add flatbuffers

tools/docker: add flatbuffers packages
go.mod: add github.com/google/flatbuffers
pkg/flatrpc: add schema
pkg/flatrpc: add connection type

Why do we need it?
What are your expectations?

Why do we need it? What are your expectations?

We want to get rid of go code running inside the machine under test in #1541. This would allow us to get coverage guided fuzzing on target platforms that have no go support. Currently we use net/rpc from the go standard library between syz-fuzzer (inside the VM) and syz-manager. As there is no C++ lib for GOs net/rpc mechanism, we need to replace it with something else before rewriting syz-fuzzer in C++.

There is a race between syz-fuzzers sending out which program is going to get executed next and executor running the program. If executor runs the program before the network packet leaves the interface, syz-manager doesn't get to know about the program that actually crashed the machine. Our current go net/rpc mechanism has buffers and submits stuff to the socket async via a go routine. Controlling the socket directly helps us minimize (though not eliminate) the problem.

Why do we need it? What are your expectations?

We want to get rid of go code running inside the machine under test in syz-fuzzer: fold fuzzer into manager process on host? #1541. This would allow us to get coverage guided fuzzing on target platforms that have no go support. Currently we use net/rpc from the go standard library between syz-fuzzer (inside the VM) and syz-manager. As there is no C++ lib for GOs net/rpc mechanism, we need to replace it with something else before rewriting syz-fuzzer in C++.
There is a race between syz-fuzzers sending out which program is going to get executed next and executor running the program. If executor runs the program before the network packet leaves the interface, syz-manager doesn't get to know about the program that actually crashed the machine. Our current go net/rpc mechanism has buffers and submits stuff to the socket async via a go routine. Controlling the socket directly helps us minimize (though not eliminate) the problem.

Thanks for providing the background.
Yes, I failed to provide this ahead of time.
Some additional points:

I want to do manual parsing, but @HerrSpace discouraged from this
we considered grpc, but discarded as over-engineered for our needs (well, at that's my opinion)
raw protobufs are better, but still look too heavy weight for our needs + slower parsing + we almost have to have header-only library b/c both integrating something like protobufs into our build system is lots of pain and we can't use distro provided libraries due to cross-compilation
flatbuffer are both faster/newer format, looks much more lightweight, don't try to do everything and header-only (at least in my early experiments I am able to build w/o linking in any libraries)

Phew, finally fixed a dozen of issues with tests, building executor in various places, old/new containers, etc. CI seems to be happy now.


bump year on license

also, drop it into the header of all files as per our new policy

 Thank you for your submission, we really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.You have signed the CLA already but the status is still pending? Let us recheck it.


Where did c2i_ functions go?

I have some sample code which uses c2i_ functions like c2i_ASN1_INTEGER(), but in openssl 1.1.1 I cannot compile it since all c2i_* functions are missing from the header file asn1.h where I used to see them: https://github.com/openssl/openssl/blob/master/include/openssl/asn1.h
Can someone let me know where these c2i functions go, or what are their replacement?

The c2i functions are considered internal only and were removed from the public API in OpenSSL 1.1.0. You should use the d2i equivalents instead.

thanks @mattcaswell

@mattcaswell do you have any recommendations on replacements for c2i_ASN1_INTEGER?
The d2i_ASN1_UINTEGER is the only similar one, and it does not do the same thing.
Thanks for any advice! I'd love to get by this blocker I have here.

The c2i_* functions parse  the content bytes of a DER INTEGER, i.e. the bit after the tag/length. You need to rewrite your code to pass the whole DER INTEGER encoding including the tag/length and use the d2i_* functions instead.

Fixed it up, not too bad. You can close this if you like.

I am recently migrating from 1.0.2 to 1.1.1. I am having hard time figuring out how to convert c2i_ASN1_INTEGER to one of its equivalent. Can someone please provide me with the source please. Thanks!!
@mattcaswell @thejeff77

@udiakasnuke - this was my solution in Swift code using the equivalent d2i function:
    func DecodeASN1Integer(startOfInt intPointer: inout UnsafePointer<UInt8>?, length: Int) -> Int? {
        let integer = d2i_ASN1_UINTEGER(nil, &intPointer, length)
        let result = ASN1_INTEGER_get(integer)
        ASN1_INTEGER_free(integer)
        return result
    }

Thanks @thejeff77, pardon me but i don't see any visible difference b/w the usage of c2i_ASN1_INTEGER and it's near equivalent d2i_ASN1_UINTEGER apart from this extra line let result = ASN1_INTEGER_get(integer) but that might further be because of swift code. I am still a bit unclear. Please clear me!!

A DER encoded integer consists of a single byte "tag", followed by 1 or more bytes indicating the length of the integer, followed by the contents of the integer itself. c2i_ASN1_INTEGER assumes that you have already decoded the tag and length bytes somehow and are just decoding the "content" bytes. d2i_ASN1_INTEGER on the other hand decodes the whole thing, tag, length bytes and the content bytes.
These two functions are not a straight one-for-one swap. You need to analyse your application that is currently calling c2i_ASN1_INTEGER and figure out how it is decoding the tag and length bytes, and replace all of that code with d2i_ASN1_INTEGER.


NativeCFFI Not Found

Describe your problem here.
im trying to compile but says this (sorry fpr bad english): System.hx:270: characters 15-25 : Type not found : NativeCFFI
characters 12-22 : Type not found : NativeCFFI
characters 12-22 : Type not found : NativeCFFI
characters 29-39 : Type not found : NativeCFFI
im using psych 0.7
Are you modding a build from source or with Lua?
Source
What is your build target?
Windows x64
Did you edit anything in this build? If so, mention or summarize your changes.
no

There is no "NativeCCFI" on psych engine, i don't know what that library means.


Add support for relational inequalities to "width" and "height"

For example:
<UIImageView image="foo.png" height=">=50"/>

This syntax would be difficult to implement, since the width and height properties are floats. A better approach might be to add min/max width and height properties to UIView:

minimumWidth
maximumWidth
minimumHeight
maximumHeight

This has been implemented but not yet tested or documented.

This is done.


path for patterns.svg in windows 10

Hi,
maybe for people in order to save search time, you could mention the path to patterns.svg in windows 10:
C:\Program Files\Inkscape\share\patterns\patterns.svg
Also as a hint: one has to open the editor as administrator via - win - (editor name) - right click - open as administrator.
(many thx, patterns work well in my inkscape!)
greetings
Dominik

Thanks! I updated the README with that information.


CubeAttrsDict compatibility with dict methods

Noted that where a Cube.attributes used to be a iris.common.mixin.LimitedAttributeDict, it is now a iris.cube.CubeAttrsDict, which inherits from MutableMapping, but not dict.
We noticed that there are a few remaining case where a CubeAttrsDict is not truly code-compatible with the existing LimitedAttributeDict type :

__or__ and __ior__ (i.e. "|" and "|="), are present on dict but not provided by MutableMapping, (for somewhat complex reasons)

they can easily be provided, based on dict.update()

Although CubeAttrsDict already supports 'update' (via MutableMapping), if given an existing CubeAttrsDict this will not respect the global/local category of its entries, so that should be fixed.
fromkeys, is a bit obscure but maybe worth supporting.  Again, the case where an existing split-attributes is the input can be handled properly (i.e. preserving global/local type of attributes).

So, these could cause errors in existing user code, and it makes sense to provide implementations of these methods.

Closed by #5592


Implement two-output division

This implements two-output division as discussed in https://github.com/unageek/inari/issues/87

Hi! Thank you for the PR! Could you generate tests for mul_rev_to_pair following the instructions below?

pushd ITF1788
git fetch upstream (whatever you named https://github.com/unageek/ITF1788)
git checkout inari-pr-89
popd
./tools/gen_itf1788_tests.sh
cargo fmt
Uncomment the next line:
https://github.com/unageek/inari/blob/7b897c4a26c166b1396cd84b1d2bb85065f17174/tests/itf1788.rs#L22

I've done so.

Rebased.

Could you revert the change to .github/workflows/build.yml?

Pull Request Test Coverage Report for Build<PHONE_NUMBER>

124 of 124 (100.0%)  changed or added relevant lines in 2 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage increased (+0.3%) to 85.212%

Totals

Change from base Build<PHONE_NUMBER>:
0.3%

Covered Lines:
2172

Relevant Lines:
2213

💛  - Coveralls

Could you revert the change to .github/workflows/build.yml?

