composition

Compositions of configurations should be defined independent of the sources of those configurations.
Let us define a composition operator for configurations: override. override takes two configurations, top and bottom, and returns a new configuration with a union of every variable in the two configurations with the values from top taking precedence. In general, clients will start with a configuration of defaults and apply a sequence of overrides.
override may optionally throw, warn, or ignore overrides that do not exist in bottom. Ignore is a good option for migrating configurations across client versions. If a client updates from version 1 to 2 and drops configuration variable "x", a good migration tool will silently drop overrides of "x" in old configurations. Warn and throw are good for clients of varying pedantry.
override may optionally type check overrides. Some clients may want to delay validation until use, to avoid validating unused variables. Some may want pedantic validation of all overrides.

Yes, composition seems essential. In the current Confit, this works using mutation: RootConfig.add() pushes a bottom data source onto a stack, and RootConfig.set() puts a top on the opposite end of the list.
About validation: It seems like it would be useful to keep composition and validation separate, right? I don't see a particular need to couple the two conceptually. (Of course, an API call that does both at once could definitely be useful for cosmetic reasons.)

You're right, it will be good to keep them separate. We can always compose them into a single function for convenience.


Livecoding demo of using aider on itself

Issue
I assume that aider is being used to develop aider itself. I would love to see a livecoding stream of this (even if completely silent) to get a sense of how this tool is being used to develop a real application. A lot of what's available online is sparse and focused on tutorial level demos.
Version and model info
No response

Thanks for trying aider and filing this issue. I'm not sure I can make the time to record a live stream.

This video from the tutorials page does some non-trivial coding:
https://youtu.be/QlUt06XLbJE

This video from the tutorials page does some non-trivial coding:
https://youtu.be/QlUt06XLbJE

OK this is pretty good.


feat: [IDP]: Added the info on mode append

Thanks for contributing to the Harness Developer Hub! Our code owners will review your submission.
Description

Please describe your changes: __________________________________
Jira/GitHub Issue numbers (if any): ______________________________
Preview links/images (Internal contributors only): __________________

PR lifecycle
We aim to merge PRs within one week or less, but delays happen sometimes.
If your PR is open longer than two weeks without any human activity, please tag a code owner in a comment.
PRs must meet these requirements to be merged:

[ ] Successful preview build.
[ ] Code owner review.
[ ] No merge conflicts.
[ ] Release notes/new features docs: Feature/version released to at least one prod environment.

Please check the Execution Link of the Pipeline for the Website Draft URL. This is located in the Preview Step behind the Harness VPN and also is available in #hdh_alerts. E.g Website Draft URL: https://unique-id--harness-developer.netlify.app. Current Draft URL is: https://670e283ddf65ca20c1979032--harness-developer.netlify.app

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Post-release github action to tag latest in Dockerhub

Description
The existing github action which tags the latest docker artifacts in dockerhub appears to be broken.  After the release of 23.7.1, the latest tags for all variants were pointing to 23.1.3.  It is less clear if the github action updated the tags to 23.1.3 or whether they hadn't been updated since the original release of 23.1.3
See this discord thread for additional details:
https://discord.com/channels/905194001349627914/1143574070974218260
Acceptance Criteria

Upon publishing of a release on github, the corresponding variants' versions get tagged as latest in dockerhub
manifest metadata is pushed for the latest such that latest tags are both amd64 and arm64

@jflo are you working on this one already?

yes, this is all getting totally refactored via #6283


data:* is an invalid navigation rule

Bug Report
Problem
I am using "data:*" in the allow-navigation rule, but when executing cordova prepare, it prompts warning message:

The following navigation rule had an invalid URI scheme and will be ignored: "data:*".

What is expected to happen?
No warning message.
What does actually happen?
cordova prepare prompts the warning message:

The following navigation rule had an invalid URI scheme and will be ignored: "data:*".

Information

Command or Code

The reproduce steps:

execute command: cordova create hello
execute command: cd hello
update config.xml to add following section:

    <allow-navigation href="http://*/*" />
    <allow-navigation href="https://*/*" />
    <allow-navigation href="data:*" />

execute command: cordova platform add android windows
execute command: cordova prepare

Environment, Platform, Device

Version information

Cordova: 9.0.0
Checklist

[x] I searched for existing GitHub issues
[x] I updated all Cordova tooling to most recent version
[x] I included all the necessary information above

Can you also post a sample snippet of how to use data: so we can confirm it actually works and applies via the plugin?

Hi @janpio , we are using data: URI to include small piece of data in the HTML page, such as:
<img src="data:image/png;base64,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" />

The Data URI is defined in https://tools.ietf.org/html/rfc2397
Not sure if cordova-plugin-whitelist supports Data URI? But I do see the "data:" exists in the example of readme: https://github.com/apache/cordova-plugin-whitelist#navigation-whitelist

@feiandytan, I am not quite sure if it is the cause (I happened to get here with a similar message with windows) but it might be related to the windows platform. That message only displays up when I try building the windows version. According to the whitelist plugin, it only supports Android.

@feiandytan, I am not quite sure if it is the cause (I happened to get here with a similar message with windows) but it might be related to the windows platform. That message only displays up when I try building the windows version. According to the whitelist plugin, it only supports Android.

it is problem from windows pltform I'm struggling

Hi @janpio , we are using data: URI to include small piece of data in the HTML page, such as:
<img src="data:image/png;base64,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" />

The Data URI is defined in https://tools.ietf.org/html/rfc2397
Not sure if cordova-plugin-whitelist supports Data URI? But I do see the "data:" exists in the example of readme: https://github.com/apache/cordova-plugin-whitelist#navigation-whitelist

allow-navigation is for the top-level frame only, e.g location.href which is the loaded documented in the webview. Not for sub-elements that is present inside the document. See https://github.com/apache/cordova-plugin-whitelist#navigation-whitelist
For images and other web assets, you probably want want to use Content Security Policy.

According to the whitelist plugin, it only supports Android.

I haven't really been involved in cordova-windows development whatsoever, so I'm not sure about our Windows platform but most platforms have their own white list (or moving forward, it will be known as allow list, however some platforms may still use white list terminology) implementation built-in. Android platform is one platform that hasn't had it's migration done yet, but I believe there is a PR intended to be included in our next major release for the android platform. Once that is done, this plugin will be completely obsolete and be deprecated accordingly.

I'm closing this issue as invalid because allow-navigation isn't intended to work on  tags or other web assets, just top-level navigation (or sometimes iframes, due to platform quirks).
If the content security policy doesn't work for you, I encourage you to try reaching out to our Slack.


[WIP] Simplify Tests

#76
just wrote some codemod - and hand edited some edge cases.
https://gist.github.com/boopathi/d979e5266df861052783acf9d3e61d22
Take a look before we apply to other packages

My concern is that source and destination strings are in different places so it might be annoying to look back and forth.
@hzoo what do ya think

Yeah, I felt that too. So I thought we could do
expect({
  source,
  result: transform(source)
}).toMatchSnapshot()

A good point - being able to see the source/expected easily is useful

Not sure if snapshot is the most appropriate approach here unless we can have input/output specified together.

Maybe we can leave this here and revisit.
Implementing what I wrote originally is easier and just reduces boilerplate


Error:Execution failed for task ':app:processExternalMin21DebugGoogleServices'. > No matching client found for package name 'com.kickstarter.kickstarter.debug'

when i build the app, get a error ->
Error:Execution failed for task ':app:processExternalMin21DebugGoogleServices'.

No matching client found for package name 'com.kickstarter.kickstarter.debug'

how can i fix the problem?

ah. for now go to View > Tool windows > Build variants and switch the build variant to anything ending in Release.  the package name in the google-services.json we're copying over only matches com.kickstarter.kickstarter release names so looks like that prevents compiling.
will work on a fix for this asap

@liuruimilk this should be fixed now for all build variants. can you please try pulling master, running make bootstrap then building the project again? thanks!

@luoser thank you.it's works now.but ,i want to install the app to see the effect.there is no release.keystore. so,how to run the app without no debug and no release.keystore.
forgive my Chinglish.

@luoser that's ok,i have downloaded apk,thanks.

it works,thank you.

when i build the app, get a error ::::::    No matching client found for package name 'com.kickstarter.kickstarter.debug'
Please, explain how can i fix it.
I am a novice

I'm getting the same issue

getting this again now. Worked previously but now the build is failing. make bootstrap doesn't seem to resolve things ... it was working about a month ago for me.

@SeanFelipe That's weird but I'm glad it's fixed. Feel free to open a new issue because @luoser had to ping me about this one since I wasn't at Kickstarter in 2016 🙂


GrowlInfo.ActionBeforeClose not called when user click the close button

Describe the bug
var info = new GrowlInfo {
    ShowDateTime = false,
    Message = "xxx",
    ActionBeforeClose = isConfirmed => Debug.WriteLine("xxx")
};
Growl.Success(info);

Expected behavior
No matter auto close or user click, should always called.
Screenshots
If applicable, add screenshots to help explain your problem.
Environment (please complete the following information):

.net: 5
IDE vs2019
Version 3.1.0

Additional context
Add any other context about the problem here.

You should write return true

You should write return true

Yeah, I forget write it, but this is a example. If the ActionBeforeClose not called, it's meaningless no matter return true or not.

@NaBian Hi, I found that if clear by right click context menu, the ActionBeforeClose also not called. Does commit a6a78f4 also fix this?


compatible run scripts for Win/NodeJS, and separate build/test examples

This allows building examples with Node 12.x in windows.
Also separates into "test:examples", and "build:examples" scripts
This leaves just running the tests exclusive to io.js due to jsdom 4 dependency.

I think, if you move out test from buildAll, you need to add test:examples explicitly into .travis.yml.

Yes, added.  Sorry about that, haven't played with Travis CI much yet.
Once Node v4 is out, some this compatible stuff will be resolved (hoping).

Should we also include "0.12" in node_js in .travis.yml now?
https://github.com/mikekidder/redux/blob/buildAll.forWinNodeJS/.travis.yml#L2

Test scripts under Node 0.12 will still doesnt support the ES6 testing, so fails.  Node v4 will see the merging of io.js 3.x and node 12.x.  yea!

Got it.


Proposal: Default options in discriminators

I have the following use case I'd like to solve:
type: object
description: Settings for a country
allOf:
   - type: object
      properties:
        country_code:
            type: string
            description: ISO 3166 country code
            enum: AD, AE, AF, AG .... ZW
        common_field:
            type: string
            description: Something all countries have
discriminator:
   description: Adds extra fields we need depending on country
   propertyName: country_code
   mapping:
       CA: #/CountryCA
       MX: #/CountryMX
       US: #/CountryGeneric
       DE: #/CountryGeneric
       ES: #/CountryGeneric
       # insert many more country definitions here that go to CountryGeneric

This can be made to work, but I would have to have all 200+ countries in the mapping object, each pointing to a reference of an object, and this is really cumbersome. I'd really love to be able to do this, treating the discriminator almost like a switch statement:
discriminator:
   description: Adds extra fields we need depending on country
   propertyName: country_code
   mapping:
       CA: #/CountryCA
       MX: #/CountryMX
   default: #/CountryGeneric

Presumably you mean "if country_code is CA then include the subschema at #/components/schema/CountryCA, and if a specific mapping doesn't exist, fall back to a generic schema? You can do that with if/then/else (and in the upcoming JSON Schema release, propertyDependencies will make it even easier).

Closing due to inactivity, but please let us know if the above didn't resolve your issue.

Unfortunately, this solve doesn't work with most OpenAPI tooling. Making it part of the core OpenAPI spec might encourage more tool developers to support such functionality. It's also important for generated documentation to be able to map it at schema time and not just validation time, which https://json-schema.org/understanding-json-schema/reference/conditionals seems to imply is when it happens.


HTTP status code is wrong when deleting non-exist scheduler

Please answer these questions before submitting your issue. Thanks!

What did you do?

