JavaSerializer.writeObject(JavaSerializer.java:226)

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

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

https://github.com/apache/incubator-dubbo/blob/6e4ff91dfca4395a8d1b180f40f632e97acf779d/dubbo-monitor/dubbo-monitor-api/src/main/java/org/apache/dubbo/monitor/support/MetricsFilter.java#L44-L47

The way to calculate the invocation time can be improved by using java.lang.System#nanoTime
Issue 1:
I hope this is not a dupe, I was really surprised by this bug because - being so simple in nature - it appears it should have been caught by your tests (or at least stumbled upon by other people), hence there is a slight probability that I misunderstood something or that the compiler isn't capable to do the necessary work (at which point I'd be doubly disappointed).

Anyway, the example is artificial because it's extracted from a larger code base.

Imagine a simple project, let's start with tsconfig.json

{
"compilerOptions": {
"target" : "ES5",
"noImplicitAny": true,
"out": "badout.js"
}
}

In the same folder, add a single file c.ts which is just class C {}

Add a subfolder called sub and put the following two files inside:

// file a.ts
class A extends B {
method1(p1:number, p2:string):void {}
}

// file b.ts
class B {
method1(p1:number, p2:string):void {}
}

Then simply run tsc in the project folder (the one containing tsconfig.json and c.ts)

The output will be:

var C = (function () {
function C() {
}
return C;
})();
var __extends = this.__extends || function (d, b) {
for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p];
function __() { this.constructor = d; }
__.prototype = b.prototype;
d.prototype = new __();
};
var A = (function (_super) {
__extends(A, _super);
function A() {
_super.apply(this, arguments);
}
A.prototype.method1 = function (p1, p2) { };
return A;
})(B);
var B = (function () {
function B() {
}
B.prototype.method1 = function (p1, p2) { };
return B;
})();

The problem is obvious - we're trying to use B before it is defined.

If this has been noted and fixed, please forgive the noise.

If not, please try to look into fixing this before 1.5 is "done".

Issue 2:
The compiler should issue an error when code uses values before they could possibly be initialized.

// Error, 'Derived' declaration must be after 'Base'
class Derived extends Base { }
class Base { }
Issue 1:
Original ticket http://projects.scipy.org/numpy/ticket/1044 on 2009-03-09 by @pv, assigned to unknown.

--- Continuation of #1440.

Ufuncs return array scalars for 0D array input:

>>> import numpy as np
>>> type(np.conjugate(np.array(1+2j)))
<type 'numpy.complex128'>
>>> type(np.sum(np.array(3.), np.array(5.)))
<type 'numpy.float64'>

Should they return 0D arrays instead?

Issue 2:
I have a unit test failing on np.divide.accumulate with numpy 1.15.0 compiled with the Intel compiler. The same code is fine with a local build of GCC.

Perhaps this is related to ENH: umath: don't make temporary copies for in-place accumulation (#10665) where @juliantaylor fixed numpy/core/src/umath/loops.c.src for GCC's ivdep pragma. That was reviewed by @eric-wieser .

Reproducing code example

Source code was 1.15.0 compiled with the Intel compiler 2018.1.163. where np.divide.accumulate fails in the numpy unit tests.

I can reproduce with this Python 2.7 test case for float64 dtype. It runs correctly for float32 and float128 on the Intel compiler. It also runs correctly for float64 using GCC instead of Intel.

$ python -c "import numpy as np; acc = np.divide.accumulate;
a = np.ones(8, dtype=np.float64);
print acc(a, out=2*np.ones_like(a)); print acc(a); print acc(a); print acc(a);
print acc(a, out=2*np.ones_like(a))"
[1. 1. 2. 2. 2. 2. 2. 2.]
[1. 1. 1. 1. 2. 2. 2. 2.]
[1. 1. 1. 1. 1. 1. 2. 2.]
[1. 1. 1. 1. 1. 1. 1. 1.]
[1. 1. 2. 2. 2. 2. 2. 2.]

Notice how only the next two elements are set each time np.divide.accumulate runs. That suggests the float64s are being processed in pairs. Then in the final line, specifying the output array again returns to the first line's incorrect behavior.

Numpy/Python version information:

numpy 1.15.0 build with ICC 2018.1.163.

python 2.7.11 built with GCC 4.9.3.
Issue 1:
Bug report

Bug summary

If there are multiple curves with the same label in a figure/subplot, only the last one of them can be selected in the Figure options window under the Curves tab. In the legend however, they appear as they should.

Code for reproduction

import matplotlib.pyplot as plt
plt.plot([0,1],[0,1],label="line")
plt.plot([0,1],[1,0],label="line")
plt.legend()
plt.show()

Actual outcome

See description above and the image below.