I deleted the relevant commit.

I performed the requested changes.  Did not find how to test the KaTeX doc however.

You can build the documentation with:
RUSTDOCFLAGS="--cfg docsrs --html-in-header /path/to/inari/src/_docs/header.html" cargo doc --open

Note that the path to header.html must be absolute.

By the way, do you have any ideas about how the decorated version of mul_rev_to_pair might be used?

You can build the documentation with:

Thanks — I should have reread the README!  (I made a shell script to stop forgetting. 🤞)

do you have any ideas about how the decorated version of mul_rev_to_pair might be used?

No.  Actually, books and papers in my field (computer assisted proofs) seldom mention decorated intervals.  Maybe in constraint-satisfaction problems but I am less familiar with that.

I've done so.  Let me know what you think.

GAOL uses the term relational division. I like this one! I'm not going to use it in the crate documentation, though.

LGTM! Thank you!


Organization members pagination

When an organization has many members, i.e. more than 200 members. We needs a pagination.

Fixed in #9452

@SpaWn2KiLl This said the problem of UI not API. It's fixed by #9092.


Task timed out after 300.07 seconds

{
"errorMessage": "2018-02-07T13:38:33.285Z 7d79ae5e-0c0b-11e8-9c34-071d38a89c78 Task timed out after 300.07 seconds"
}
Getting above error when try to run below code
const mpromise = require('promise');
const setupS3Chrome = () => {
return new mpromise(function (resolve, reject) {
const params = {
Bucket: config.remoteChromeS3Bucket,
Key: config.remoteChromeS3Key
};
console.log(params);
s3.getObject(params).createReadStream().on('error', err => reject(err)).pipe(tar.x({
C: config.setupChromePath
})).on('error', err => reject(err)).on('end', () => resolve());
});
};

I had the same problem. In my case, I had not configured my VPC correctly in order for my Lambda Function to have access to the internet.
You should check out this article.
After I had configured my VPC correctly (2 private subnets, 1 public subnet) and added only the two private subnets to my Lambda Function, I was able to access the Internet from within and the S3 Download started working too.


Open up Discussions, so people can ask questions not in the issues area

@stlehmann could you open up the Discussions area on the repo (if you think it is a good idea), trying to get people to use that or stack overflow for asking more "how-to" like questions (still get a lot of people querying the how to create a route). It also gives me the ability to move issues to discussions (and vice versa), so I don't have to keep closing issues

Hey Chris,
I'm glad you're still on it. I think it is a good idea to use the
discussion feature. I just set it up. Hope this helps.
Best regards
Stefan

Freundliche Grüße
Stefan Lehmann
Am 13.08.2024 10:59 schrieb chrisbeardy:

@stlehmann [1] could you open up the Discussions area on the repo (if
you think it is a good idea), trying to get people to use that or
stack overflow for asking more "how-to" like questions (still get a
lot of people querying the how to create a route). It also gives me
the ability to move issues to discussions (and vice versa), so I don't
have to keep closing issues
--
Reply to this email directly, view it on GitHub [2], or unsubscribe
[3].
You are receiving this because you were mentioned.Message ID:
@.***>
Links:
[1] https://github.com/stlehmann
[2]
https://github.com/stlehmann/pyads/issues/407#issuecomment-2285729854
[3]
https://github.com/notifications/unsubscribe-auth/ABDNGBULWCLXD5XWG2376IDZRHDHNAVCNFSM6AAAAABMNZQO2KVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDEOBVG4ZDSOBVGQ

thanks


{network}Add tests for four network commands

Description

Add tests for four network commands, below tests will be affected:
test_network_ag_auth_cert
test_network_ag_root_cert
test_network_ag_rule
test_network_app_gateway_with_private_ip
Testing Guide

History Notes

[Component Name 1] BREAKING CHANGE: az command a: Make some customer-facing breaking change.
[Component Name 2] az command b: Add some customer-facing feature.

This checklist is used to make sure that common guidelines for a pull request are followed.

[ ] The PR title and description has followed the guideline in Submitting Pull Requests.

[ ] I adhere to the Command Guidelines.

[ ] I adhere to the Error Handling Guidelines.

network


Treat robot heading as a manifold (cos/sin unit vector)

Does this PR supersede https://github.com/SleipnirGroup/TrajoptLib/pull/191?

Yes, due to the refactors breaking mergeability.


Missing cannon spots

I have made a list of (what I think is) all the missing monsters and their respective spots. Attached is a link to images of all the spots. Link

Brine rat(2707, 10132, 0)
Dagganoth(2524, 10020, 0)
Dark beast(1992, 4655, 0)
Dust devil(3218, 9366, 0)
Kalphite(3307, 9528, 0)
Lesser demon(2838, 9559, 0)
Lizardmen(1500, 3703, 0)
Scabarites/minions of scarabas(3297, 9252, 0)
Smoke devil(2398, 9444, 0)

Added in #566


Default key mappings in definition window don't work

Describe the bug
The default mappings that start with <C-c> don't work for me. E.g. when I press <C-c>i I'm in INSERT mode.
The only key that works is q.
I also tried to use a different prefix like split = '<C-a>v' but it doesn't work either.
It works, if I define it with a single key only like split = '<C-a>'
Steps to reproduce

open definition with :Lspsaga peek_definition
Press <C-c>i

Expected behavior
It should open a split windows with the definition.
Neovim version (nvim -v)
0.10.0-dev-753+g4a06de40e
lspsaga commit
535d28a
Terminal name/version
Gnome Terminal 3.44.0

the keymap just normal mode .

The output of :nmap when the definition window is opened:
n  q           *@<Lua 312: ~/.local/share/nvim/plugged/lspsaga.nvim/lua/lspsaga/definition.lua:69>
n  <C-C>o      *@<Lua 297: ~/.local/share/nvim/plugged/lspsaga.nvim/lua/lspsaga/definition.lua:69>
n  <C-C>k      *@<Lua 291: ~/.local/share/nvim/plugged/lspsaga.nvim/lua/lspsaga/definition.lua:91>
n  <C-C>i      *@<Lua 281: ~/.local/share/nvim/plugged/lspsaga.nvim/lua/lspsaga/definition.lua:69>
n  <C-C>v      *@<Lua 310: ~/.local/share/nvim/plugged/lspsaga.nvim/lua/lspsaga/definition.lua:69>
n  <C-C>t      *@<Lua 311: ~/.local/share/nvim/plugged/lspsaga.nvim/lua/lspsaga/definition.lua:69>

the keymap just normal mode .

But I am in normal mode.

Hmm, weird. As a workaround I've now defined single key maps (I don't edit in the definition window):
  definition = {
    keys = {
      edit = '<C-e>',
      vsplit = '<C-v>',
      split = '<C-x>',
      quit = 'q',
    }
  },

I can live with that. But if you want me to debug something let me know.
The mappings have never worked for me (also not in the older releases).

which language ?

Aah: I was typing to slow. It works if I type fast. Is this controlled by timeoutlen (which I have set to 200)?
Most other key sequences don't have a timeout (like gd), so I didn't expect it here.

yup the value too small maybe try 500 ?


cleanup harness code (part 3)

dropped Tao tests generation as it does not seem to be used
switch fourslash from using global currentTestState to local test state. The effect of this change:

generated test files are smaller since all runtime infrastructure is moved to core fourslash bits - less code to eval
fourslash.ts used by tests now stores only declarations so it is smaller - less code to parse/typecheck

:+1: :trophy:


Include branch name other than main in the PR title

Description of changes:
This PR is a follow-up to #3643 for Makefile generated help PRs and go.mod update PRs.
By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

/cherry-pick release-0.20

/cherry-pick release-0.19

/approve


FEATURE - EXT - Enhance search results to show relevant node(s)

As a developer using Guided Answers extension,
I want to see exact nodes which meets my search criteria,
so that I I precisely know which node to navigate under a tree rather than tying everywhere
Description

Currently, when we search, the result list view show list of all the Guided Answers tree which meets search criteria
As a user, I have no information which branch/node to click once i am INSIDE a tree
This US will help users to reach specific nodes directly without much guess work
Taking example of GA web application, search for Request failed with the status code 400
And search result will highlight the node which is clickable and take the user directly
The node title and the sentence in the Guided Answer content that matches the search criteria will display under the Tree title and description. Text matching the search criteria will be lightly highlighted or bolded as shown in the designs.
Up to two inner nodes of a tree that match the search criteria will be displayed ( if there is two nodes) the user will be able show more if there is more nodes.