Say there is a store leader evicting scheduler for store 6, and I want to delete that scheduler to allow leaders to be transferred back to it.
The 1st attempt:
$ curl -v -XDELETE "http://<IP_ADDRESS>:2379/pd/api/v1/schedulers/evict-leader-scheduler-6"
* About to connect() to <IP_ADDRESS> port 2379 (#0)
*   Trying <IP_ADDRESS>...
* Connected to <IP_ADDRESS> (<IP_ADDRESS>) port 2379 (#0)
> DELETE /pd/api/v1/schedulers/evict-leader-scheduler-6 HTTP/1.1
> User-Agent: curl/7.29.0
> Host: <IP_ADDRESS>:2379
> Accept: */*
>
< HTTP/1.1 200 OK
< Access-Control-Allow-Headers: accept, content-type, authorization
< Access-Control-Allow-Methods: POST, GET, OPTIONS, PUT, DELETE
< Access-Control-Allow-Origin: *
< Content-Type: application/json; charset=UTF-8
< Date: Thu, 02 Apr 2020 09:49:48 GMT
< Content-Length: 5
<
null
* Connection #0 to host <IP_ADDRESS> left intact

All looks good.
But if I repeat that request again:
$ curl -v -XDELETE "http://<IP_ADDRESS>:2379/pd/api/v1/schedulers/evict-leader-scheduler-6"
* About to connect() to <IP_ADDRESS> port 2379 (#0)
*   Trying <IP_ADDRESS>...
* Connected to <IP_ADDRESS> (<IP_ADDRESS>) port 2379 (#0)
> DELETE /pd/api/v1/schedulers/evict-leader-scheduler-6 HTTP/1.1
> User-Agent: curl/7.29.0
> Host: <IP_ADDRESS>:2379
> Accept: */*
>
< HTTP/1.1 500 Internal Server Error
< Access-Control-Allow-Headers: accept, content-type, authorization
< Access-Control-Allow-Methods: POST, GET, OPTIONS, PUT, DELETE
< Access-Control-Allow-Origin: *
< Content-Type: application/json; charset=UTF-8
< Date: Thu, 02 Apr 2020 09:50:25 GMT
< Content-Length: 22
<
"scheduler not found"
* Connection #0 to host <IP_ADDRESS> left intact

The status code is 500 Internal Server Error, which is not correct for this circumstance.

What did you expect to see?

Return 404 Not Found or 410 Gone would be the proper response, as we can see the body says scheduler not found, this is clearly supposed to be 404 Not Found.

What did you see instead?

It responses a 500 Internal Server Error status code with scheduler not found in the body.

What version of PD are you using (pd-server -V)?

Release Version: v4.0.0-beta.2
Git Commit Hash: 7ebad5232e7df681d8d425900f52984574539568
Git Branch: HEAD
UTC Build Time:  2020-03-18 01:25:40

https://github.com/pingcap/pd/blob/878ac7e830af195912603501d5d4de8ccffff232/server/api/scheduler.go#L263
PD will request another API internally
and seems it will always return  "scheduler not found"
so, when returning "scheduler not found", the user can't make sure it's truly "scheduler not found"


can't build the example-service??

can't build the example-service??

Hi! Can you share the steps to reproduce this build issue? (I tried a few ways that seem to be working on my machine)

Closing due to inactivity. Feel free to reopen with more details!


Java JDBI is first party not native

follow up for #5512:
based on the documentation (https://jdbi.org/#_opentelemetry_tracing) the otel integration requires a plugin and is not a native integration:

The OpenTelemetry project provides a SDK to implement distributed tracing. Installing the JdbiOpenTelemetryPlugin from the jdbi3-opentelemetry artifact will cause Jdbi statements to emit trace spans recording data similarly as JFR events above. Additionally, it will add the trace id to JFR events.

cc @jaydeluca

thanks for catching this! I wasn't sure about the distinction between first party and native, but from this im gathering, if any additional dependencies are needed (in this case, jdbi3-opentelemetry), then it would not be native?
Also, did you mean to open this PR against the primary site repo instead of https://github.com/svrnm/opentelemetry.io ?

thanks for catching this! I wasn't sure about the distinction between first party and native, but from this im gathering, if any additional dependencies are needed (in this case, jdbi3-opentelemetry), then it would not be native?

Yes, native would mean they take direct dependency on the opentelemetry api and a user can turn on otel by simply adding the SDK to their application

Also, did you mean to open this PR against the primary site repo instead of https://github.com/svrnm/opentelemetry.io ?

Yes... this happens when I edit the file in the UI and create the PR from there ;-)


feat: add Virtual Machine, VMSS and NetworkInterfaces

Add Azure compute related resources, VirtualMachine, VirtualMachineScaleSets, and NetworkInterfaces.
Todos:

[ ] add additional tests
[ ] add reasonable samples

Pull Request Test Coverage Report for Build 116213552

-143 of 169 (15.38%)  changed or added relevant lines in 6 files are covered.
1 unchanged line in 1 file lost coverage.
Overall coverage increased (+1.8%) to 30.9%

Changes Missing Coverage
Covered Lines
Changed/Added Lines
%

apis/microsoft.network/v1/conversion.go
0
1
0.0%

pkg/xform/xform.go
10
11
90.91%

apis/microsoft.network/v1/resources.go
0
6
0.0%

apis/microsoft.network/v1/zz_generated.deepcopy.go
0
135
0.0%

Files with Coverage Reduction
New Missed Lines
%

pkg/xform/xform.go
1
39.45%

Totals

Change from base Build 115126797:
1.8%

Covered Lines:
491

Relevant Lines:
1589

💛  - Coveralls


TypeError: Cannot read property 'sys' of undefined

I have this config file:
{
  "contentModelOnly": true,
  "sourceSpace": "12345678",
  "destinationSpace": "12345678",
  "sourceDeliveryToken": "12345678",
  "sourceManagementToken": "12345678",
  "destinationManagementToken": "12345678"
}

Which gives the following error:
sean@somepc:~$ contentful-space-sync --config config.json
> Contentful Space Sync:
Let's sync some content across spaces!
> No previous sync token found or fresh sync requested.
Synchronizing fresh content from 12345 to 12345
> Getting content from source space
ERR! Failed with
 TypeError: Cannot read property 'sys' of undefined
ERR! Failed with
     at new APIError (/usr/local/lib/node_modules/contentful-space-sync/node_modules/contentful-management/lib/api-error.js:10:20)
ERR! Failed with
     at /usr/local/lib/node_modules/contentful-space-sync/node_modules/contentful-management/index.js:128:13
ERR! Failed with
     at tryCatcher (/usr/local/lib/node_modules/contentful-space-sync/node_modules/questor/node_modules/bluebird/js/main/util.js:26:23)
ERR! Failed with
     at Promise._settlePromiseFromHandler (/usr/local/lib/node_modules/contentful-space-sync/node_modules/questor/node_modules/bluebird/js/main/promise.js:507:31)
ERR! Failed with
     at Promise._settlePromiseAt (/usr/local/lib/node_modules/contentful-space-sync/node_modules/questor/node_modules/bluebird/js/main/promise.js:581:18)
ERR! Failed with
     at Promise._settlePromises (/usr/local/lib/node_modules/contentful-space-sync/node_modules/questor/node_modules/bluebird/js/main/promise.js:697:14)
ERR! Failed with
     at Async._drainQueue (/usr/local/lib/node_modules/contentful-space-sync/node_modules/questor/node_modules/bluebird/js/main/async.js:123:16)
ERR! Failed with
     at Async._drainQueues (/usr/local/lib/node_modules/contentful-space-sync/node_modules/questor/node_modules/bluebird/js/main/async.js:133:10)
ERR! Failed with
     at Immediate.Async.drainQueues [as _onImmediate] (/usr/local/lib/node_modules/contentful-space-sync/node_modules/questor/node_modules/bluebird/js/main/async.js:15:14)
ERR! Failed with
     at processImmediate [as _immediateCallback] (timers.js:383:17)
ERR! Failed with
  [TypeError: Cannot read property 'sys' of undefined]

Which seems to be an error in the library

Hi there,
would you mind trying version 2.1.1 and seeing if the output is any different? I've released an updated version of contentful-management which improves error handling and should give you a better idea of what the actual problem is in this case.

Error is now: (not sure if it changed)
ERR! Failed with
 TypeError: Cannot read property 'sys' of undefined
ERR! Failed with
     at new APIError (/usr/local/lib/node_modules/contentful-space-sync/node_modules/contentful-management/lib/api-error.js:10:20)
ERR! Failed with
     at /usr/local/lib/node_modules/contentful-space-sync/node_modules/contentful-management/index.js:128:13
ERR! Failed with
     at tryCatcher (/usr/local/lib/node_modules/contentful-space-sync/node_modules/questor/node_modules/bluebird/js/main/util.js:26:23)
ERR! Failed with
     at Promise._settlePromiseFromHandler (/usr/local/lib/node_modules/contentful-space-sync/node_modules/questor/node_modules/bluebird/js/main/promise.js:507:31)
ERR! Failed with
     at Promise._settlePromiseAt (/usr/local/lib/node_modules/contentful-space-sync/node_modules/questor/node_modules/bluebird/js/main/promise.js:581:18)
ERR! Failed with
     at Promise._settlePromises (/usr/local/lib/node_modules/contentful-space-sync/node_modules/questor/node_modules/bluebird/js/main/promise.js:697:14)
ERR! Failed with
     at Async._drainQueue (/usr/local/lib/node_modules/contentful-space-sync/node_modules/questor/node_modules/bluebird/js/main/async.js:123:16)
ERR! Failed with
     at Async._drainQueues (/usr/local/lib/node_modules/contentful-space-sync/node_modules/questor/node_modules/bluebird/js/main/async.js:133:10)
ERR! Failed with
     at Immediate.Async.drainQueues [as _onImmediate] (/usr/local/lib/node_modules/contentful-space-sync/node_modules/questor/node_modules/bluebird/js/main/async.js:15:14)
ERR! Failed with
     at processImmediate [as _immediateCallback] (timers.js:383:17)
ERR! Failed with
  [TypeError: Cannot read property 'sys' of undefined]

That seems to be the same. Can you force the installation of contentful-space-sync 2.1.1 with npm install -g<EMAIL_ADDRESS>?
After that, check if the file /usr/local/lib/node_modules/contentful-space-sync/node_modules/contentful-management/package.json has a line "version": "0.6.0"