Expected outcome

All curves should be listed in the Figure options window, even if they have the same label.

Matplotlib version

Operating system: Windows 10

Matplotlib version (import matplotlib; print(matplotlib.__version__)): 3.3.4

Matplotlib backend (print(matplotlib.get_backend())): Qt5Agg

Python version: 3.8.5

Jupyter version (if applicable): -

Other libraries: -

Matplotlib has been installed with pip.

Issue 2:
Bug report

Bug summary

Code for reproduction

# Paste your code here
#
#

Actual outcome

# If applicable, paste the console output here
#
#

Expected outcome

Matplotlib version

Operating system:

Matplotlib version:

Matplotlib backend (print(matplotlib.get_backend())):

Python version:

Jupyter version (if applicable):

Other libraries:
Issue 1:
ISSUE TYPE

Bug Report

COMPONENT NAME

Templating

ANSIBLE VERSION

1.8.2

CONFIGURATION

OS / ENVIRONMENT

N/A (but rhel7)

SUMMARY

When entering passwords for login/sudo, they are parsed by the templater instead of being used literally. This creates unexpected behaviour - for example, with a password that contains a '{':

$ ansible -i "testhost1," all -k -m ping
SSH password:
testhost1 | FAILED => template error while templating string: unexpected token 'eof'
$

If the substring in the password were valud template syntax we'd just get 'invalid password' errors, which would be even more confusing.

There is potential for destructive behaviour and - while very minor - potential security concerns by use of a malicious password (I don't have a specific example, but in places with esoteric security implementations one could see this being exploitable).

STEPS TO REPRODUCE

Set password on remote host '123abc{'

Attempt ansible run with -k

EXPECTED RESULTS

Successfully authenticates against remote host and runs my command

ACTUAL RESULTS

Connecting to host host fails with 'error while templating string'

SSH password:
testhost1 | FAILED => template error while templating string: unexpected token 'eof'

Issue 2:
ISSUE TYPE

Documentation Report

COMPONENT NAME

https://docs.ansible.com/ansible/latest/dev_guide/developing_modules_documenting.html#return-block

ANSIBLE VERSION

N/A since it's web documentation

CONFIGURATION

N/A

OS / ENVIRONMENT

N/A

SUMMARY

Documentation has the description forreturned as "When this value is returned, such as always, on success, always". First, always is repeated. Second, it should list all the options for returned and not just 3.

STEPS TO REPRODUCE

N/A

EXPECTED RESULTS

I'd expect this to be more thorough with a bulleted list of values and what they mean. Such as...

always - Returned on every request
success - Returned only when request was successful

ACTUAL RESULTS

See webpage.
Issue 1:
please add 'raw' to modules allowed to have duplicate parameters.

ansible/lib/ansible/runner/__init__.py

Line 444
in
be4dbe7

is_shell_module = self.module_name in ('command', 'shell')

- is_shell_module = self.module_name in ('command', 'shell')
+ is_shell_module = self.module_name in ('command', 'shell', 'raw')

thank you.

Issue 2:
Issue Type:

Feature Idea

Ansible Version:

1.6.6

Environment:

N/A

Summary:

I developed a lookup plugin which is for pulling secret keys from the cloud server. However, when I use with_secret statement like this

- name: foo
bar: secret={{ item }}
with_secret: eggs/spam/secret.txt

and you will see something like this on the screen when you run ansible

ok: [default] => (item="MY_SUPER_SECRET_KEY_HERE")

I really don't like how this works, it displays your secret key on screen. If you redirect logging for ansible to a file or what, then all your secret keys are in the file now.

And in other situations, like with_file, you may see something like

ok: [default] => (item="
thousand lines here
thousand lines here
thousand lines here
thousand lines here
thousand lines here
....

I think this is really bad for ansible to print literately everything it got from a lookup. So, I propose, for a lookup class, if a sanitize method is defined, ansible should then call the method for each item before printing it on the screen. So that for my secret getting lookup I can write something like this

def sanitize(self, item):
return '*' * 8

And for huge file and other things, people can write something like

def sanitize(self, item):
return 'File {} ({} bytes)'.format(item._filename, item._size)

def run(self, terms, inject=None, **kwargs):
result = 'a long long long long file content'
result._filename = terms
result._size = len(result)
return [result]

Steps To Reproduce:

N/A

Expected Results:

N/A

Actual Results:

N/A
Issue 1:
Electron Version: 3.0 Beta 4

Operating System (Platform and Version): Windows10, 8 (x86 and x64)

Last known working Electron version: 2.x

Expected Behavior

PDF should be displayed.