Visual Design
https://www.figma.com/file/XSZw3w81MYY0QPe1BoUcBO/Guided-Answers?node-id=0%3A1&t=RaMPLflFLW8yQ6jx-0

Technical Design
Needs inputs from both @Klaus-Keller and @JonWiens (Guided Answers API needs to be enhanced)
Acceptance Criteria

On entering search criteria all  nodes/outcomes  of a tree that that contain or match the search input will be displayed in the returned Guided Answer list.

On click of a node/outcome the user will be navigated to that specific node in the tree.  No path will be shown as the user will not have navigated through the other nodes in the tree to arrive at that specific node.

If there are multiple nodes of tree that match the search input then only two will be displayed but the user can click to show more.

On click of the tree in the list the user will be navigated to the start of the tree.

Notes

Tasks

[ ]
[ ] Unit Tests added
[ ] Test Cases defined

Updated the highlights, we are not doing brutal yellow ones. Found search query is bold (or if it's already bold, it just stays bold, like in the titles).

@unseen1980 - I updated the story regarding the different view modes.


Machines not detected on autocrafting

Issue description: Crusher not detected on autocrafting
What happens:
When I try to upgrade Mekanism machine, the low tiers is not detected.
What you expected to happen: Take the old machine and autocraft it
Steps to reproduce:

Make recipe with crusher, basic crushing factrory, etc
Try to autocraft

Version (make sure you are on the latest version before reporting):

Minecraft: 1.12.2
Forge: <IP_ADDRESS>68
Refined Storage: 1.6.9

Does this issue occur on a server?
yes
If a (crash)log is relevant for this issue, link it here:
I made a video of the problem, This is made to Reborn crafter, but bug also reproduceable on Refined crafter
https://www.youtube.com/watch?v=OZMijo9zjHk&feature=youtu.be
Hope I can help ^^

Duplicate of #2064, sorry


all times in KeyboardDragListener should be in milliseconds

Discovered while working on phetsims/gravity-force-lab#186. There are some values in step that are in seconds, and some in milliseconds. Today I talked to @jessegreenberg about it, and we thought about converting it to seconds because that is what the sim gives us in the step function. After poking around I don't think that is the direction we should go. All the following are public options, and seem nice as millisecond values:

hotkeyHoldInterval
*moveOnHoldInterval
moveOnHoldDelay

And each of these have an internal counter that is stepped. I think that these should continue to be in milliseconds.
In which case, we should see if the whole file can run in ms.

This small change seems good for the most part. For the velocity, @jessegreenberg and I thought it best not to change the public facing apis from "pixels/second" (i.e. dragVelocity), so I ended up just converting within the step function and it is sorta a wash. I don't think it is too horrible though, and this way you have 5 sections in a row doing actions from ms instead of 4 from ms and one from dt. @jessegreenberg please review. Close if all looks good to you.

Change looks good and has been working for a long time. Other scenery/PhET APIs like FireListener and axon timers are in ms so this was a good change. At the same time, the "velocity" options in pixels per second is also most intuitive.
Closing.


Release 3.5.1

This will be merged and released on the 13th of February, 2018.

Why are we bothering to support a getSize variant for longs instead of just making the normal RichPresence getSize return a long?

The limit of the party size values are 2**63 (signed long) according to jake, this limit was used just yesterday and broke parsing for several bots which relied on 32 bits.
Changing getSize() to return int instead of long is a breaking change:
int size = party.getSize(); // compiler error, incompatible types. required: int found: long

In addition to that, discord apparently provides these values in Strings once they exceed 32 bit ranges to be compatible with their javascript parts, this ultimately broke the JSONObject#getInt parsing.


Trying to stop the bundle returned by GetBundle(0) causes a crash

The following code causes a crash on Mac OS X. Haven't tried on other platforms.
void TestIndirectFrameworkStop()
{
    auto f = FrameworkFactory().NewFramework();
    f.Start();
    auto bundle = f.GetBundleContext().GetBundle(0);
    bundle.Stop(); // crashes here
    US_TEST_CONDITION(f.GetState() != Bundle::STATE_ACTIVE, "Framework failed to stop");
}

I understand why the above bundle.Stop() does not call Framework::Stop() method but is this expected ?
At the very least, it should not crash.

Uh, that is a nasty one. You are right, the current implementation does not call Framework::Stop() when calling Stop() on the a Bundle instance that represents the system bundle. That is definitely a bug and a severe one. The problem extends to all other methods that the Framework class intends to override. This was introduced when we made the Bundle and BundleContext classes value-types.


Add yaml serialization tags to breaker config

This change allows introduces yaml tags to the fields of the circuit breaker config. Allowing config to be sourced from a common file and removing the need for a code change when making adjustments

@fishy Would changing the keys to pascal case make this change backwards compatible with any existing serialization usages?

@marcoferrer I have lost track of what "pascal case" is 😅 , but the only true backward compatible way is to use all lower case (e.g. minrequeststotrip for field MinRequestsToTrip), which is the same as don't specify yaml tag at all.
also I'm not saying we should not make breaking changes. I'm just saying we need to slightly delay the merging of approved breaking changes as we don't want to maintain a 0.8.x branch :)


Implement Animations and 3D Models Using Three.js

As the website currently lacks engaging animations and interactive 3D models, this issue aims to enhance the user experience by integrating Three.js. The objective is to add dynamic animations for key elements and incorporate lightweight, responsive 3D models to elevate visual interactivity. The addition of Three.js will help create a more immersive and visually appealing interface while maintaining optimal performance.
Acceptance Criteria:
-Smooth animations are implemented for key interactions.
-3D models are properly loaded and displayed on the site.
-The website remains responsive and performance metrics are not negatively affected.
-Changes follow the design and coding standards outlined in the README.md.

I can work on this issue
But can you describe what kind of animations would you like in the website
if any idea or any abstract you could give


fix: add timestamp to fake log line to match actual API

I'm working on tests for Argo CD's log fetcher. Our log parsing logic assumes lines start with timestamps. That works for actual API responses, but not the fake log response.
I can fix the test by adding logic to skip timestamp parsing. But it would be nice to have the change upstream as well.
Sorry, we do not accept changes directly against this repository, unless the
change is to the README.md itself. Please see
CONTRIBUTING.md for information on where and how to contribute instead.

I should read the contributing guide. :-P


Unclear documentation of topic format

The documentation says that:
"When a message with a topic that ends in a statistical function name is received"
So from this I assume that I can make a topic like this: "myData_mean", and statistics will know that i want the mean, since the topic ends with "mean".
But the way it is coded, the topic splitter is required to be "/". This is rather confusing.

Thanks for reporting this.  Like all nodes in Node Red, it uses the MQTT topic format that specifies the '/' as a topic level separator.  I've clarified the documentation.


Issue with pjit during training

I'm trying to use the following command to train with the unchanged code on a v3-8 TPU VM:
python3 training/train_clip.py --output_dir outputs --config_name training/configs/clip-vit-large-patch16 --tokenizer_name openai/clip-vit-base-patch16
I get the following exception:
At:
  /home/raphaelp/clip-jax/venv/lib/python3.8/site-packages/jax/_src/sharding.py(162): _check_mesh_resource_axis
  /home/raphaelp/clip-jax/venv/lib/python3.8/site-packages/jax/_src/sharding.py(297): _preprocess
  /home/raphaelp/clip-jax/venv/lib/python3.8/site-packages/jax/interpreters/pxla.py(3716): _create_mesh_pspec_sharding
  /home/raphaelp/clip-jax/venv/lib/python3.8/site-packages/jax/experimental/pjit.py(461): _create_mesh_pspec_sharding_from_parsed_pspec
  /home/raphaelp/clip-jax/venv/lib/python3.8/site-packages/jax/experimental/pjit.py(476): _create_sharding_for_array
  /home/raphaelp/clip-jax/venv/lib/python3.8/site-packages/jax/experimental/pjit.py(358): <lambda>
  /home/raphaelp/clip-jax/venv/lib/python3.8/site-packages/jax/_src/tree_util.py(207): <genexpr>