I forced the update, this results in the following:
ERR! Failed with
 OperationalError
ERR! Failed with
     at BufferList.<anonymous> (/usr/local/lib/node_modules/contentful-space-sync/node_modules/questor/index.js:40:25)
ERR! Failed with
     at BufferList.end (/usr/local/lib/node_modules/contentful-space-sync/node_modules/bl/bl.js:75:10)
ERR! Failed with
     at IncomingMessage.onend (_stream_readable.js:490:10)
ERR! Failed with
     at IncomingMessage.g (events.js:260:16)
ERR! Failed with
     at emitNone (events.js:72:20)
ERR! Failed with
     at IncomingMessage.emit (events.js:166:7)
ERR! Failed with
     at endReadableNT (_stream_readable.js:905:12)
ERR! Failed with
     at doNTCallback2 (node.js:450:9)
ERR! Failed with
     at process._tickCallback (node.js:364:17)
ERR! Failed with
  { [OperationalError]
ERR! Failed with
   cause: '',
ERR! Failed with
   body: '',
ERR! Failed with
   headers: { 'content-length': '0', connection: 'Close' },
ERR! Failed with
   status: 502,
ERR! Failed with
   request:
ERR! Failed with
    { method: 'GET',
ERR! Failed with
      uri: 'https://api.contentful.com:443/spaces/123456/entries?sys.id%5Bin%5D=IhzxcItOi2sOM0CK6uAkY%2C3C6FpCGAdOMciEU6MaOYqE%2C2w62rmGl7WugagUKiuyeq0%2C4RXLdEOfwsamceeiWKoE8I%2C3YYr9VlNPWYOk8i4EkuKoe%2C4Jc6IBfHUki08qyu28i44c%2C3Mcbmv2ZfWq2gagkMWYK0a%2C2P5iaMZW5G2co0is2meMko%2CEYJI1GeYWOUuUk0GqYecY%2C4Lr0B3mXq0E46kMqky0SCM%2C5H1mqdFrxuIgOEo4oIuyg8%2CK0OpLFcPi6WM6ieyMi8MC%2C3V4WsKOrkkaCOKEG8WIoAu%2C1ub27qLU0QQywy2s0mGO6a%2C4XmwmZMUyIIU2sck2wkMOy%2C53biRFHnYsEGOGGkmcgCaW%2C1aNnJbimmukS8IY8eiImuy%2CqVLMNDNahMmY6cWeMWS6k%2C564lCe54k02wsIOM2qcyOO%2C20pVHRLTriyauGsaK6CYkq%2C3rQLjRIWowia66kMiY4KOM%2C3qPGjs3zvySwwKoqaKYY2w%2C3d0MWdcuicwGAge4E4k2qU%2C5Tde8xQiYgA0mmy2wscS08%2C1VWedWELkkeWKSSy8a4Gq4%2C52mCFro3u8aaKoiCKOSg8A%2C4ks8juA7SEGamiIocgYi0w%2C2ZumIRJjpY4ayAOgOKqUCo%2C3uR00hDq8wsmc6CsI66IGg%2C5SJDMR94IMyKCukgScEi6g%2C24xyaqfsMAUmKgquCUsKcC%2CJf2oUrSLYYmw2GKMqQK8I%2C3caD3EmSGAQWqwAueMSc4y%2C34QOsUUyYgY8GEK6cagYQW%2C2Ll97DVFEceaUEueWQAeeQ%2C33FnKz6gR2Eo0SiA2yEOKc%2C7ol1HH2zleqsMM2CywqKmw%2CSiypGMqUsEueAO2e4aykg%2C1AJK9yn0LeOGG4IsgQWSGu%2C1EvlHZEgTaouKGqOkOmY6o%2C3qcjmcnLOgui4uicGuqmgC%2C1zTZnRp4GYY2syg6uQKW6C%2C5VTZfRS5Hiw4mucuu0UcW8%2C7ugvIDdmQE6UA2OKKWwomq%2C6ht0e0cjTigm8aSyIm2sae%2C7qsnamcQvK0WUE0Eogemu%2C45xfub7BocmUSumUKkkkmk%2C1Kkz7MIvGMIOgyaSAucMeE%2C1BzWj5qfBeYi462OwcMEQ6%2C38GcDp6SoU6ok2uEwkq8Ss%2C5KhXskc8CsgykeEw0EUASq%2C8mImMlW4EMK40EwO6Qguu%2C3vHICSkah2cYAouUGMesWE%2C6bAzegWVKoAYW0CQquSYkc%2C1Bq9lXZPwkgwAcYMgcsoe2%2C1BSGRQD6Ju8kYMoSWcCC4i%2C14iBOrTKHC0UuMsas4oU48%2C2h8XzWyCx6oCmMc42Siqao%2C4Np4TKBHLGoIQmI2qicSQY%2C3ui9rQUkasOkECAIGCuSsc%2C6JMCN2PKDYwuqqqaiUgI8s%2C4ZHlo3GXmguWsg2E2e8CMY%2C3xH9UYxpxKgq8OaekmE4Cg%2C5DqNJZNvtSay08KmgQsmuq%2C4ihVijg6OkSqSauEgm4suK%2C2yFXlcKJEESu82oCyACAAa%2C6mcIaDqGf6qeCysWUkam4o%2C1kHhqzTp7iYS4uqO08YauQ%2CoWP7sY69QO0qGUMeEGkuE%2C1Pmy5fHDYQsCQk8IWoOmWc%2C3jfUf78Fos6g86cCs8UWqK%2C41nLu1vy0UQGwYMCimCwiq%2C5YIHI1Og36cqIEQYKm8sgy%2C3waXfCKdv28I8aoamIE4eG%2C4IiuDzCXKoci2wK46I8uIm%2C4SmVNLEmLmUIao2oaaMGa%2C1rzdLeZPVi8YG6gIIiMYAg%2C3G3JBUJdNum8Wwccgc8AUW%2C14HtF804SqYKykYI4c4mOe%2C6OJA6zBGYogyyI8emYOucw%2C3mi0Nlx6ZW4KogkIE6OWAM%2C2kCon7Ipeow0K0g0WEe086%2C2GUqMcdCXYOcka20I4eMwm%2C4oQupSG1moYE0ScuKueima%2C6vc6kLFU5yeM8gsYkKueK2%2Ccfea7iE1DGw46KSIIkIM8%2CrxCX9i24vY2sGcIGwEakA%2C37YMmqw4dyOiOc4AqSa2ek%2Chg1Zj82x0c6uwiUwqwm88%2C3orQdYooqsamIiWSWmu4YY%2C6xJCRJUlq0wame4aUAewyO%2C5QsnS6DxZeSaYueUWCmu4e%2C47sGYzzekwkWAoKiCA8gsO%2C6mEJN8NOFyuO8m6Imw668I%2C3h9bqCtoUUUCcI42k0isCq%2C4i4pDVbWvCikIcceoE446i%2C53nJotV7NCeKOUK8wWOWEs%2C4A7F8Ud6A8qCMMC4QAao8k%2C1pnPKNV0p2IYIK6g0mkskY%2C5ME6IgdKQEgWmYWYkceKiQ%2C20VVHdDXPGeEikA2m2oUIy%2C52ZanSwMC4SeeY8Qea6isI%2CqHHBtELPfEEouqK6ausGe%2C7b9dZnkK5iKIW20Ce02S8u%2C4GoxqmIPjy8CoS6qK4IEqs%2C6PlFQkXnBSiwAaS22qiC2q%2C11slRzofn66gsQ24meic8s%2Ci4reDLZr8W8cOoeO0KiWE%2C2ewZGkzDOguyQqcy0aQQWO%2C2Y4IKEtTYQCU0gGQw4isKM%2C3s9WGHRiyISYWYcsayGMwk%2CztUOHidsnAWkmGaEk8EIc%2C3co3FrSc7eoS66oqCYC0iY%2C4uJWHirGkw8ugcieS4iIUG%2C6ldfqYc97GQKiUS0u8Ye4S%2C1crIorNTcMUuMsYEuk6QSa%2C1MvN5xmrfWc6qiiQaegOoa%2C7vpTcFR4KQwwIymuy4wyM2%2C5m0HZO4DC0uEIsOAwSoM0M%2C6RruOcsOYwwsu6e4EkOUk2%2C2OSxsrewIEA4mS2A6QqGkE%2C1GDqiRtj0oo2GGMI8maoeW%2C5Lzl5DABDGaeU0e4UiAAUc%2C2n8nu2cZUgAW0Qwyeo0IU4%2C4FTxxTZD4QukIUAYQ8Iw4u%2C5XaThtn2cocuG2oUkwGgYe%2C2T9ZA5TqascGEC8G8yUMM2%2C78siUVxNqoioMeyky8IeaK%2C6hiBiH6xkA0OWQKE2mKesa%2C67ZYPHUTfiUkoM62iqUUku%2CQxhz4kdDiKCSy8ME0So0I%2CXjvL7AR3mEGywSOKMMmKQ%2Ctp1jSWnmbmomWm4qqM2ka%2CUSjueY1F4GkGKmkcUWWqC%2C19SpCMAmAIUsU88Q880scG%2C6uDL8WsDZY644iIymOCk6U%2C1EPItusMA8YQAAsGUYugUS%2C1UH29roXYMCAC4mC86K4Sw%2C16aWOmOoEUMWIQgc4ik0oE%2C683doV5tGEKWuiiC2kkGSs%2Ctg8KSucJCokkKSaq0eC44%2CcJab2XeRe8Kw8s08EQs8k%2C5vgePCSTL2w4YkCY2KEUU6%2C4Moj0BfLzWgu6miQKmKYCW%2C2Lz75bCKVaqsUOMCQQKw2E%2C7uhqKZ1HMI8gWo6cokyIaq%2C6yjbF4YeUo6smqY8oqM6i2%2C4tCT0DZ0FqAwomWYq6Uii6%2C2WiMN1rjE4MI20OyyGUCYI%2Ck8eqtcktigY80UASmmo6G%2C6c4Dlx1JQIaIiQimQAawEi%2C2FTnchzmLuosYIos4SOkoQ%2C5v0fWz3ou44WGsqOiOasG8%2C42YYmmT6eIouYEOqssWQ0Y%2C5znWqI12sEmiIKqYqemUc0%2C6ADFiXNjc4YE4eoKOyiY80%2C5sGCXTzjQkCQKQYowsmuGA%2CCsWBgIzYkgGWQqIoU0Ymg%2C3ixzKJqS3YwSGMWgk88MmI%2C4N63SHgMI8UQaUCGIs6wEW%2C71YLBcdvhuiSKiAUc0602G%2C4QXtniHyikUwYumA6gwWuO%2C6irrJyIn5KMm4QYuaumWwS%2C69SVmsaisw2qaQo6SOWe4g%2C6dbvAMr3y0iIgAq6QwEWCY%2C4r1r3aExJeG0gAokYg6CIO%2C6CSDQqYMqAi8mQ2sgikYO0%2C4a07iPQct2kKoEcoIeQYEw%2C5cztaA1GRG6AUE8WoYogAQ%2C68XBNg1nagmoggaS6yKqWK%2C1UVVBwPYAQ6YSeWcUMSWao%2C1eZ02NUArOUUcGEQUKAwyy%2CkrU0Jo9G48kuysOOIm2m%2C3taLxU7H9CeuAAis0keIYi%2C5i6sAiYBcWOYIyoqicSqIa%2CRrlzLsua0EGeEcaAwYKac%2C3TRtN2h1p6ACGmsqS8s6aI%2C32AsXf2Gg0gMeYgwO8mQcq%2C3qMFk7BTdm2IoMO20WOGqs%2C2omYa7iFEEUQegO8ySEQc6%2C39Ppr4vEn6YoMAK6eMEGKk%2C2jnvFbD1fC6y40IUoMqsuW%2C3c5b5g63kkmueOiO8Awasy%2C7H3F8OqwKsIakICmum6uYy%2C5xL4bv8pwI2SSo6g6w4cq6%2C3ROP9Mjy6QO6i6SqAek2UY%2C6wrE6QL2g0C6m4c00gEKWE%2C6y3VJNU8Dewuk2CsO8WIGs%2Czs8b5PQSgo6KysiyisOow%2C1oOpKoCqCgS0MWM8iYWAYi%2C3y9kW2q2NOa0aOqQC8kUSK%2C4hbbh3svaUoaCus2koiCem%2C1maArAnoqQgyamymWyi6Is%2C4YvF2OYpZS22QWcqY868AG%2C6J1etn71Pa0MSqSkmaoC44%2CJwdHoV4XGUKkKCKIIgGq0%2C3js668cmiIWI6mOMUQmMIC%2C6n5OOB9VYWy406YgK0u26K%2C7hN9spX49G6GECsacwCGEk%2C27MVcZeataIgU0Y2AaWKEC%2C3WU5hPvNdKCEGEyaKm4MoO%2C3xeMusKGLeaOaIoaWagui0%2C7p9efKKN8WOA8AkMQmeuc4%2C3WQI01nIgU2aiyOsqmw8qE%2C1k4etnjp54Icsiko60eSyM%2C1GbSQkhBcQewicCgOYU8Eo%2C1Pbk4HqOt2y8ISg6Sc4oGk%2C2zsztpEmQA8uc2EW6U8W6K%2C1faw8Dc20KcIU2ie8wKUY0%2C514GYMohcIEYGeGey6i6ke%2C54sN3aOFSgGoeYoAWmw2gW%2C3dR5eO9nckyScyyIe6Aa2o%2C4WzVH5lhcc4aQs8a6wIqcA%2C5J6dVQfdzUoQ00KCEQMmEY%2C74nnczzJq8ewUqcyUyc268%2C4ySlo5gWo8sOyCOYYQmSAy%2C2FOe3TcVGoQuyuuCqoCUC6%2CvlwXMC4zVAkO60y4guSig%2CpPuMRlT4beG2aw6UYOiuY%2C38tXzOq3oIOUecqyWKwIam%2C2NumKqbCU0yQQY8Y2UamEs%2C5yMsMXOtQ4iyOyO4iiuSWa%2C2ai62L2gyE48WU2caWsKq8%2C39c1FswSDukSoSQ6gS6uiW%2C2d0Hh3A888c6gASQE8koom%2C3mws0McTqoYeKewCIcmk6e%2C2UHfh7XlKg4QCc0scYksKm%2C52a0PiirfGmAUEKA8uQoUk%2C4oUDGfbMyc2ggYU6IuEicw%2C6lyChXIlY4oAqIMEWo6GC2%2C2wkN09yddyWQOIaEk0osOU%2C27oTINcZkQuCQgiIKsGwOI%2C14syhAYDmGQMcYaSCKoKwE%2C1MCej1J10YgWeGIyaQ6eIc%2C5VAKP8da6ce08gm8cCuCEM%2C6ubWBeoYWkosYse6QWCQiE%2C3WJ24lZBIkOAWwwumWWUOG%2C4rNy7VHIjYUwMqoE8qEKGU%2C1LcSf37xNKiYmKQCQaYM0o%2CJ4mPlhq2MSeaos0aEI6W0%2C2lDrjchxZSMOKewiuOia8g%2CyG3w6jC9peM4ue2oWwQ8k%2C665Pj7nzz2aO0kEwi6m6qy%2Chskla3esMguKuO8U6MOoc%2C4CC4q5PIxGAI6Uso0mAOKw%2C34mRFsmGic4Gg6WOuCOCMU%2C3jI27TSEPmCqEwkgqokeMa%2C5sD2dlWhUWGiaGCIqQoWoS%2C3TxZRUdEAUc0YaMyOOicMK%2C2sjAhYlZzmSSEWEUgqAuu2%2C43MRVa9LVSOu82gwAQOq8w%2C2uIt9dwrc8kqqou4SMgySy%2C54BT0YCZCowq8WSakm2Us6%2C6E7RyoVJ3qc0CK4WMaQk0e%2C6kTLwbfCaAaI0yA20MsgQU%2C27RNrv8NBeIy8Amo2eAii6%2CBJZ7qJ96i4K2CauwKGmkG%2C18EST6bO5mSUimyiY08OQi%2C2S3e8ziws0eiuouo4u40c8%2C2XARpJKs0M2E4SGSQc8kyQ%2C1WNYnvwpqkAW8cC02uOwMI%2CnhmSFjdhgOOyCew4UOAM2%2C3TNQ0stmmkM6Eqmw6GigkQ%2C6Iq6WgFhq8oWIMK68SyAkS%2C72CXOlzNSwq6wQgiU2IiYw%2Cj1LLXbBKLY8UUCCuY80Ei%2C2MV9gJSD0sEewMKO2qOwgU%2C4boV1dqHq82EuQAqE8Wkmo%2C4qIYYbj6FWI2CeSYKc2Asg%2C5vyQVIh6G4QkQseISe04SS%2C2jyTfK7oQ00IME6Qk8AC2Y%2CpCjI0pqcmWqOocWEgq4iG%2C1AfznyvZE8GeOUgWssQS6Y%2C3uEYF13kRWOGcSKegsaUka%2C23dJ2TujiIA8W86Giu4U0m%2CYrDkxmUyaceEEMGUiImmo%2C1fVqJ8k1FO62AsccI0oOmi%2C2nS1AANWBe6AKeSqIAQWEO%2C4yt1VEfEzKukig0GmsScQg%2C2wUyzvVJzqwi4EWyaiiIga%2C20XQzS3hM0kuwQMYOgcSI6%2C399gNz5fDqy2seSMYok6QQ%2C5SH6RZCgWACucY4SU4OQa4%2C474TTYdoXeus2QewwoYM4g%2C6uwDfTE4wguiaCIcUaysS4%2C1CXpTw5nQgWug2CwQU4sIC%2C64z9DBMBqMcIS4SaUui6YS%2C6gpyOT1idUo0OmYiCeaYCu%2C2M7RzeFmY8YGesGWA4gWcM%2C5Q1nCG7NnikqM8eUIeisCm%2ClyCsTy2EtEaIyKUusaAky%2C6qi35PItOwaIcgg6A6aIo2%2C6rOAZHZJ60AaymCQgeceIS%2C2MqIPlKHRewecKYoo2o8oC%2C3WSiBALoE0GUuWMqgOkuEs%2C6yJ08qUA1iOyqg8y0QMuI8%2C230oJDPSgcSG6cceQqQiUC%2C5p5bBRrAKkYU8oEEiWYS8u%2C28E88IfMVK8MmMEKyiMGA8%2C5suA882uwEIuSkOiCmESwU%2C1fHfp9dKlQkYGSmMOEqCEs%2C5yVaQyQt44CQcgGW2CYqIK%2C5HR1SewNwcmu4o2UG2syaO%2C5X7xR2ks7ueoko2UCqQKI2%2C2W061cDOCs08M6U6siyMuk%2C5B9SOaAtdC0oSIMs2qsoWU%2C3tSPQlsabeoaCGgM6Ae6k4%2C5i2drpcYVyuqc66AUmoUmi%2C2ZoY1YIWUgcewww6yq06AG%2C5Sgstu32g0wUsk8mQ8eW04%2C13aS6CaTvS62mKKuoUUs04%2C1OQBjPsFTKAYge4owsUkMg%2C2NJEdyNdnaka2GKwIE6KOy%2C3tFsIHObviYcisUk8yuUow%2C2JkttozSx2YoqUYK4Ck8U0%2C4E0E3UnQ9y8OGMGMKKMAuc%2C2qxIB5xJjmouyuwSUwk2mI%2CwRPZ0dlP0cWQUQmsKyemM%2C6xZNXL3EfCUcSgCeCckiaI%2C6TLdSZdW8wQScIGEQiYQCm%2C2LClLady6cUoqsAA0mawkA%2C5pSxoHSv6gO0gCqm6QYyaE%2C4zT7lREbgksW2YsQ0iiQ48%2C6bqJvC4ImcqC0UCi2YoEA6%2C6MS1pPc3ZKKEAYCso4iUo4%2C6vYP1kLx16y4sUaGAEi2OG%2C2XcyJMkvBCIam4IS4EwKao%2C4mX4T8W3DyuOQAMGmcKwCs%2C2vQRnlN3O8IKuQomkKuyKK%2C5muJ72X3ocEsiiSSkKIquM%2C26mLDpr10kyOmGygQ6MGWq%2C7gidBUwcogyu2osC0IIqU%2CTw5jPgarYW60qMEA0g8mI%2C61GwuEWukMCWqgsWwgAgUk%2CNNgrmR2O0SMAQ2CQQymCa%2C6N6cuR6tBCKswMOO0MMgiy%2C5w9qLgTiJUo6KIoUWWEUQ2%2C2iM2wtKxFGSwWuwOEqCa8c%2C6g8rpVHjy06yC0YYwaES8S%2C5wlA4ilOH6GqqGUc260M2E%2CSnT8RWNvu66kEIiqKuWYy%2C6gzkwMgw1iI4s8CewAGG4e%2C1QYmBZFSY4kMSK2eag6KcK%2C5Z53R219ZKswCCAgyQmWq6%2C1R0M0Hoaj2Gqm6w6Cy6OAK%2C6eCjPz9i5UWaQWgaYOEaiq%2C3ZkI2RM0o8mYIC4WqEckqm%2C26Of8yHFWEY88OgakM4Uoc%2C549AHjZNks2Wu6OsigGkoy%2CBIxfv4cfUO0O0IsIIm0WW%2C3LobuNhWy4qkm8MsWKCa4w%2C1ZmNB3ylMUmIsuwQcuWeum%2CtcUElPWHugqIYAEGkwc8Y%2C1VbDegWLBu0muKeaGwEe6k%2C3ySaF8U8ysEoCI4WyyII0K&access_token=123456',
ERR! Failed with
      body: undefined } }