Actual behavior

"Save As.." dialog for download appears and pdf will not be displayed, even when downloaded.

To Reproduce

Create a BrowserWindow and load an URL with a pdf or iframes with pdfs.

const win = new BrowserWindow({
width: 800,
height: 600,
webPreferences: {
plugins: true
}
});

win.webContents.loadURL(URL_TO_PDF);

Issue 2:
Electron version: 0.36.7

Operating system: OS X

My electron app needs to make an HTTP request to a service that returns malformed headers:

$ curl -IS 'http://192.168.0.1/getdeviceinfo/info.bin'
HTTP/1.1 200 OK
Date: Wed, 02 Dec 2015 16:42:45 GMT
Server: nostradamus 1.9.5
Connection: close
Etag: Aug 28 2015, 07:57:20
Transfer-Encoding: chunked
HTTP/1.0 200 OK
Content-Type: text/html

Notice the duplicate 200 OK header? Yeah, so does the native Node.js parser and it chokes. In a standalone test script, I've found that I can use the http-parser-js library to make the same request and it handles the bad headers gracefully.

Now I need to make that work within the Electron app that needs to actually make the call and retrieve the data and it's failing with the same HPE_INVALID_HEADER_TOKEN I've been getting all along. I assume, for that reason, that the native HTTP parser is not getting overridden the way that it does in the test script.

In my electron app's main process, I have the same code I used in my test script:

process.binding('http_parser').HTTPParser = require('http-parser-js').HTTPParser;
var http = require('http');

var req = http.request( ... )

Is there an alternate process binding syntax I can use within Electron? Or some other means of making an HTTP request without taxing the Node parser?
Issue 1:
Fixing issue #9691 revealed a deeper problem with the QuadPrefixTree's memory usage. At 1m precision the example shape in https://gist.github.com/nknize/abbcb87f091b891f85e1 consumes more than 1GB of memory. This is initially alleviated by using 2 bit encoded quads (instead of 1byte) but only delays the problem. Moreover, as new complex shapes are added duplicate quadcells are created - thus introducing unnecessary redundant memory consumption (an inverted index approach makes mosts sense - its Lucene!).

For now, if a QuadTree is used for complex shapes great care must be taken and precision must be sacrificed (something that's automatically done with the distance_error_pct without the user knowing - which is a TERRIBLE approach). An alternative improvement could be to apply a Hilbert R-Tree - which will be explored as a separate issue. Or to restrict the accuracy to a lower level of precision (something that's undergoing experimentation).

Issue 2:
Elasticsearch version:

2.3.4

JVM version:

1.8

some gc log

[monitor.jvm ] [Powerpax] [gc][young][132059][21130] duration [756ms], collections [1]/[1.4s], total [756ms]/[10.3m], memory [26.7gb]->[24.1gb]/[29.6gb

], all_pools {[young] [2.7gb]->[3.2mb]/[2.7gb]}{[survivor] [20.6mb]->[240.8mb]/[357.7mb]}{[old] [23.9gb]->[23.9gb]/[26.5gb]}

doubt

I use bulk to index. 30 billion doc very day. But some node alway out of cluster because long time full gc after some days.

I dump bin log and find taskManager object cost most of memory(18GB), I doubt why taskManager cost cost many memory ? It cache large number of task(request) ? some body can explain the situation? thanks
Issue 1:
The Problem

VSCode can nicely handle comments in JSON files. However, the JSON specification does not allow comments in JSON files. Therefore most JSON parsers fail when JSON files contain comments. E.g. package.json fails, and even the typescript compiler fails on tsconfig.json files that include comments.

Another problem is that syntax highlighting does not work on github. For example, the file extensions/xml/xml.configuration.json ist not legal JSON. This is symptomatic for repositories that are managed with VSCode:

@dbaeumer in eslint/.vscode/tasks.json

@weinand in vscode-node-debug/.vscode/launch.json

@egamma in vscode-go/blame/master/.vscode/launch.json

Proposal: create a new file type, e.g. .tson

Instead of misleading people about the syntax of JSON by providing support for comments in JSON files, VSCode should rather use a new format like TSON, that would be an extension of JSON that allows for comments. There could be a simpler pre-processor for TSON (like JSON.minify) that converts TSON into JSON by stripping away the comments.

With such a new file type, VSCode could happily use them without compromising the integrity of existing .json files.

In addition, it would be cool to use typescript ambient definitions for .tson files. I really like the typescript schema for tasks.json.

Issue 2:
I'm running VSCode in a corporate network (Active Directory), when I install VSCode I'm asked for an admin password, after the installation normal users sessions don't have the VSCode context menu options, only the admin account has that menu.

How can I add the context menu for all users?