Traceback (most recent call last):
  File "training/train_clip.py", line 1570, in <module>
    main()
  File "training/train_clip.py", line 941, in main
    state = pjit(
  File "/home/raphaelp/clip-jax/venv/lib/python3.8/site-packages/jax/experimental/pjit.py", line 135, in cache_miss
    outs, out_flat, out_tree = _python_pjit_helper(infer_params, *args, **kwargs)
  File "/home/raphaelp/clip-jax/venv/lib/python3.8/site-packages/jax/experimental/pjit.py", line 109, in _python_pjit_helper
    args_flat, _, params, _, out_tree, _ = infer_params(*args, **kwargs)
  File "/home/raphaelp/clip-jax/venv/lib/python3.8/site-packages/jax/experimental/pjit.py", line 357, in infer_params
    out_shardings = tree_map(
  File "/home/raphaelp/clip-jax/venv/lib/python3.8/site-packages/jax/_src/tree_util.py", line 207, in tree_map
    return treedef.unflatten(f(*xs) for xs in zip(*all_leaves))
  File "/home/raphaelp/clip-jax/venv/lib/python3.8/site-packages/jax/_src/tree_util.py", line 207, in <genexpr>
    return treedef.unflatten(f(*xs) for xs in zip(*all_leaves))
  File "/home/raphaelp/clip-jax/venv/lib/python3.8/site-packages/jax/experimental/pjit.py", line 358, in <lambda>
    lambda x: _create_sharding_for_array(pjit_mesh, x), out_axis_resources)
  File "/home/raphaelp/clip-jax/venv/lib/python3.8/site-packages/jax/experimental/pjit.py", line 476, in _create_sharding_for_array
    return _create_mesh_pspec_sharding_from_parsed_pspec(mesh, x)
  File "/home/raphaelp/clip-jax/venv/lib/python3.8/site-packages/jax/experimental/pjit.py", line 461, in _create_mesh_pspec_sharding_from_parsed_pspec
    return pxla._create_mesh_pspec_sharding(mesh, x.user_spec, x)
  File "/home/raphaelp/clip-jax/venv/lib/python3.8/site-packages/jax/interpreters/pxla.py", line 3716, in _create_mesh_pspec_sharding
    return sharding_internal.NamedSharding(mesh, pspec, parsed_pspec)
  File "/home/raphaelp/clip-jax/venv/lib/python3.8/site-packages/jax/_src/sharding.py", line 297, in _preprocess
    _check_mesh_resource_axis(self.mesh, self._parsed_pspec)
  File "/home/raphaelp/clip-jax/venv/lib/python3.8/site-packages/jax/_src/sharding.py", line 162, in _check_mesh_resource_axis
    raise ValueError(f"Resource axis: {e.args[0]} of {parsed_pspec.user_spec} is "
ValueError: Resource axis: model of PartitionSpec('model', None) is undefined.

I haven't checked since 0.4 but may just need to set the JAX array config flag to false until I migrate whatever is necessary.

Hi Boris! Thanks for the quick response!
Using jax==0.3.25 does not seem to fix it. I got the same exception again.
Also tried to set jax.config.update('jax_array', False) right after importing jax which also did not seem to help.
Any other ideas for things I could try?

That is strange. You have a dataset?
Do you pass "__do_train" also?
One of the spec from the state may have a wrong default.

A typical command for me would be:
python train_clip.py
  --assert_TPU_available
  --output_dir OUTPUT_FOLDER
  --train_folder xxx
  --config_name configs/clip-xxx
  --learning_rate 0.0001 --warmup_steps 2000
  --tokenizer_name xxx --do_train
  --batch_size_per_node xxx --gradient_accumulation 1
   --gradient_checkpointing --logging_steps 100 --eval_steps 500 --save_steps 2000 --log_model

Yeah I created the dataset with your script from the repo which worked fine.
I was running the exact command I provided above without --do_train (silly me) but I get the same error with your command so that should not have been the main issue?
I keep getting the same exception mentioning ValueError: Resource axis: model of PartitionSpec('model', None) is undefined. so I would guess it could be a wrong default.

Oh I think I found it.
In the training script replace all "dp" with "data" and all "mp" with "model".
The issue is in the partitioning file that I added later I call the model partition "model" while I call it "mp" (as "model parallel") in the training script.

That did it! It's running now! Thanks a lot!

Nice, let me know how it goes. I'll be working on improving it in the coming weeks.


[BUG] getch in _get_pixel_size hangs on package import on Windows until a key is pressed

Describe the bug
Calling import pytermgui spawns a Terminal; on Windows terminals (tested: Windows Terminal, Powershell, and the built-in VS Code terminal), the Terminal constructor attempts to call _get_pixel_size after sending the (ignored) ANSI terminal command [14t, to which Windows terminals do not respond. The resulting getch call blocks until a key is pressed.
To Reproduce
Steps to reproduce the behavior:

On Windows, start any Python file containing import pytermgui, within Powershell or Windows Terminal.
Further Python code does not run until you push any key, which causes the getch call to return (and set pixel size = (0,0) due to it not finding a ;).

Expected behavior
On terminals that don't support ANSI terminal commands, don't hang trying to get pixel size.
Screenshots
If applicable, add screenshots to help explain your problem.
System information
PyTermGUI v3.2.1

Python: 3.9.4
Platform: Windows-10-10.0.19041-SP0

Possible solution
Is it sufficient to add an additional check to get_chars (line 135 in input.py) that checks msvcrt.kbhit() prior to any getch call, and returns an empty buffer if kbhit returns false?

Still getting this behavior arch linux.
ptg --version
PyTermGUI v4.3.0

Python: 3.10.4
Platform: Linux-5.16.14-arch1-1-x86_64-with-glibc2.35

even running ptg --version or python setup.py install  it's blocked and you have to press a key for it to run.

No idea what might be going on there. Could you run something, and while it's waiting for a key interrupt (CTRL_C) the process?
Also, which terminal emulator are you using?

I'm using st (simple terminal from suckless) and this is the output after pressing CTRL_C
> ptg
Traceback (most recent call last):
  File "/home/kamikaze/.virtualenvs/pytermgui/lib/python3.10/site-packages/pytermgui-4.3.0-py3.10.egg/pytermgui/input.py", line 362, in getch
    key = _getch()
  File "/home/kamikaze/.virtualenvs/pytermgui/lib/python3.10/site-packages/pytermgui-4.3.0-py3.10.egg/pytermgui/input.py", line 105, in __call__
    buff = "".join(self.get_chars())
  File "/home/kamikaze/.virtualenvs/pytermgui/lib/python3.10/site-packages/pytermgui-4.3.0-py3.10.egg/pytermgui/input.py", line 93, in get_chars
    yield self._read(1)
  File "/home/kamikaze/.virtualenvs/pytermgui/lib/python3.10/site-packages/pytermgui-4.3.0-py3.10.egg/pytermgui/input.py", line 73, in _read
    char = os.read(sys.stdin.fileno(), 1)
KeyboardInterrupt