Ok, this is very helpful. Essentially what's happening here is that when you are synchronizing to a space where you've synchronized before, the script will try to check if any content already exists there to use the appropriate versions when updating.
However, the request for the existing content should be split in multiple requests due to limitations in the size of URLs accepted by servers. In this particular situation it looks like the requests aren't being split properly.
I'll investigate this and come up with a bugfix soon as this is a major issue.
Thank you for the report!

Hi there,
version 2.1.2 was just released which should fix this problem.
Let me know how it works out.

It seems to work now (still can't sync but for different reasons)


add a unix socket example

I think this example would save time for new users, like me

Thanks for the PR!
My feeling here is that it's very similar to existing example, and so I don't think it teaches enough to overcome the time/cost of compiling it when checking tests. It would be a fair blog post, or similar, if you'd like to publish it :)

I understand (I did cut&paste), but really I spent a morning for just 3 lines to replace.
Also hyperlocal is a project that exists just for that, and now it is obsoleted.
Does it makes sense to add comments in hello.rs for the unix socket version?

#3443 like this, maybe?


Practicality of the model on 64x64 images

Thank you for your work. I benefit a lot from it. I now want to train with 64x64 or smaller images. Does this model work? Do I need to change the channel_multiplier module in the Unet module? It means a lot to me. I appreciate it.

Thank you for your answer, it is very helpful to me

Hello my teacher, happy Teachers' Day. I am a novice who is learning the diffusion model, I am familiar with the theory, but I am still not clear about the code, I would like to know why I need to set three numbers for "t",such as "t": [50,150,400], I wonder if it is OK to set only one? I understand that t is to solve for x_t but why do I have to solve for x_t at three times at the same time?

@YluHuu Hey, thanks for your question. The t denotes the amount of noise added to the clean image (i.e., noise variance). When extracting the features for the change detection task, we found out that, extracting features corresponds to multiple noise levels such as 50, 150, 400 improves the performance. Please check the ablation study on how the performance varies when you combine features corresponds to different noise levels. Although, you can use features corresponds to one noise level, the performance can be enhanced by combining multiple.

Thank you very much. I will try my best to continue my study and I hope I can get your guidance later😜😁

@YluHuu Np.

Hello, when I read your code, I was a little confused about the Unet part. I think this code should be  Encoder + Decoder 's channels ?
ups.append(ResnetBlocWithAttn(
                    pre_channel+feat_channels.pop(), channel_mult, noise_level_emb_dim=noise_level_channel, norm_groups=norm_groups,
                        dropout=dropout, with_attn=use_attn))
                pre_channel = channel_mult

In addition, I would also like to know where the skip connections are made in the code and where the Encoder is used to add the feature graph of the Decoder

Hello, I would like to know why only transformer parameters need to be updated when fine-tuning ddpm?
``


libbpf integration failed

What happened?
There is frequent failure in integration test of libbpf: https://github.com/sustainable-computing-io/kepler/actions/workflows/integration_test_libbpf.yml at healthz logic.
I also did the integration test locally here https://github.com/sunya-ch/kepler/actions/runs/5598480773 but not sure what makes difference.
What did you expect to happen?
pass the test
How can we reproduce it (as minimally and precisely as possible)?
run integration test
Anything else we need to know?
No response
Kepler image tag

Kubernetes version

$ kubectl version
# paste output here

Cloud provider or bare metal

OS version

# On Linux:
$ cat /etc/os-release
# paste output here
$ uname -a
# paste output here

# On Windows:
C:\> wmic os get Caption, Version, BuildNumber, OSArchitecture
# paste output here

Install tools

Kepler deployment config

For on kubernetes:
$ KEPLER_NAMESPACE=kepler

# provide kepler configmap
$ kubectl get configmap kepler-cfm -n ${KEPLER_NAMESPACE}
# paste output here

# provide kepler deployment description
$ kubectl describe deployment kepler-exporter -n ${KEPLER_NAMESPACE}

For standalone:
put your Kepler command argument here

Container runtime (CRI) and version (if applicable)

Related plugins (CNI, CSI, ...) and versions (if applicable)

@sunya-ch , I find that your local forked repo's GHA ymls are too old, could you please rebase to kepler:main  latest and have more tries?
Also, please look at the latest run pass job which triggered by @rootfs's new PR.
Let's check if such failure jobs were transient ones?

this looks no more an issue now


chore: Upgrade pylint to 2.5.3 and fix most new rules

SUMMARY
Bumps pylint to 2.5.3, fixes most of the new checks, disables one new check that would keep us from working around circular import issues that we have right now.
BEFORE/AFTER SCREENSHOTS OR ANIMATED GIF
n/a
TEST PLAN
Rely on CI
ADDITIONAL INFORMATION

[ ] Has associated issue:
[ ] Changes UI
[ ] Requires DB Migration.
[ ] Confirm DB Migration upgrade and downgrade tested.
[ ] Introduces new feature or API
[ ] Removes existing feature or API

Reviewers
@john-bodley @mistercrunch @craig-rueda

Woohoo! Clean build!

Squeaky clean build!

Mergin' before it conflicts with any of the 40 files it touches!

Thanks @willbarrett. So happy to see people chipping away at fixing pylint.


Provide 'DOMContentLoaded' option for waitUntil in page.goto()

It would be a faster variant for cases when the DOM tree suffices to retrieve some info.
https://developer.mozilla.org/en-US/docs/Web/Events/DOMContentLoaded

+1, this would be great. When parallel crawling some resource-heavy pages, waiting for a full load event before starting navigation significantly slows down the rate of the crawler.

@aslushnikov Is PR accepted for this?

Not seems to work : UnhandledPromiseRejectionWarning: Unhandled promise rejection (rejection id: 2): AssertionError [ERR_ASSERTION]: Unknown value for options.waitUntil: domcontentloaded

@gregoirepuget It is available only from the v0.13.0-alpha. You can upgrade by npm install https://github.com/GoogleChrome/puppeteer/


Add TernaryToNullCoalescingFixer

Change wherever possible ternary operator to null coalescing operator:
// Before
$x = isset($var) ? $var : 'ops';
// After
$x = $var ?? 'ops';

It reduces code duplication, so it should always be favoured.
The fixer is marked as risky because of this example (https://3v4l.org/Fjddg):
// Before
$a = 1;
var_dump(isset($a) ? $a : isset($b) ? $b : false);
// Outputs:
// Notice: Undefined variable: b
// NULL

// After
$a = 1;
var_dump($a ?? $b ?? false);
// Outputs:
// int(1)

This only occurs if you concatenate ternary operator without parenthesis, that is an insane usage of ternary operator: the fixer actually fix the code to the right intent, but has to be marked as risky.