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/kamikaze/.virtualenvs/pytermgui/bin/ptg", line 33, in <module>
    sys.exit(load_entry_point('pytermgui==4.3.0', 'console_scripts', 'ptg')())
  File "/home/kamikaze/.virtualenvs/pytermgui/bin/ptg", line 25, in importlib_load_entry_point
    return next(matches).load()
  File "/usr/lib/python3.10/importlib/metadata/__init__.py", line 171, in load
    module = import_module(match.group('module'))
  File "/usr/lib/python3.10/importlib/__init__.py", line 126, in import_module
    return _bootstrap._gcd_import(name[level:], package, level)
  File "<frozen importlib._bootstrap>", line 1050, in _gcd_import
  File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
  File "<frozen importlib._bootstrap>", line 992, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "<frozen importlib._bootstrap>", line 1050, in _gcd_import
  File "<frozen importlib._bootstrap>", line 1027, in _find_and_load
  File "<frozen importlib._bootstrap>", line 1006, in _find_and_load_unlocked
  File "<frozen importlib._bootstrap>", line 688, in _load_unlocked
  File "<frozen importlib._bootstrap_external>", line 883, in exec_module
  File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
  File "/home/kamikaze/.virtualenvs/pytermgui/lib/python3.10/site-packages/pytermgui-4.3.0-py3.10.egg/pytermgui/__init__.py", line 21, in <module>
    from .parser import *
  File "/home/kamikaze/.virtualenvs/pytermgui/lib/python3.10/site-packages/pytermgui-4.3.0-py3.10.egg/pytermgui/parser.py", line 120, in <module>
    from .terminal import terminal
  File "/home/kamikaze/.virtualenvs/pytermgui/lib/python3.10/site-packages/pytermgui-4.3.0-py3.10.egg/pytermgui/terminal.py", line 443, in <module>
    terminal = Terminal()
  File "/home/kamikaze/.virtualenvs/pytermgui/lib/python3.10/site-packages/pytermgui-4.3.0-py3.10.egg/pytermgui/terminal.py", line 224, in __init__
    self.pixel_size: tuple[int, int] = self._get_pixel_size()
  File "/home/kamikaze/.virtualenvs/pytermgui/lib/python3.10/site-packages/pytermgui-4.3.0-py3.10.egg/pytermgui/terminal.py", line 242, in _get_pixel_size
    output = getch()[4:-1]
  File "/home/kamikaze/.virtualenvs/pytermgui/lib/python3.10/site-packages/pytermgui-4.3.0-py3.10.egg/pytermgui/input.py", line 372, in getch
    raise KeyboardInterrupt("Unhandled interrupt") from error
KeyboardInterrupt: Unhandled interrupt

Hmm, interesting. Do other terminals also exhibit the behaviour?

urxvt and it works fine.

I guess they might be missing the ability to report terminal pixel dimensions. I think I have a fix in mind for this, though it involves a feature introduced in the compositor branch, so I might need to port it over.
The new feature is the new getch_timeout function, which calls getch but applies some timeout under which a result has to come in. We could do a tiny timeout (enough for the terminal to respond, not enough for the user to notice), and just return a default size of some resolution.

It's also worth noting that AFAIK (or remember) this specific functionality is currently only used for SVG exports, and was originally implemented as a preparatory step for Image support. A feature which is still yet to come to fruition, since I decided on focusing on quality of life and stability instead of flashy things that ultimately suffer from a slow compositor.


Add support for CRBRUS bridged from Osmosis

Cerberus chain was shut down earlier this year (see here: https://medium.com/@windpowerstake/closing-cerberus-c74df872d669) and CRBRUS token is now native to Osmosis. These changes will allow CRBRUS that has been bridged from Osmosis to other chains to be shown in mintscan.

Happy new year cosmostation team! Let me know if I can answer any questions to help move this forward. Cheers

Thank you for providing us with the information. We also wish for the success of the CRBRUS.
After several internal reviews, we made several modifications and merged the PR.

Firstly, we modified the display of the origin_type as “ibc” which is incorrect. So, we modified the depth of the chain.
For some of the chains that does not have liquidity (e.g. Secret, Stargaze), the token information is not registered
Some errors were found in the code in the PR that you submitted. So, we closed the submitted PR and applied the fixes individually on the following chains (Injective, Kujira, Neutron, Sei, Terra)

Thanks for this! I did notice that CRBRUS is not showing up on Terra (phoenix-1) chain.


⚠ linktree/kiondaga has degraded performance

In 6611414, linktree/kiondaga (https://linktr.ee/kiondaga) experienced degraded performance:

HTTP code: 200
Response time: 69 ms

Resolved: linktree/kiondaga performance has improved in 57f9a82 after 14 minutes.


RFE: add cloud provider sdk methods to fetch objects (in addition to type=url)

The url type property (in runtime_parameters, extension_services, post_onboard_enables, pre_onboard_enable) results in a simple HTTP get request to fetch scripts, parameters or declarations. I'm asking for the possibility, in addition, to use the cloud-vendor (through its sdk) methods to fetch the refered objects.
Example: with AWS, have a s3 type property with the bucket+key information as parameters.
The request to fetch the object will be executed with, for ex. the s3.getObject method.
Goal: be able to place those scripts, parameters or declarations on a bucket and comply to stricter enterprise policies, such as

better ACL/bucket policy definition (using the cloud vendor sdk method, we can use the instance_profile role in the policies)
bucket is set to SSE-KMS encryption. Using the instance_profile role, with according policies, the encrypted objects can be fetched.

With the actual code, AFAIK it's not possible to satisfy those requirements
Note: I refer to AWS just because of my prio use-case. However, it makes sense to do the same for all cloud providers.

Thanks for your request. We are now tracking this internally with ID ESECLDTPLT-2951.

Hi @amolari, this RFE is still in our backlog, but here are examples for all three clouds that will let you download and install AT packages from storage using IAM tokens with the current code:
# aws
controls:
  logLevel: info
  logFilename: /var/log/cloud/bigIpRuntimeInit.log
pre_onboard_enabled: []
runtime_parameters:
  - name: INSTANCE_PROFILE
    type: url
    value: 'http://<IP_ADDRESS>/latest/meta-data/iam/security-credentials/'
  - name: TEMPORARY_CREDENTIALS
    type: url
    value: 'http://<IP_ADDRESS>/latest/meta-data/iam/security-credentials/{{{INSTANCE_PROFILE}}}'
bigip_ready_enabled:
  - name: download_packages
    type: inline
    commands:
      - "date=\"`date +'%a, %d %b %Y %H:%M:%S %z'`\"; signature_string=\"GET\n\n\n${date}\nx-amz-security-token:{{{TEMPORARY_CREDENTIALS.Token}}}\n/<myBucketName>/<myKey>/f5-appsvcs-templates-1.16.0-1.noarch.rpm\"; signature=`/bin/echo -en \"${signature_string}\" | openssl sha1 -hmac {{{TEMPORARY_CREDENTIALS.SecretAccessKey}}} -binary | base64`; authorization=\"AWS {{{TEMPORARY_CREDENTIALS.AccessKeyId}}}:${signature}\"; curl -s -H \"Date: ${date}\" -H \"X-AMZ-Security-Token: {{{TEMPORARY_CREDENTIALS.Token}}}\" -H \"Authorization: ${authorization}\" \"https://<myBucketName>.s3.amazonaws.com/<myKey>/f5-appsvcs-templates-1.16.0-1.noarch.rpm\" -o /var/config/rest/downloads/f5-appsvcs-templates-1.16.0-1.noarch.rpm"
extension_packages:
  install_operations:
    - extensionType: fast
      extensionVersion: 1.16.0
      extensionUrl: file:///var/config/rest/downloads/f5-appsvcs-templates-1.16.0-1.noarch.rpm
post_onboard_enabled: []

# azure
controls:
  logLevel: info
  logFilename: /var/log/cloud/bigIpRuntimeInit.log
pre_onboard_enabled: []
runtime_parameters:
  - name: ACCESS_TOKEN
    type: url
    query: access_token
    value: 'http://<IP_ADDRESS>/metadata/identity/oauth2/token?api-version=2018-02-01&resource=https%3A%2F%2Fstorage.azure.com%2F'
    headers:
      - name: Metadata
        value: true
      - name: x-ms-version
        value: '2017-11-09'
bigip_ready_enabled:
  - name: download_packages
    type: inline
    commands:
      - 'curl https://<myStorageAccountName>.blob.core.windows.net/<myContainerName>/f5-appsvcs-templates-1.16.0-1.noarch.rpm -o /var/config/rest/downloads/f5-appsvcs-templates-1.16.0-1.noarch.rpm -H "x-ms-version:2017-11-09" -H "Authorization: Bearer {{{ACCESS_TOKEN}}}"'
extension_packages:
  install_operations:
    - extensionType: fast
      extensionVersion: 1.16.0
      extensionUrl: file:///var/config/rest/downloads/f5-appsvcs-templates-1.16.0-1.noarch.rpm
post_onboard_enabled: []

# google
controls:
  logLevel: info
  logFilename: /var/log/cloud/bigIpRuntimeInit.log
pre_onboard_enabled: []
runtime_parameters:
  - name: ACCESS_TOKEN
    type: url
    query: access_token
    value: 'http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/<myServiceAccount>/token'
    headers:
      - name: Metadata-Flavor
        value: Google
bigip_ready_enabled:
  - name: download_packages
    type: inline
    commands:
      - 'curl https://storage.googleapis.com/storage/v1/b/<myBucketName>/f5-appsvcs-templates-1.16.0-1.noarch.rpm -o /var/config/rest/downloads/f5-appsvcs-templates-1.16.0-1.noarch.rpm -H "Authorization: Bearer {{{ACCESS_TOKEN}}}"'
extension_packages:
  install_operations:
    - extensionType: fast
      extensionVersion: 1.16.0
      extensionUrl: file:///var/config/rest/downloads/f5-appsvcs-templates-1.16.0-1.noarch.rpm
post_onboard_enabled: []

@mikeshimkus
About the GCP use-case I have the issue that the workaround cannot be directly used for the Vault secretProvider.
My declaration has:
  - name: GLOBAL
    type: secret
    verifyTls: true
    secretProvider:
      type: Vault
      environment: hashicorp
      vaultServer: https://vault.xxxxx.com
      appRolePath: /v1/namespace/auth/approle/login
      secretsEngine: kv2
      secretPath: namespace/secret/data/global
      field: data
      version: '1'
      authBackend:
        type: approle
        roleId:
          type: url
          value: https://storage.googleapis.com/storage/v1/b/<myBucketName>/role-id_file
        secretId:
          type: inline
          value: <secret_id>
          unwrap: true

My buckets are not public => auth is required
The issue are in the roleId:

AFAIK there are no header (Authorization Bearer) I could configure here
if point 1 was possible, I guess I cannot rely on another runtime-parameter (in your example ACCESS_TOKEN) ?

At this time my workaround is to fetch the file with curl (auth bearer) locally and point the url value to file:///.
However, I do not like much this approach, as the role-id value is permanent in time (unlike the secret-id) and getting that credential leaked would break the tight security in place.

Hi @amolari, according to Vault best practices the role ID isn't considered sensitive, however it makes sense to limit its exposure. If it needs to be treated like a secret, you could store it in Google secrets manager, retrieve it using the secrets provider, and reference the returned value using the "inline" type for roleId instead of "url".
Another option would be to use the metadata runtime parameter time to retrive it from instance metadata, also using the roleId inline type. Or you could bake it into the image as Hashicorp suggests and use the local file URL.
The internal issue we currently have is for securely downloading files to the device, we would need a new issue for extending that bucket authentication to HTTP requests made by other providers. I will chat with the product owners and report back here.

@amolari This could work as well:
controls:
  logLevel: info
  logFilename: /var/log/cloud/bigIpRuntimeInit.log
runtime_parameters:
  - name: ACCESS_TOKEN
    type: url
    query: access_token
    value: 'http://metadata.google.internal/computeMetadata/v1/instance/service-accounts/<myServiceAccount>/token'
    headers:
      - name: Metadata-Flavor
        value: Google
  - name: ROLE_ID
    type: url
    value: 'https://storage.googleapis.com/storage/v1/b/<myBucketName>/role-id_file'
    headers:
      - name: Authorization
        value: Bearer {{{ACCESS_TOKEN}}}
  - name: GLOBAL
    type: secret
    verifyTls: true
    secretProvider:
      type: Vault
      environment: hashicorp
      vaultServer: https://vault.xxxxx.com
      appRolePath: /v1/namespace/auth/approle/login
      secretsEngine: kv2
      secretPath: namespace/secret/data/global
      field: data
      version: '1'
      authBackend:
        type: approle
        roleId:
          type: inline
          value: {{{ROLE_ID}}}
        secretId:
          type: inline
          value: <secret_id>
          unwrap: true

It seems like runtime init only resolves parameters twice, not specifically related to the secret type. In this case it may require three rounds of parameter resolution.
Seems like implementing this as part of a secure downloader would be better, but need to examine that. If part of that were a built-in function to get IAM tokens, I think it would allow you to do this (but would need to be separate from downloader itself, since downloading to the device doesn't work for you).


LBA incompatibility

There are 2 problems:

UC makes Block implement InventoryProvider, which triggers LBA's sanity check when adding compatibility wrappers for InventoryProvider - which causes crashes like https://github.com/AlexIIL/SimplePipes/issues/43.
UC uses mixins to provider UC -> LBA compatibility, which were broken in LBA 0.7.0. I can submit a PR to make UC use LBA's existing API for adding compatibility wrappers instead of mixins though.

For the first issue I can think of two fixes:

UC stops making every block potentially an inventory, and instead takes a different path for providing UC -> vanilla compatibility. I don't know how feasible this is though, or what this would mean for UC users. (LBA currently requires every block that provides an inventory to implement InventoryProvider themselves if they want vanilla users to see it's inventory, I guess that implies UC users would need to do the same).
LBA downgrades the sanity check to a warning rather than a crash when adding "compatibility" wrappers. I'd prefer not to do this as I think pretending every Block is an inventory bad idea (and will prevent any later wrappers from adding compatibility), but it's technically the simplest solution.

If LBA has an API for adding compat wrappers, that would be great! I'll look into something better for UC intercompat.


Adding an md5 checksum of bzrlib/index.py

Adding an md5 checksum of bzrlib/index.py to check that it has been patched.

Thanks! :-)


added missing $ to .Release.Namespace fix for #14412

Which issue this PR fixes

fixes #14412

Duplicate of #14425, but that one is not passing the checks.
/approve
/ok-to-test

/retest

/lgtm
thanks for contributing and congrats on your first contribution!


fix: rsbuild scss module mangle

Summary

Closed https://github.com/web-infra-dev/rspack/issues/5968

Require Documentation?

[ ] No
[ ] Yes, the corresponding rspack-website PR is __

!canary


Checkout fails

Run actions/checkout@v3
  with:
    fetch-depth: 0
    repository: pyiron/pyiron_vasp
    ssh-strict: true
    persist-credentials: true
    clean: true
    sparse-checkout-cone-mode: true
    fetch-tags: false
    lfs: false
    submodules: false
    set-safe-directory: true
Error: Input required and not supplied: token

For example in https://github.com/pyiron/pyiron_contrib or https://github.com/pyiron/pyiron_vasp

I won't be able to look at this until tomorrow at the absolute earliest; the first link doesn't indicate the error clearly to me, and the second link is a 404.

All the pull requests in pyiron contrib fail https://github.com/pyiron/pyiron_contrib/pull/912 - https://github.com/pyiron/pyiron_contrib/pull/918

The second is currently a private repository

So this seems to only be occurring for the dependabot PRs (possibly for other non-member fork PRs, but at least dependabot is the thing that's causing trouble), there's no trouble on my own PRs.
In addition to the token failure, UpdateDependabotPR / pyiron / dependabot (pull_request_target): Run pyiron/actions/update-env-files@main is failing with
Traceback (most recent call last):
  File "/home/runner/work/_actions/pyiron/actions/main/update-env-files/../.support/update_environment.py", line 116, in <module>
    raise ValueError(f"Title of a dependabot PR 'Bump <package> from <version> to <version>' expected, "
ValueError: Title of a dependabot PR 'Bump <package> from <version> to <version>' expected, but got Bump pyiron-base from 0.6.8 to 0.6.9 .ci_support/environment.yml /home/runner/work/_actions/pyiron/actions/main/update-env-files/../.support/pypi_vs_conda_names.json
Error: Process completed with exit code 1.

Which I guess is related to #56. Could that failure be causing the other tests to break?

Ok, indeed, check here -- the dependabot commit has always been failing the CI with such a message! It's just that after that, the centralized CI makes another commit propagating the dependabot changes to other files, and runner has all the necessary secrets!

Indeed, this is just the new issue #61 letting us once again see the known issue #44

Ah, ok, not quite just those two as dependabot should be getting extra power from on: pull_request_target. Let me see if that is (a) real and not just me failing to notice it was the runner commits that were actually working, and (b) different enough from #44 to warrant reopening this...

Ok, so the fork commit from dependabot itself fails the push-to-main CI, but it safely triggers pyiron runner to commit other updates and these have the power to run the push-to-main CI. So #44 is still interesting for non-dependabot forks, but solving #61 really is the heart of this problem.


can you provide resnet50_ibn_a pre_model?

The link of the pre-trained models are provided in the table in README.
The link of resnet50_ibn_a pre_model is https://github.com/XingangPan/IBN-Net/releases/download/v1.0/resnet50_ibn_a-d9d0bb7b.pth

@XingangPan
In the paper, you use IN+BN in residuals 2 through 4, but I want to use it in residuals 2, some of the errors are as follows:
“KeyError: 'layer2.0.bn1.IN.weight”

@H-YunHui You cannot use the pretrained model weight after changing the model. Alternatively, you may rename the layer that you changed to avoid confilict, and load the model with strict=False.