The fixer is marked as risky because of this example (https://3v4l.org/Fjddg):

It shouldn't be risky because it's broken. That's not what risky means.
This fixer should correct any precedence issues it causes with brackets.

This fixer should correct any precedence issues it causes with brackets.

I'm sorry I don't understand, how
isset($a) ? $a : isset($b) ? $b : false

should be fixed?

Yeh, sure it should?

isset($a) ? $a : isset($b) ? $b : false needs fixing to $a ?? isset($b) ? $b : false, but no further, since this is evaluated as:
($a ?? isset($b)) ? $b : false
since the original was evaluated as:
(isset($a) ? $a : isset($b)) ? $b : false.

Fixing to $a ?? $b ?? false would be wrong since that's not the same at all.

Ok, I excluded any subsequent ternary operator that isn't rounded by parenthesis, so the risky flag can be removed.
Thanks for the hint

Comments preserved, logics simplified.

@GrahamCampbell , could you please play with a fixer and make a full review ?

Will do at the weekend. I'll try my best to break it. :trollface:

ping @GrahamCampbell did you find time to test?

$x = isset($a[yield]) ? $a[yield] : null;

is not valid PHP ;) please use https://3v4l.org/TDKnd (note that HHVM is acting diff. :/ )

complete example: https://3v4l.org/KsmSR

All higher precedence operators are now excluded because the behaviour actually changes:
$x = $a && isset($b) ? $b : null;
$x = $a & isset($b) ? $b : null;
$x = ! isset($a) ? $a : null;
$x = false === isset($a) ? $a : 2;
$x = 4 * isset($a) ? $a : 2;
$x = 3 ** isset($a) ? $a : 2;
$x = 1 | isset($a) ? $a : 2;
$x = (array) isset($a) ? $a : 2; // That's OMG, but cannot be touched

Also every changing code and function calls are excluded:
$x = isset($a[++$i]) ? $a[++$i] : null;
$x = function(){isset($a[yield]) ? $a[yield] : null;};
$x = isset($a[foo()]) ? $a[foo()] : null;
$x = isset($a[$callback()]) ? $a[$callback()] : null;

NOTE: what I don't like in my implementations for this cases is that I had to check everything "manually", i.e. explicitly writing T_ARRAY_CAST, T_BOOLEAN_AND etc.
I tried to use TokensAnalyzer without success.
If you have better ideas and/or cases ping me.

@keradus squashed and rebased. Is there a chance of seeing this on v2.0.0 milestone?

is it still risky ? or riskness has been fixed ?
we need anyone first to review it (cc @GrahamCampbell @gharlan , maybe you could ?)

Riskness has been fixed

Great to hear ;)

@SpacePossum I'm periodically rebasing this PR, and now I'm getting this error in tests:
1) PhpCsFixer\Tests\FixerFactoryTest::testShortFixerDefinition
Not valid amount of fixers using ShortFixerDefinition.
If this test is failing it means one of those scenario occurred:
- you introduced new Fixer using `ShortFixerDefinition`, you should use `FixerDefinition` instead,
- you update the Fixer to stop using `ShortFixerDefinition`, you should decrease the guard value.

Can you help me to fix it? I can't figure out how 😕

Hi,
For new fixers we added test to make sure these come with a description.
You might want to add some like this to your fixer:
    /**
     * {@inheritdoc}
     */
    public function getDefinition()
    {
        return new FixerDefinition(
            'Use null coalescing operator (??) wherever possible.',
            array(
                new VersionSpecificCodeSample(
                    "<?php\n\$sample = isset(\$a) ? \$a : \$b;",
                    new VersionSpecification(70000)
                ),
            )
        );
    }

includes are like:
use PhpCsFixer\FixerDefinition\FixerDefinition;
use PhpCsFixer\FixerDefinition\VersionSpecification;
use PhpCsFixer\FixerDefinition\VersionSpecificCodeSample;

Thank you, now everything is ok.
Note: there is no evidence on https://github.com/FriendsOfPHP/PHP-CS-Fixer/blob/v2.0.0/doc/COOKBOOK-FIXERS.md about the new getDefinition() method and the FixerDefinition class, it would be very usefull for newcomers.

Ah yes, the always the docs ^_^
https://github.com/FriendsOfPHP/PHP-CS-Fixer/issues/2404

Backtick operator adopted in the description.

@Slamdunk please see https://github.com/Slamdunk/PHP-CS-Fixer/pull/2

Thank you @Slamdunk.

Thank you @SpacePossum


Update CLI extensions available doc

Update CLI extensions available doc.
Triggered by Azure/azure-cli-extensions - TRAVIS_BUILD_ID=445240212
https://github.com/Azure/azure-cli-extensions/commit/13a6af2eecae99fad02b2490538a94607e41653b

:white_check_mark: Validation status: passed

File
Status
Preview URL
Details

docs-ref-conceptual/azure-cli-extensions-list.md
:white_check_mark:Succeeded
View (azure-cli-latest) View (azure-cli-2017-03-09-profile) View (azure-cli-2018-03-01-hybrid)

For more details, please refer to the build report.
Note: If you changed an existing file name or deleted a file, broken links in other files to the deleted or renamed file are listed only in the full build report.


Support for Eastern European characters

Hello,
Everything works as it should, the problem is Postie does not handle Easter European characters well. I tried using local characters in both template.txt and recipient.txt files, both time Postie prints out errors.
As soon as it finds some unknown character, it generates an error (see below). Could it be Postie's problem or my own? I'm running this on Raspbian (derivative of Debian - for Raspberry Pi).
Errors :
Traceback (most recent call last):
File "/usr/local/bin/postie", line 9, in
load_entry_point('py-postie==0.1', 'console_scripts', 'postie')()
File "/usr/local/lib/python2.7/dist-packages/postie/cli.py", line 60, in main
p.run()
File "/usr/local/lib/python2.7/dist-packages/postie/main.py", line 141, in run
self.send_emails()
File "/usr/local/lib/python2.7/dist-packages/postie/main.py", line 127, in send_emails
content=self.construct_msg(row))
File "/usr/local/lib/python2.7/dist-packages/postie/main.py", line 109, in construct_msg
return self.render_template(**template_kwargs)
File "/usr/local/lib/python2.7/dist-packages/postie/main.py", line 91, in render_template
template = Template(fp.read().strip())
File "/usr/local/lib/python2.7/dist-packages/jinja2/environment.py", line 906, in new
return env.from_string(source, template_class=cls)
File "/usr/local/lib/python2.7/dist-packages/jinja2/environment.py", line 841, in from_string
return cls.from_code(self, self.compile(source), globals, None)
File "/usr/local/lib/python2.7/dist-packages/jinja2/environment.py", line 540, in compile
source = self._parse(source, name, filename)
File "/usr/local/lib/python2.7/dist-packages/jinja2/environment.py", line 459, in _parse
return Parser(self, source, name, encode_filename(filename)).parse()
File "/usr/local/lib/python2.7/dist-packages/jinja2/parser.py", line 32, in init
self.stream = environment._tokenize(source, name, filename, state)
File "/usr/local/lib/python2.7/dist-packages/jinja2/environment.py", line 490, in _tokenize
source = self.preprocess(source, name, filename)
File "/usr/local/lib/python2.7/dist-packages/jinja2/environment.py", line 484, in preprocess
self.iter_extensions(), text_type(source))
UnicodeDecodeError: 'ascii' codec can't decode byte 0xc4 in position 37: ordinal not in range(128)
Best regards,
B. Kosir

What character are you using?

I'm trying to use ščž ŠČŽ.

Alright @k0b0 . I am working on it


[Game Request] Albion Online

The download link can be found here. The game already has a linux version.

I'm not familiar with the game. Maybe you could try to write a derivation for it and PR?


Problems trying to convert the sg2 model

I am currently trying to run the program and I need to convert the ffhq.pkl model do a .pt one.
When I enter python stylegan_nada\convert_weight.py --repo stylegan_ada --gen models/ffhq.pkl it says:
Traceback (most recent call last):   File "stylegan_nada\convert_weight.py", line 11, in <module>     from ZSSGAN.model.sg2_model import Generator, Discriminator   File "C:\Users\msk4x\Documents\Projekte\stylegan\stylegan_nada\ZSSGAN\sg2_model.py", line 11, in <module>     from op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d, conv2d_gradfix
How can I fix this?

Hi,
For a quick workaround you can just use the colab notebook and convert the FFHQ model using the script there. The result will be in the models folder.
That said, you seem to be failing on the part that imports some of StyleGAN2's compiled cuda ops - this might be a problem when you try to actually train the model as well. Can you post the full error so I can see what exactly fails?

Yeah thought of the colab workaround already, takes ages to download tho.
I will post another comment after I tried to train it.

Umm now I get this error...
C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\clip-1.0-py3.6.egg\clip\clip.py:109: UserWarning: File C:\Users\msk4x/.cache/clip\ViT-B-32.pt is not a JIT archive. Loading as a state dict instead
warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
Traceback (most recent call last):
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\clip-1.0-py3.6.egg\clip\clip.py", line 104, in load
model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval()
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\jit_init_.py", line 275, in load
cpp_module = torch._C.import_ir_module(cu, f, map_location, _extra_files)
RuntimeError:
aten::_convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled) -> (Tensor):
Expected at most 12 arguments but found 13 positional arguments.
:
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py(420): _conv_forward
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py(423): forward
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py(709): _slow_forward
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py(725): _call_impl
/root/workspace/multimodal-pytorch/multimodal/model/multimodal_transformer.py(85): forward
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py(709): _slow_forward
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py(725): _call_impl
/root/workspace/multimodal-pytorch/multimodal/model/multimodal_transformer.py(221): visual_forward
/opt/conda/lib/python3.7/site-packages/torch/jit/_trace.py(940): trace_module
(36): export_torchscript_models
(3):
/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py(3418): run_code
/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py(3338): run_ast_nodes
/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py(3147): run_cell_async
/opt/conda/lib/python3.7/site-packages/IPython/core/async_helpers.py(68): _pseudo_sync_runner
/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py(2923): _run_cell
/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py(2878): run_cell
/opt/conda/lib/python3.7/site-packages/IPython/terminal/interactiveshell.py(555): interact
/opt/conda/lib/python3.7/site-packages/IPython/terminal/interactiveshell.py(564): mainloop
/opt/conda/lib/python3.7/site-packages/IPython/terminal/ipapp.py(356): start
/opt/conda/lib/python3.7/site-packages/traitlets/config/application.py(845): launch_instance
/opt/conda/lib/python3.7/site-packages/IPython/init.py(126): start_ipython
/opt/conda/bin/ipython(8):
Serialized   File "code/torch/torch/nn/modules/conv/___torch_mangle_9366.py", line 8
def forward(self: torch.torch.nn.modules.conv.___torch_mangle_9366.Conv2d,
input: Tensor) -> Tensor:
x = torch._convolution(input, self.weight, None, [32, 32], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
~~~~~~~~~~~~~~~~~~ <--- HERE
return x
def forward1(self: torch.torch.nn.modules.conv.___torch_mangle_9366.Conv2d,
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "stylegan_nada.py", line 166, in
net = ZSSGAN(args)
File "stylegan_nada\ZSSGAN\model\ZSSGAN.py", line 174, in init
for model_name in args.clip_models}
File "stylegan_nada\ZSSGAN\model\ZSSGAN.py", line 174, in
for model_name in args.clip_models}
File "stylegan_nada\ZSSGAN\criteria\clip_loss.py", line 36, in init
self.model, clip_preprocess = clip.load(clip_model, device=self.device)
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\clip-1.0-py3.6.egg\clip\clip.py", line 111, in load
state_dict = torch.load(model_path, map_location="cpu")
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\serialization.py", line 584, in load
return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\serialization.py", line 842, in _load
result = unpickler.load()
ModuleNotFoundError: No module named 'torch'

After downloading the .pt model from colab I now get this error:
`Loading base models...
100%|███████████████████████████████████████| 354M/354M [00:36<00:00, 9.79MiB/s]
C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\clip-1.0-py3.6.egg\clip\clip.py:109: UserWarning: File C:\Users\msk4x/.cache/clip\ViT-B-32.pt is not a JIT archive. Loading as a state dict instead
warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
Traceback (most recent call last):
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\clip-1.0-py3.6.egg\clip\clip.py", line 104, in load
model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval()
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\jit_init_.py", line 275, in load
cpp_module = torch._C.import_ir_module(cu, f, map_location, _extra_files)
RuntimeError:
aten::_convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled) -> (Tensor):
Expected at most 12 arguments but found 13 positional arguments.
:
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py(420): _conv_forward
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/conv.py(423): forward
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py(709): _slow_forward
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py(725): _call_impl
/root/workspace/multimodal-pytorch/multimodal/model/multimodal_transformer.py(85): forward
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py(709): _slow_forward
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py(725): _call_impl
/root/workspace/multimodal-pytorch/multimodal/model/multimodal_transformer.py(221): visual_forward
/opt/conda/lib/python3.7/site-packages/torch/jit/_trace.py(940): trace_module
(36): export_torchscript_models
(3):
/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py(3418): run_code
/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py(3338): run_ast_nodes
/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py(3147): run_cell_async
/opt/conda/lib/python3.7/site-packages/IPython/core/async_helpers.py(68): _pseudo_sync_runner
/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py(2923): _run_cell
/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py(2878): run_cell
/opt/conda/lib/python3.7/site-packages/IPython/terminal/interactiveshell.py(555): interact
/opt/conda/lib/python3.7/site-packages/IPython/terminal/interactiveshell.py(564): mainloop
/opt/conda/lib/python3.7/site-packages/IPython/terminal/ipapp.py(356): start
/opt/conda/lib/python3.7/site-packages/traitlets/config/application.py(845): launch_instance
/opt/conda/lib/python3.7/site-packages/IPython/init.py(126): start_ipython
/opt/conda/bin/ipython(8):
Serialized   File "code/torch/torch/nn/modules/conv/___torch_mangle_9366.py", line 8
def forward(self: torch.torch.nn.modules.conv.___torch_mangle_9366.Conv2d,
input: Tensor) -> Tensor:
x = torch._convolution(input, self.weight, None, [32, 32], [0, 0], [1, 1], False, [0, 0], 1, False, False, True, True)
~~~~~~~~~~~~~~~~~~ <--- HERE
return x
def forward1(self: torch.torch.nn.modules.conv.___torch_mangle_9366.Conv2d,
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "stylegan_nada.py", line 166, in
net = ZSSGAN(args)
File "stylegan_nada\ZSSGAN\model\ZSSGAN.py", line 174, in init
for model_name in args.clip_models}
File "stylegan_nada\ZSSGAN\model\ZSSGAN.py", line 174, in
for model_name in args.clip_models}
File "stylegan_nada\ZSSGAN\criteria\clip_loss.py", line 36, in init
self.model, clip_preprocess = clip.load(clip_model, device=self.device)
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\clip-1.0-py3.6.egg\clip\clip.py", line 111, in load
state_dict = torch.load(model_path, map_location="cpu")
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\serialization.py", line 584, in load
return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\serialization.py", line 842, in _load
result = unpickler.load()
ModuleNotFoundError: No module named 'torch'`