Camera: unlabeled input pad 1 on filter Parsed_overlay_0

I'm receiving following error when trying to add camera overlay:

Cannot find a matching stream for unlabeled input pad 1 on filter Parsed_overlay_0

$ : ffscreencast -c
/opt/local/bin/ffmpeg -hide_banner -loglevel info -thread_queue_size 512 -f avfoundation  -i "1" -thread_queue_size 512 -f avfoundation -video_size 160x120 -framerate-i "0" -c:v libx264 -crf 0 -preset ultrafast -filter_complex 'overlay=main_w-overlay_w-10:main_h-overlay_h-10' -threads 0 "/Users/rafi/Desktop/Screencast 2017-03-17 at 15.06.11.mkv"
[avfoundation @ 0x7f88f2003400] Selected pixel format (yuv420p) is not supported by the input device.
[avfoundation @ 0x7f88f2003400] Supported pixel formats:
[avfoundation @ 0x7f88f2003400]   uyvy422
[avfoundation @ 0x7f88f2003400]   yuyv422
[avfoundation @ 0x7f88f2003400]   nv12
[avfoundation @ 0x7f88f2003400]   0rgb
[avfoundation @ 0x7f88f2003400]   bgr0
[avfoundation @ 0x7f88f2003400] Overriding selected pixel format to use uyvy422 instead.
[avfoundation @ 0x7f88f2003400] Stream #0: not enough frames to estimate rate; consider increasing probesize
Input #0, avfoundation, from '1':
  Duration: N/A, start:<PHONE_NUMBER>33, bitrate: N/A
    Stream #0:0: Video: rawvideo (UYVY / 0x59565955), uyvy422, 2880x1800, 1000k tbr, 1000k tbn, 1000k tbc
Cannot find a matching stream for unlabeled input pad 1 on filter Parsed_overlay_0

$ ffscreencast --test
[OK]   Operating system supported: Darwin
[OK]   ffmpeg found: /opt/local/bin/ffmpeg
[OK]   [OSX]: AVFoundation available in ffmpeg.
          + Desktop recording possible.
          + Sound recording possible.
          + Camera overlay possible.

$ ffscreencast --list
Available screen recording devices (monitors):

[1] Capture screen 0

Available audio recording devices (monitors):

[0] Wirecast Virtual Microphone
[1] Built-in Microphone
[2] Boom2Device

Available camera recording devices:

[0] FaceTime HD Camera (Built-in) (160x120@ 29.970000]fp 160x120@ 25.000000]fp 160x120@ 23.999981]fp 160x120@ 14.999993]fp 176x144@ 29.970000]fp 176x144@ 25.000000]fp 176x144@ 23.999981]fp 176x144@ 14.999993]fp 320x240@ 29.970000]fp 320x240@ 25.000000]fp 320x240@ 23.999981]fp 320x240@ 14.999993]fp 352x288@ 29.970000]fp 352x288@ 25.000000]fp 352x288@ 23.999981]fp 352x288@ 14.999993]fp 480x360@ 29.970000]fp 480x360@ 25.000000]fp 480x360@ 23.999981]fp 480x360@ 14.999993]fp 640x480@ 29.970000]fp 640x480@ 25.000000]fp 640x480@ 23.999981]fp 640x480@ 14.999993]fp 960x540@ 29.970000]fp 960x540@ 25.000000]fp 960x540@ 23.999981]fp 960x540@ 14.999993]fp 1024x576@ 29.970000]fp 1024x576@ 25.000000]fp 1024x576@ 23.999981]fp 1024x576@ 14.999993]fp 1280x720@ 29.970000]fp 1280x720@ 25.000000]fp 1280x720@ 23.999981]fp 1280x720@ 14.999993]fp)

$ ffmpeg -version
ffmpeg version 3.2.4 Copyright (c) 2000-2017 the FFmpeg developers
built with Apple LLVM version 8.0.0 (clang-8<IP_ADDRESS>)
configuration: --prefix=/opt/local --enable-swscale --enable-avfilter --enable-avresample --enable-libmp3lame --enable-libvorbis --enable-libopus --enable-libtheora --enable-libschroedinger --enable-libopenjpeg --enable-libmodplug --enable-libvpx --enable-libsoxr --enable-libspeex --enable-libass --enable-libbluray --enable-lzma --enable-gnutls --enable-fontconfig --enable-libfreetype --enable-libfribidi --disable-indev=jack --disable-outdev=xv --enable-audiotoolbox --enable-sdl2 --mandir=/opt/local/share/man --enable-shared --enable-pthreads --cc=/usr/bin/clang --enable-vda --enable-videotoolbox --arch=x86_64 --enable-yasm --enable-libx265 --enable-gpl --enable-postproc --enable-libx264 --enable-libxvid
libavutil      55. 34.101 / 55. 34.101
libavcodec     57. 64.101 / 57. 64.101
libavformat    57. 56.101 / 57. 56.101
libavdevice    57.  1.100 / 57.  1.100
libavfilter     6. 65.100 /  6. 65.100
libavresample   3.  1.  0 /  3.  1.  0
libswscale      4.  2.100 /  4.  2.100
libswresample   2.  3.100 /  2.  3.100
libpostproc    54.  1.100 / 54.  1.100

@rafi could you paste the --dry output. Also as I don't have a Mac anymore, could you adjust the command from --dry to fit the suggestion here: https://stackoverflow.com/questions/35251122/using-ffmpeg-to-add-overlay-with-opacity

Changing the -filter_complex from original command (using --dry):
/opt/local/bin/ffmpeg -hide_banner -loglevel info -thread_queue_size 512 -f avfoundation -i "1" -thread_queue_size 512 -f avfoundation -video_size 160
x120 -framerate -i "0" -c:v libx264 -crf 0 -preset ultrafast -filter_complex 'overlay=main_w-overlay_w-10:main_h-overlay_h-10' -threads 0 "/Users/rafi/Desktop/Screencast 2017-03-18 at 15.12.04.mkv"

to
/opt/local/bin/ffmpeg -hide_banner -loglevel info -thread_queue_size 512 -f avfoundation -i "1" -thread_queue_size 512 -f avfoundation -video_size 160
x120 -framerate -i "0" -c:v libx264 -crf 0 -preset ultrafast -filter_complex "[0:v]setsar=sar=1[v];[v][1]blend=all_mode='overlay':all_opacity=0.7" -threads 0 "/Users/rafi/Desktop/Screencast 2017-03-18 at 15.12.04.mkv"

via recommendation from suggested post gives me this error:

Invalid file index 1 in filtergraph description [0:v]setsar=sar=1[v];[v][1]blend=all_mode='overlay':all_opacity=0.7.

Hmm that's  unfortunate. For now I am afraid you have to find a solution on your own, as I don't have a mac anymore to test things out.
I will leave this open for others to see who might provide some help.
Btw, could you try to discuss your problem on stackoverflow/reddit and see if you get a nice solution for your ffmpeg problem.