With another torch version it doesnt get this far...
Loading base models...
Traceback (most recent call last):
File "stylegan_nada.py", line 166, in
net = ZSSGAN(args)
File "stylegan_nada\ZSSGAN\model\ZSSGAN.py", line 174, in init
for model_name in args.clip_models}
File "stylegan_nada\ZSSGAN\model\ZSSGAN.py", line 174, in
for model_name in args.clip_models}
File "stylegan_nada\ZSSGAN\criteria\clip_loss.py", line 36, in init
self.model, clip_preprocess = clip.load(clip_model, device=self.device)
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\clip-1.0-py3.6.egg\clip\clip.py", line 133, in load
model.apply(patch_device)
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\nn\modules\module.py", line 445, in apply
module.apply(fn)
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\nn\modules\module.py", line 445, in apply
module.apply(fn)
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\nn\modules\module.py", line 446, in apply
fn(self)
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\clip-1.0-py3.6.egg\clip\clip.py", line 130, in patch_device
if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"):
TypeError: 'torch._C.Node' object is not subscriptable

Trying with 1.8.1 now because pip warns me that clip requires 1.8.1

You could also try taking a look at: https://github.com/openai/CLIP/issues/79
The latest CLIP should work fine with 1.9 as well.

Already saw that
I upgraded to the recommended version (1.8.1) and now I get this:
Traceback (most recent call last):
File "stylegan_nada.py", line 166, in
net = ZSSGAN(args)
File "stylegan_nada\ZSSGAN\model\ZSSGAN.py", line 174, in init
for model_name in args.clip_models}
File "stylegan_nada\ZSSGAN\model\ZSSGAN.py", line 174, in
for model_name in args.clip_models}
File "stylegan_nada\ZSSGAN\criteria\clip_loss.py", line 36, in init
self.model, clip_preprocess = clip.load(clip_model, device=self.device)
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\clip-1.0-py3.6.egg\clip\clip.py", line 133, in load
model.apply(patch_device)
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\nn\modules\module.py", line 473, in apply
module.apply(fn)
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\nn\modules\module.py", line 473, in apply
module.apply(fn)
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\nn\modules\module.py", line 473, in apply
module.apply(fn)
[Previous line repeated 2 more times]
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\nn\modules\module.py", line 474, in apply
fn(self)
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\clip-1.0-py3.6.egg\clip\clip.py", line 124, in patch_device
graphs = [module.graph] if hasattr(module, "graph") else []
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\jit_script.py", line 449, in graph
return self._c._get_method("forward").graph
RuntimeError: Method 'forward' is not defined.

With the jit=False fix (trying ViT-B/32 now):
100%|███████████████████████████████████████| 256M/256M [00:28<00:00, 9.01MiB/s]
Traceback (most recent call last):
File "stylegan_nada.py", line 166, in
net = ZSSGAN(args)
File "stylegan_nada\ZSSGAN\model\ZSSGAN.py", line 174, in init
for model_name in args.clip_models}
File "stylegan_nada\ZSSGAN\model\ZSSGAN.py", line 174, in
for model_name in args.clip_models}
File "stylegan_nada\ZSSGAN\criteria\clip_loss.py", line 36, in init
self.model, clip_preprocess = clip.load(clip_model, device=self.device, jit=False)
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\clip-1.0-py3.6.egg\clip\clip.py", line 100, in load
raise RuntimeError(f"Model {name} not found; available models = {available_models()}")
RuntimeError: Model ViT-B/16 not found; available models = ['RN50', 'RN101', 'RN50x4', 'ViT-B/32']

ViT-B/32 doesnt get this far tho...

Okay ViT-B/32 gets further now
Models loaded! Starting training...
Traceback (most recent call last):
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\tqdm\notebook.py", line 96, in status_printer
pbar = IProgress(min=0, max=total)
NameError: name 'IProgress' is not defined
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "stylegan_nada.py", line 191, in
for i in notebook.tqdm(range(args.iter)):
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\tqdm\notebook.py", line 209, in init
self.fp, total, self.desc, self.ncols)
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\tqdm\notebook.py", line 104, in status_printer
"FloatProgress not found. Please update jupyter and ipywidgets."
ImportError: FloatProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
Exception ignored in: <bound method tqdm.del of   0%|| 0/151 [00:00<?, ?it/s]>
Traceback (most recent call last):
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\tqdm\std.py", line 1086, in del
self.close()
File "C:\Users\msk4x\AppData\Local\Programs\Python\Python36\lib\site-packages\tqdm\notebook.py", line 245, in close
self.sp(bar_style='danger')
AttributeError: 'tqdm_notebook' object has no attribute 'sp'

ViT-B/32 might be the fix, will have to fix an issue related to tqdm

WORKS NOW!!
Thank you!
I have a few questions about the program itself... (I am pretty new to this stuff and very interested in trying it out)
How does it "know" all of these target classes?
Can I somehow output 512/1024 images instead of 256?

Regarding the ViT-B/16 error - that means you don't have the most up-to-date version of CLIP. You can run fine without it for most things though, so it's no biggie.
The way it 'knows' all the target classes is through CLIP - it's a model that was trained on ~400M images and associated text labels from the internet, and it learned to match images with texts that describe them. We're using that model in order to guide the training.
Outputs at 512/1024: The size of the outputs you get during training depends only on the resolution of the model you started with. If you're using a 1024x1024 FFHQ model, you're going to get 1024x1024 images.
If you want to generate them after the fact, you can look at the colab for an example of a function that does it. Everything up until display part is still using the full resolution of the model.

Okay thanks!
Is there any way to speed up importing everything?

It runs without any errors now but somehow the network doesnt get trained (the samples always look the same)... could that be because of jit=False and the ViT-B/32 instead of ViT-B/16?

I installed clip via pip install git+https://github.com/openai/CLIP.git so it should be the latest one, right?

It will be the latest one, assuming you installed it in the last 26 days. If you had a version beforehand, then no. You can try to uninstall and install it again.
What texts are you using? Try something simple from our examples to make sure things are working correctly. Photo -> Sketch should be a good one.
ViT-B/32 should be enough for most changes. The ViT-B/16 version just helps with more local details.
I don't think jit=False is the problem since the newer CLIP versions (for example the one in the colab notebook) have it as a default, and the notebook works fine.
On speeding up imports - are you talking about the notebooks, or locally? If locally - first run takes a bit of extra time because it compiles some CUDA operations on the fly (and possibly downloads new CLIP models), but that only happens once and after that it should be quicker.

just using person -> zombie
Worked on the colab at least

Can you paste the parameters you're using?
And which FFHQ model did you use? The official 1024x1024 one?

I will just post my whole code (converted the notebook and then changed some stuff)...
I am using the official one in the notebook

import os
import sys

pretrained_model_dir = os.path.join("", "models")
os.makedirs(pretrained_model_dir, exist_ok=True)
restyle_dir = os.path.join("", "restyle")
stylegan_ada_dir = os.path.join("", "stylegan_ada")
stylegan_nada_dir = os.path.join("", "stylegan_nada")
output_dir = os.path.join("", "output")
output_model_dir = os.path.join(output_dir, "models")
output_image_dir = "images/results/"
sys.path.append(restyle_dir)
sys.path.append(stylegan_nada_dir)
sys.path.append(os.path.join(stylegan_nada_dir, "ZSSGAN"))

from argparse import Namespace
import numpy as np
from PIL import Image
from tqdm import tqdm
import torch
import torchvision.transforms as transforms
from restyle.utils.common import tensor2im
from restyle.models.psp import pSp
from restyle.models.e4e import e4e
from ZSSGAN.model.ZSSGAN import ZSSGAN
import numpy as np
from ZSSGAN.utils.file_utils import save_images
from ZSSGAN.utils.training_utils import mixing_noise

device = 'cuda'

source_model_type = 'ffhq' #@param['ffhq', 'cat', 'dog', 'church', 'horse', 'car']

source_model_download_path = {"ffhq":   "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/ffhq.pkl",
                              "cat":    "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/afhqcat.pkl",
                              "dog":    "https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada/pretrained/afhqdog.pkl",
                              "church": "1iDo5cUgbwsJEt2uwfgDy_iPlaT-lLZmi",
                              "car":    "1i-39ztut-VdUVUiFuUrwdsItR--HF81w",
                              "horse":  "1irwWI291DolZhnQeW-ZyNWqZBjlWyJUn"}

model_names = {"ffhq":   "ffhq.pkl",
               "cat":    "afhqcat.pkl",
               "dog":    "afhqdog.pkl",
               "church": "stylegan2-church-config-f.pkl",
               "car":    "stylegan2-car-config-f.pkl",
               "horse":  "stylegan2-horse-config-f.pkl"}

download_string = source_model_download_path[source_model_type]
file_name = model_names[source_model_type]
pt_file_name = file_name.split(".")[0] + ".pt"

dataset_sizes = {
    "ffhq":   1024,
    "cat":    512,
    "dog":    512,
    "church": 256,
    "horse":  256,
    "car":    512,
}

if not os.path.isfile(os.path.join(pretrained_model_dir, file_name)):
    print("Pls download "+file_name)
    exit()

if not os.path.isfile(os.path.join(pretrained_model_dir, pt_file_name)):
    convert_script = os.path.join(stylegan_nada_dir, "convert_weight.py")
    print("Enter this command: "+'python '+convert_script+' --repo '+stylegan_ada_dir+' --gen '+pretrained_model_dir+'/'+file_name+'( This might take a few minutes)')
    exit()

encoder_type = 'psp' #@param['psp', 'e4e']

restyle_experiment_args = {
    "model_path": os.path.join(pretrained_model_dir, f"restyle_{encoder_type}_ffhq_encode.pt"),
    "transform": transforms.Compose([
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])
}

model_path = restyle_experiment_args['model_path']
ckpt = torch.load(model_path, map_location='cpu')

opts = ckpt['opts']

opts['checkpoint_path'] = model_path
opts = Namespace(**opts)

restyle_net = (pSp if encoder_type == 'psp' else e4e)(opts)

restyle_net.eval()
restyle_net.cuda()
print('Model successfully loaded!')

print("Loaded stuff!")

#training

print("Training parameters")

source_class = input("Source class? ")
target_class = input("Target class? ")

improve_shape = False

model_choice = ["ViT-B/32", "ViT-B/32"]
model_weights = [1.0, 0.0]

if improve_shape:
    model_weights[1] = 1.0

mixing = 0.9 if improve_shape else 0.0

auto_layers_k = int(2 * (2 * np.log2(dataset_sizes[source_model_type]) - 2) / 3) if improve_shape else 0
auto_layer_iters = 1 if improve_shape else 0

training_iterations = int(input("Iterations? "))
output_interval     = 50
save_interval       = 0

training_args = {
    "size": dataset_sizes[source_model_type],
    "batch": 2,
    "n_sample": 4,
    "output_dir": output_dir,
    "lr": 0.002,
    "frozen_gen_ckpt": os.path.join(pretrained_model_dir, pt_file_name),
    "train_gen_ckpt": os.path.join(pretrained_model_dir, pt_file_name),
    "iter": training_iterations,
    "source_class": source_class,
    "target_class": target_class,
    "lambda_direction": 1.0,
    "lambda_patch": 0.0,
    "lambda_global": 0.0,
    "lambda_texture": 0.0,
    "lambda_manifold": 0.0,
    "auto_layer_k": auto_layers_k,
    "auto_layer_iters": auto_layer_iters,
    "auto_layer_batch": 8,
    "output_interval": 50,
    "clip_models": model_choice,
    "clip_model_weights": model_weights,
    "mixing": mixing,
    "phase": None,
    "sample_truncation": 0.7,
    "save_interval": save_interval
}

args = Namespace(**training_args)

print("Loading base models...")
net = ZSSGAN(args)
print("Models loaded! Starting training...")

g_reg_ratio = 4 / 5

g_optim = torch.optim.Adam(
    net.generator_trainable.parameters(),
    lr=args.lr * g_reg_ratio,
    betas=(0 ** g_reg_ratio, 0.99 ** g_reg_ratio),
)

sample_dir = os.path.join(args.output_dir, "sample")
ckpt_dir   = os.path.join(args.output_dir, "checkpoint")

os.makedirs(sample_dir, exist_ok=True)
os.makedirs(ckpt_dir, exist_ok=True)

seed = 3

torch.manual_seed(seed)
np.random.seed(seed)

fixed_z = torch.randn(args.n_sample, 512, device=device)

for i in tqdm(range(args.iter)):
    net.train()

    sample_z = mixing_noise(args.batch, 512, args.mixing, device)

    [sampled_src, sampled_dst], clip_loss = net(sample_z)

    net.zero_grad()
    clip_loss.backward()

    g_optim.step()

    if i % output_interval == 0:
        net.eval()

        with torch.no_grad():
            [sampled_src, sampled_dst], loss = net([fixed_z], truncation=args.sample_truncation)

            if source_model_type == 'car':
                sampled_dst = sampled_dst[:, :, 64:448, :]

            grid_rows = 4

            save_images(sampled_dst, sample_dir, "dst", grid_rows, i)

            img = Image.open(os.path.join(sample_dir, f"dst_{str(i).zfill(6)}.jpg")).resize((1024, 256))

    if (args.save_interval > 0) and (i > 0) and (i % args.save_interval == 0):
        torch.save(
            {
                "g_ema": net.generator_trainable.generator.state_dict(),
                "g_optim": g_optim.state_dict(),
            },
            f"{ckpt_dir}/{str(i).zfill(6)}.pt",
        )

print("Now the fun part...")

#editing real images

def run_alignment(image_path):
    import dlib
    from scripts.align_faces_parallel import align_face
    if not os.path.exists("shape_predictor_68_face_landmarks.dat"):
        print('Downloading files for aligning face image...')
        os.system('wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2')
        os.system('bzip2 -dk shape_predictor_68_face_landmarks.dat.bz2')
        print('Done.')
    predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
    aligned_image = align_face(filepath=image_path, predictor=predictor)
    print("Aligned image has shape: {}".format(aligned_image.size))
    return aligned_image

def get_avg_image(net):
    avg_image = net(net.latent_avg.unsqueeze(0),
                    input_code=True,
                    randomize_noise=False,
                    return_latents=False,
                    average_code=True)[0]
    avg_image = avg_image.to('cuda').float().detach()
    return avg_image

while 1:

    image_path = input("Image name? (exit to exit and train to train new model) ")

    image_path_no_extension = image_path.split('.',2)[0]

    if image_path=="exit":
        break
    if image_path=="train":
        continue

    try:

        image_path = "images/"+image_path

        original_image = Image.open(image_path).convert("RGB")

        input_image = run_alignment(image_path)

        img_transforms = restyle_experiment_args['transform']
        transformed_image = img_transforms(input_image)

        opts.n_iters_per_batch = 5
        opts.resize_outputs = False

        from restyle.utils.inference_utils import run_on_batch

        with torch.no_grad():
            avg_image = get_avg_image(restyle_net)
            result_batch, result_latents = run_on_batch(transformed_image.unsqueeze(0).cuda(), restyle_net, opts, avg_image)

        inverted_latent = torch.Tensor(result_latents[0][4]).cuda().unsqueeze(0).unsqueeze(1)

        with torch.no_grad():
            net.eval()

            [sampled_src, sampled_dst] = net(inverted_latent, input_is_latent=True)[0]
            joined_img = torch.cat([sampled_src, sampled_dst], dim=0)
            save_images(joined_img,output_image_dir , "joined", 2, 0)
    except IOError:
        print("File not found")
    except UnboundLocalError:
        print("Face not found")

Give me a few minutes, bringing up the colab to try and run with those parameters and check.

I reinstalled CLIP and there still is no ViT-B/16. Also reinstalled torch1.7.1 and there is no difference to before.

Ok, found your issue.
In the model choice lines, replace your current code with just:
model_choice = ["ViT-B/32"]
model_weights = [1.0]
On getting the version with ViT-B/16 - please make sure it's not re-installing from your local cache.
The clip model isn't mine, it's OpenAIs, and since the colab notebook is able to get ViT-B/16 I don't think there's any issue on their end.

Wow, thanks!

Works now :)

If I would save the models (or are they called checkpoints?) after training them, how much storage space do they take?

Oh and how to I "retrain" the model to something else after training it? What variable to I have to delete (or do I need to unload torch?) so that there actually is a fresh model?

FFHQ 1024x1024 are about 300 MB per checkpoint.
Retraining with something else - initializing a new network from scratch should be good enough. If you're working with a notebook, make sure you re-run all cells starting from the one where you define the args and do net = ZSSGAN(args).

Thank you for your time and your nice answers! Got the retraining working by restructuring some of my code... I might add a feature that saves the checkpoints after training and then loads them instead of training new ones (if I would want to use the same classes again)

I am currently trying to run the program and I need to convert the ffhq.pkl model do a .pt one.
When I enter python stylegan_nada\convert_weight.py --repo stylegan_ada --gen models/ffhq.pkl it says:
Traceback (most recent call last): File "stylegan_nada\convert_weight.py", line 11, in <module> from ZSSGAN.model.sg2_model import Generator, Discriminator File "C:\Users\msk4x\Documents\Projekte\stylegan\stylegan_nada\ZSSGAN\sg2_model.py", line 11, in <module> from op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d, conv2d_gradfix
How can I fix this?

I fix it by changing the import path in sg2_model.py
from
from op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d, conv2d_gradfix

to
from ZSSGAN.op import FusedLeakyReLU, fused_leaky_relu, upfirdn2d, conv2d_gradfix


Document what's new in ASP.NET Core for .NET 9 Preview 3

Description
Update the "What's new in ASP.NET Core 9.0" for .NET 9 Preview 3
@adityamandaleeka @mkArtakMSFT Please have folks comment on this issue with the desired content.
FYI: @samsp-msft @JeremyLikness @mikekistler @claudiaregio @JamesNK @SteveSandersonMS @davidfowl
Page URL
https://learn.microsoft.com/en-us/aspnet/core/release-notes/aspnetcore-9.0?view=aspnetcore-8.0
Content source URL
https://github.com/dotnet/AspNetCore.Docs/blob/main/aspnetcore/release-notes/aspnetcore-9.0.md
Document ID
4e75ad25-2c3f-b28e-6a91-ac79a9c683b6
Article author
@Rick-Anderson

Handle keyboard composition events in Blazor
The new KeyboardEventArgs.IsComposing property indicates if the keyboard event is part of a composition session. This is useful for tracking the composition state of keyboard events, which is crucial for handling international character input methods.
Thank you @BattlefieldDuck for this contribution!

@tdykstra Looks like most of this stuff got added to the What's New doc for .NET 9, but we're still missing Handle keyboard composition events in Blazor. Can we get that added?

👀
I'll put an 👁 on this in case something else comes up. I didn't see the post.

I'll put an 👁 on this in case something else comes up. I didn't see the post.

@guardrex There's no blog post for most .NET 9 preview releases this time around. Instead, the related content is being published as release notes and then the release is announced using GitHub Discussions. Here's the release notes for ASP.NET Core in .NET 9 Preview 3: https://github.com/dotnet/core/blob/main/release-notes/9.0/preview/preview3/aspnetcore.md.


Being able to drop a plugin into an empty layout plugin

Is your feature request related to a problem? Please describe.
Right now it seems to be impossible to drop a plugin into an empty layout plugin. The only way to add content to an empty layout plugin seems to be using the "+" button.
Describe the solution you'd like
It would probably be ideal to have the "insert-new" component as a drop target. Highlighting and all.

i know, i actually failed to implement that correctly ;-) Maybe i give it another try now, because i run into that problem just yesterday myself

ok fixed in beta and master (currently building)
i also increased the size of the insert new to 50%

fixed in 1.6.0


NavBarPage cannot be found

Hi, ALL THE 'Edit on GitHub' LINKS ARE BROKEN!
I am trying to use the following - sidebar-with-navbar
The NavbarPage cannot be found and the example link does not work.
Could I have a working example please?
Thanks

Hi @st-clair-clarke , we will fix example links as soon as possible, but all code example are just under the preview in the website :

Thanks for your 'qwik' reply. But in the specific example above, I cannot import the component NavbarPage that is being used to host the SideBar component.

example code is here : https://github.com/qwikerx/flowbite-qwik/blob/main/apps/web/src/routes/examples/[theme-rtl]/sidebar/04-sidebar-with-navbar/index.tsx
NavbarPage example is here : https://github.com/qwikerx/flowbite-qwik/blob/main/apps/web/src/components/NavbarPage/NavbarPage.tsx


Geohash.neighbours with a given radius in km.

Thanks, your library helps me a lot.
I'm trying to search data within a certain radius in km.
and I wonder if I could do like
Geohash.neighbours(geohash, radius, { units: km })

Is there any way to get array of geohash for a grid in a given radius?
input: geohash, radius(km)
output: [geohash1, geohash2, ...] that is a grid(circle or polygon) representing a given radius.

Thank you

Well, just arrived here, but answering your question...
take a look on this page: https://www.movable-type.co.uk/scripts/geohash.html
Seems like you just need to increase/decrease the precision for the encode..
Depending on your precision, more specific or general will be your location.
So two persons in a range of 15km, would have the same geocode if you pass a lower precision (4 for example). but would have different geocode if you pass a higher precision (8 as example).

Thanks for that explanation @smarcosanderson.


Sync failed android: error Cannot add task 'copyDebugIconFonts'

[X] Review the documentation: https://github.com/oblador/react-native-vector-icons
[X] Search for existing issues (including closed ones): https://github.com/oblador/react-native-vector-icons/issues/1041#issuecomment-515673115

Environment
System:
OS: macOS Mojave 10.14.6
CPU: (4) x64 Intel(R) Core(TM) i5-7360U CPU @ 2.30GHz
Memory: 31.91 MB / 8.00 GB
Shell: 3.2.57 - /bin/bash
Binaries:
Node: 12.11.1 - ~/.nvm/versions/node/v12.11.1/bin/node
Yarn: 1.16.0 - /usr/local/bin/yarn
npm: 6.11.3 - ~/.nvm/versions/node/v12.11.1/bin/npm
Watchman: 4.9.0 - /usr/local/bin/watchman
SDKs:
iOS SDK:
Platforms: iOS 13.2, DriverKit 19.0, macOS 10.15, tvOS 13.2, watchOS 6.1
IDEs:
Android Studio: 3.5 AI-191.8<IP_ADDRESS>10548
Xcode: 11.3/11C29 - /usr/bin/xcodebuild
npmPackages:
react: 16.9.0 => 16.9.0
react-native: 0.61.5 => 0.61.5

Description
I tried some solution but no one worked. Please help!
Demo

#18970 Duplicate

Do you solve it ?

I solved it by adding/updating the following line in node_modules/react-native-vector-icons/fonts.gradle:
...
        def rand = Math.random()

        // Create task for copying fonts
        def currentFontTask = tasks.create(
                name: "copy${targetName}IconFonts-${rand}",
...


CLOUD-53898 ignore ambari start plugin failures

@akanto

Jenkins build finished, all tests passed.
Refer to this link for build results: http://ci.sequenceiq.com/job/cloudbreak-pull-request/2220/


changed  describe-db-instance command to use query option instead of jq

*Issue #79
Description of changes:
Changed describe-db-instances command to use query option instead of JQ in order to get the db instance identifier
By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.

Thank you for your contribution!


Add Lumen catalog item

Adding https://courses.lumenlearning.com/catalog/lumen to Education resource list 👍

Thank you!


[LOC] Back-Translation "フレームワーク"→"Framework"

https://docs.microsoft.com/ja-jp/nuget/tools/cli-ref-install

The PR is not being built because the target branch live is filtered in .openpublishing.publish.config.json on live branch.

@hyoshioka0128:
Thank you for your feedback and suggestion!
We agree with your suggestions, and we will include them into the next article update release.
Kind regards,
Microsoft DOCS Global Experience Team

The PR is not being built because the target branch live is filtered in .openpublishing.publish.config.json on live branch.


SpellForce: Platinum Edition → crash at launch

When I launch the game, I have a black screen then it crashes.
System information

GPU: Nvidia GT 730M
Driver: 435.21
Wine version: Proton 4.11
DXVK version: #852

Apitrace file(s)
I can't apitrace the game. It just ignores the d3d9.dll, I think. Probably related to the issue.
Log files
steam-39540-d3d9.log
steam-39540-d9vk.log

It doesn't crash in windowed mode (use the command line parameter -window)

Does eadde9dd10bf52ab5531413b46e58503813e21d3 help here too?

No, nothing has changed.

I can launch the game in windowed mode. The issue is something about fullscreen, I guess...

For a weird reason, I can launch the game with my external 16:10 monitor, but not with my other monitor with 16:9 ratio and not with the laptop's monitor alone.

It doesn't crash with Proton-5.9-GE-3-ST. I don't know why, but it's not DXVK's fault.

Retested with Proton-5.9-GE-6-ST and Proton-tkg 5.18 and it crashes with both. Maybe my previous comment was wrong... I reopen it.

A solution has been found here:
https://github.com/ValveSoftware/Proton/issues/686#issuecomment-715441869

Fixed with Proton 5.13-2 RC (I hope Wine will add this fix too)
https://github.com/ValveSoftware/Proton/issues/4360


[ Fix ] Backend

Reworked backend logic

Fixed race condition
The process would read the output folder
created by the process while converting ,
resuling in an infinite loop
Now ignores already converted files
Now ignores destination folder

Hi @PhoneDroid ,
Sorry for the delayed response.
Thank you for your contributions and bug fixes.


Run iOS native tests (iOS_squid48_1611 branch)

Following tests are failing:
update - do we cancel timer properly on an immediate
update - test that we pick up new sequences while we wait
We properly fire peer unavailable and then available when connection fails
Can connect to a remote peer
Can do HTTP requests between peers
Can do HTTP requests after connections are cut
Can still do HTTP requests between peers with coordinator
test thali manager multiple starts and stops
test thali manager spies

We'll create issues for each failed test today.


Unable to open Palworld player files

Similarly to #44  I tried to open various files of the Palworld savedGames directory with gvas2json
However for most of the files, I'm getting various Error: Invalid header: File type XXXX not recognized errors (the XXX number varies for each file).
Some files like UserOption.sav are parsed without issues.
Is there a way to help gvas lib understand the inner format?
files.zip

The file looks like it's compressed/encrypted, currently investigating.

The file uses simple zlib compression, should be fairly trivial to add support

The format is as follows:
  <"PlZ1" constant>
Compressed length only includes the zlib stream length

Decompressing it as zlib only works for some files, and even then the gvas file is not read correctly by the lib, the game itself uses FCompression::CompressMemory for compressing the save file, will look more into it in the morning.

ref https://github.com/trumank/uesave-rs/issues/22

The game also saves most of the character data inside of an array and then serializes this into this gvas file, the array is the RawData field inside of the CharacterSaveParameterMap

You are a legend dude. Thanks a lot!

@localcc so, all files seem to be converted correctly both ways, but this one:
Level.zip
Getting Error: Missing hint for struct StructProperty at path worldSaveData.StructProperty.CharacterSaveParameterMap.MapProperty.Key.StructProperty, cursor position: 1773
I built a new version of gvas2json targeting the latest main commit of this repo and temporarily fixed the code to read using GameVersion::Palworld.
I wish I know more about gvas and Rust to handle it by myself but unfortunately this is a bit too much for my skills...

You can find the required hints here https://github.com/localcc/gvas/blob/main/tests/palworld.rs

Thanks! I added the following 2 hints that were missing for this specific file
    (
        String::from("worldSaveData.StructProperty.WorkSaveData.ArrayProperty.WorkAssignMap.MapProperty.Value.StructProperty"),
        String::from("StructProperty")
    ), (
        String::from("worldSaveData.StructProperty.DungeonSaveData.ArrayProperty.MapObjectSaveData.ArrayProperty.ConcreteModel.StructProperty.ModuleMap.MapProperty.Value.StructProperty"),
        String::from("StructProperty")
    ),

However JSON format of this file is 2.94GB so I'd suggest not including it in tests 😁


Add something like env["omniauth.headers"]

In the same way that params are kept in env['omniauth.params'], it would be desirable to keep headers in env['omniauth.headers'].
I'm trying to implement multiple providers. The problem is that I'm not using a cookie based authentication system, but a token one, where users are sending their credentials in the request headers each time.
The problem is that I loose that credentials when the user goes back to my app from the provider.
For example, say a user calls /auth/github with its credentials in the headers. Then, from /auth/:provider/callback I can't retrieve them so I can't say if the user was logged in or not.
It would be great if omniauth had some way to keep headers info, and adding `env['omniauth.headers']' seems the most natural for me .
Thanks

this is what I am looking for. I am using token auth too but cannot check whether it's request header

@TravisTam I submitted a pull request which implements it:
https://github.com/intridea/omniauth/pull/806
It is still pending to review.


clientOverlay colors are too low contrast

Hello,
I have found that the HTML clientOverlay uses some color, opacity and font settings that make it very difficult to read on many monitors.
Specifically the darkgrey: https://github.com/glenjamin/webpack-hot-middleware/blob/c3f18fad072ea5c24fc27d1d85f5c062d82f0070/client-overlay.js#L38
The result is that the file and line number are often unreadable without highlighting the text or straining:

The colors in the above screenshot score significantly below passing levels in WCAG 2.0 level AA - https://webaim.org/resources/contrastchecker/?fcolor=5C6478&bcolor=262626
Making it white helps a bit, but you may wish to tune it other ways:

Please save our eyes!
Thank you

This isn’t my area of expertise, but I will happily merge a PR from someone who knows what they’re doing here.
I suppose it should be something slightly off-white to stick with the spirit of the colour name?


NoSuchMethodError: org.apache.commons.lang3.Validate.inclusiveBetween

Hi,
I'm using Android build of FHIR library (hapi-fhir-android v1.4) within an app I developed some months ago. Everything works fine but on some versions of Xiaomi I'm gettting this error:
java.lang.NoSuchMethodError: org.apache.commons.lang3.Validate.inclusiveBetween
at ca.uhn.fhir.context.BaseRuntimeDeclaredChildDefinition.(BaseRuntimeDeclaredChildDefinition.java:49)
at ca.uhn.fhir.context.BaseRuntimeChildDatatypeDefinition.(BaseRuntimeChildDatatypeDefinition.java:43)
at ca.uhn.fhir.context.RuntimeChildPrimitiveDatatypeDefinition.(RuntimeChildPrimitiveDatatypeDefinition.java:35)
at ca.uhn.fhir.context.ModelScanner.scanCompositeElementForChildren(ModelScanner.java:630)
at ca.uhn.fhir.context.ModelScanner.scanCompositeElementForChildren(ModelScanner.java:399)
at ca.uhn.fhir.context.ModelScanner.scanCompositeDatatype(ModelScanner.java:374)
at ca.uhn.fhir.context.ModelScanner.scan(ModelScanner.java:321)
at ca.uhn.fhir.context.ModelScanner.init(ModelScanner.java:202)
at ca.uhn.fhir.context.ModelScanner.(ModelScanner.java:113)
at ca.uhn.fhir.context.FhirContext.scanResourceTypes(FhirContext.java:431)
at ca.uhn.fhir.context.FhirContext.(FhirContext.java:140)
at ca.uhn.fhir.context.FhirContext.(FhirContext.java:113)
at ca.uhn.fhir.context.FhirContext.forDstu2(FhirContext.java:507)
Another one is:
java.lang.NoSuchMethodError: No static method inclusiveBetween(JJJLjava/lang/String;)V in class Lorg/apache/commons/lang3/Validate; or its super classes (declaration of 'org.apache.commons.lang3.Validate' appears in /system/framework/framework.jar:classes2.dex)
at ca.uhn.fhir.context.BaseRuntimeDeclaredChildDefinition.(BaseRuntimeDeclaredChildDefinition.java:49)
at ca.uhn.fhir.context.BaseRuntimeChildDatatypeDefinition.(BaseRuntimeChildDatatypeDefinition.java:43)
at ca.uhn.fhir.context.RuntimeChildPrimitiveDatatypeDefinition.(RuntimeChildPrimitiveDatatypeDefinition.java:35)
at ca.uhn.fhir.context.ModelScanner.scanCompositeElementForChildren(ModelScanner.java:630)
at ca.uhn.fhir.context.ModelScanner.scanCompositeElementForChildren(ModelScanner.java:399)
at ca.uhn.fhir.context.ModelScanner.scanCompositeDatatype(ModelScanner.java:374)
at ca.uhn.fhir.context.ModelScanner.scan(ModelScanner.java:321)
at ca.uhn.fhir.context.ModelScanner.init(ModelScanner.java:202)
at ca.uhn.fhir.context.ModelScanner.(ModelScanner.java:113)
at ca.uhn.fhir.context.FhirContext.scanResourceTypes(FhirContext.java:431)
at ca.uhn.fhir.context.FhirContext.(FhirContext.java:140)
at ca.uhn.fhir.context.FhirContext.(FhirContext.java:113)
at ca.uhn.fhir.context.FhirContext.forDstu2(FhirContext.java:507)
The app is shpped with commons-lang3-3.4.jar dependency but seems to me that the Xiaomi custom image of Android already comes with a different version of the same library.
Do you know if there's some workaround to force using the latest version of commons-lang3 (shipped with my app) instead of the one shipped with the OS?
Thanks in advance.
Ciao,
Paolo

More discussion here: https://groups.google.com/d/msgid/hapi-fhir/CAMKPDh-iWbSzRrUWf%2BpzMV-fr6oWwdJvkD7ft9G-GbOpbNJk9w%40mail.gmail.com?utm_medium=email&utm_source=footer

I'm going to commit a fix which replaces the method call with something local. If anyone can figure out which version of the library is being used by the phone though that would be useful so that we can add a specific test.

Method inclusiveBetween in class Validate is present since version 3.0
https://commons.apache.org/proper/commons-lang/javadocs/api-3.0/org/apache/commons/lang3/Validate.html#inclusiveBetween(T, T, java.lang.Comparable)
Smartphones with this issue are both Xiaomi or Lenovo (from Android 4.4. to Android 6.0).
Logs do not reveal anything else...

We'll try your fix ASAP.

FWIW the specific inclusiveBetween method we were calling was the one that takes long as its paramrter type, and that one was added in commons-lang3 3.3.... So I guess the Xiaomi phone might have a version of CL3 as new as 3.2


Add option to display task configurations

Initial implementation done via #31.
@mimaison, after playing around a bit with the feature, I ended up re-arranging the information a bit. Instead of this one:

Tasks
Config
Tasks Config

It's now like this:

Config
Tasks

The tasks part shows the corresponding config if --tasks-config has been passed. I feel it's a bit more coherently organized that way. Closing this one as done with that. Thanks again!


Implement pan & zoom in the sprite editor

[x] Add pinch-to-zoom support to the sprite editor
[x] Add panning around the image if it's larger than the screen
[x] Make sure touch detection works with the updated block sizes/ panned page

63386ca0167f4b262a2daec25fcf52f3776e74ad fixes this.