➜  Desktop ffscreencast --test
[OK]   Operating system supported: Darwin
[OK]   ffmpeg found: /usr/local/bin/ffmpeg
[OK]   [OSX]: AVFoundation available in ffmpeg.
+ Desktop recording possible.
+ Sound recording possible.
+ Camera overlay possible.
➜  Desktop ffscreencast --list
Available screen recording devices (monitors):
[1] Capture screen 0
Available audio recording devices (monitors):
[0] Built-in Microphone
Available camera recording devices:
[0] FaceTime HD Camera (Built-in) (160x120@ 29.970000]fp 160x120@ 25.000000]fp 160x120@ 23.999981]fp 160x120@ 14.999993]fp 176x144@ 29.970000]fp 176x144@ 25.000000]fp 176x144@ 23.999981]fp 176x144@ 14.999993]fp 320x240@ 29.970000]fp 320x240@ 25.000000]fp 320x240@ 23.999981]fp 320x240@ 14.999993]fp 352x288@ 29.970000]fp 352x288@ 25.000000]fp 352x288@ 23.999981]fp 352x288@ 14.999993]fp 480x360@ 29.970000]fp 480x360@ 25.000000]fp 480x360@ 23.999981]fp 480x360@ 14.999993]fp 640x480@ 29.970000]fp 640x480@ 25.000000]fp 640x480@ 23.999981]fp 640x480@ 14.999993]fp 960x540@ 29.970000]fp 960x540@ 25.000000]fp 960x540@ 23.999981]fp 960x540@ 14.999993]fp 1024x576@ 29.970000]fp 1024x576@ 25.000000]fp 1024x576@ 23.999981]fp 1024x576@ 14.999993]fp 1280x720@ 29.970000]fp 1280x720@ 25.000000]fp 1280x720@ 23.999981]fp 1280x720@ 14.999993]fp)
➜  Desktop ffmpeg -version
ffmpeg version 3.2.4 Copyright (c) 2000-2017 the FFmpeg developers
built with Apple LLVM version 8.1.0 (clang-802.0.38)
configuration: --prefix=/usr/local/Cellar/ffmpeg/3.2.4 --enable-shared --enable-pthreads --enable-gpl --enable-version3 --enable-hardcoded-tables --enable-avresample --cc=clang --host-cflags= --host-ldflags= --enable-ffplay --enable-libmp3lame --enable-libvorbis --enable-libvpx --enable-libx264 --enable-libx265 --enable-libxvid --enable-opencl --disable-lzma --enable-vda
libavutil      55. 34.101 / 55. 34.101
libavcodec     57. 64.101 / 57. 64.101
libavformat    57. 56.101 / 57. 56.101
libavdevice    57.  1.100 / 57.  1.100
libavfilter     6. 65.100 /  6. 65.100
libavresample   3.  1.  0 /  3.  1.  0
libswscale      4.  2.100 /  4.  2.100
libswresample   2.  3.100 /  2.  3.100
libpostproc    54.  1.100 / 54.  1.100
➜  Desktop  ffscreencast -c0
/usr/local/bin/ffmpeg -hide_banner -loglevel info -thread_queue_size 512 -f avfoundation  -i "1" -thread_queue_size 512 -f avfoundation -video_size 160x120 -framerate   -i "0" -c:v libx264 -crf 0 -preset ultrafast -filter_complex 'overlay=main_w-overlay_w-10:main_h-overlay_h-10' -threads 0 "/Users/kewin/Desktop/Screencast 2017-03-31 at 23.01.45.mkv"
[avfoundation @ 0x7fde87000000] Selected pixel format (yuv420p) is not supported by the input device.
[avfoundation @ 0x7fde87000000] Supported pixel formats:
[avfoundation @ 0x7fde87000000]   uyvy422
[avfoundation @ 0x7fde87000000]   yuyv422
[avfoundation @ 0x7fde87000000]   nv12
[avfoundation @ 0x7fde87000000]   0rgb
[avfoundation @ 0x7fde87000000]   bgr0
[avfoundation @ 0x7fde87000000] Overriding selected pixel format to use uyvy422 instead.
[avfoundation @ 0x7fde87000000] Stream #0: not enough frames to estimate rate; consider increasing probesize
Input #0, avfoundation, from '1':
Duration: N/A, start: 27320.825167, bitrate: N/A
Stream #0:0: Video: rawvideo (UYVY / 0x59565955), uyvy422, 2560x1600, 1000k tbr, 1000k tbn, 1000k tbc
Cannot find a matching stream for unlabeled input pad 1 on filter Parsed_overlay_0
➜  Desktop  ffscreencast -a0
/usr/local/bin/ffmpeg -hide_banner -loglevel info -thread_queue_size 512 -f avfoundation  -i "1" -thread_queue_size 512 -f avfoundation -ac 2 -i ":0" -c:a libfaac -c:v libx264 -crf 0 -preset ultrafast -threads 0 "/Users/kewin/Desktop/Screencast 2017-03-31 at 23.01.53.mkv"
[avfoundation @ 0x7ff5c1002600] Selected pixel format (yuv420p) is not supported by the input device.
[avfoundation @ 0x7ff5c1002600] Supported pixel formats:
[avfoundation @ 0x7ff5c1002600]   uyvy422
[avfoundation @ 0x7ff5c1002600]   yuyv422
[avfoundation @ 0x7ff5c1002600]   nv12
[avfoundation @ 0x7ff5c1002600]   0rgb
[avfoundation @ 0x7ff5c1002600]   bgr0
[avfoundation @ 0x7ff5c1002600] Overriding selected pixel format to use uyvy422 instead.
[avfoundation @ 0x7ff5c1002600] Stream #0: not enough frames to estimate rate; consider increasing probesize
Input #0, avfoundation, from '1':
Duration: N/A, start: 27327.773167, bitrate: N/A
Stream #0:0: Video: rawvideo (UYVY / 0x59565955), uyvy422, 2560x1600, 1000k tbr, 1000k tbn, 1000k tbc
Input #1, avfoundation, from ':0':
Duration: N/A, start: 27328.415578, bitrate: 2822 kb/s
Stream #1:0: Audio: pcm_f32le, 44100 Hz, stereo, flt, 2822 kb/s
Unknown encoder 'libfaac'

I just want to chime in here because I hit a similar error but a different cause and this helps somebody else.
I was calling -acodec aac before a second input -i watermark.png and I got the same error.
After moving my -acodec aac to after the second input and before the output, everything worked.

For what it's worth, I got the exact same error as @rafi on my Mac.
I was able to get the desired result (but not fix the ffscreencast command) using this command:
ffmpeg -thread_queue_size 50 -f avfoundation -framerate 30 -i "1" -thread_queue_size 50 -f avfoundation -framerate 30 -video_size 640x480 -i "0" -c:v libx264 -crf 18 -preset ultrafast -filter_complex 'overlay=main_w-overlay_w-10:main_h-overlay_h-10' -capture_cursor 1 -capture_mouse_clicks 1 -r 30 ~/Desktop/out.mkv
Perhaps a better man could piece together what is needed into the original ffscreencast command


Implement forgot password feature

Previous team has left the forgot password feature under maintenance and haven't implemented it. This is an important feature to be covered as if the user forgets is password, there will be no way for him to check is tasks which will be a critical issue for him.
Previous Team

Idea on how to build the feature
->When the user clicks on the forgot password link, we need to ask him for his email details so that we can send a reset password link to his mail. Once the user clicks the link, it needs to redirect to our resetPassword page which has a new password field in there. Build on this idea to implement this feature.

For this feature we have integrated with Gmail, When ever the users clicks forget password we are sending them a mail with password reset link and user token. Using this link the user will set his new password and it gets updated in Mongodb.


[ListItem] Fix primaryTogglesNestedList not working with checkbox #4753

Closes #4753
Closes #4947

[x] PR has tests / docs demo, and is linted.
[x] Commit and PR titles begin with [ComponentName], and are in imperative form: "[Component] Fix leaky abstraction".
[x] Description explains the issue / use-case resolved, and auto-closes the related issue(s) (http://tr.im/vFqem).

@rafaelsales Thanks!


Local launch: misleading suggestion

configuration file example is given in yaml (https://github.com/the-guild-org/conductor/blob/master/README.md#L55)
However, when I try to launch locally conductor with cargo run --bin conductor I have that error:
thread 'main' panicked at /Users/trkohler/open-source/conductor/libs/config/src/lib.rs:816:25:
Failed to read config file "./config.json": No such file or directory (os error 2)

config.yaml doesn't seem to work.
Is that expected behavior?

tried with json and it worked. So seems like only json works right now?

tried with json and it worked. So seems like only json works right now? running from fresh master
hey @trkohler, yaml also works, the issue is that it defaults to reading the configuration file from config.json when not specified, the first argument is the one it uses for config file location. so if you want to use yaml, you can run:

cargo run --bin conductor ./test_config/config.yaml

I added some explanations to readme regarding that behaviour because I couldn't guess it just from the reading the source code (I didn't read too deep, to be honest). Hopefully it will save few mins for the next contributor during onboarding!


.github/workflows/aws.ymlCreate aws.yml

Closes bunqCommunity/bunqDesktop#<issue_number>

What is this for?


🌐 Add Korean translation for docs/ko/docs/deployment/index.md

Translates docs/ko/docs/deployment/index.md and add deploy menu in Korean.
related: https://github.com/tiangolo/fastapi/issues/2017

omg I deployed other translate commit at this PR. I just recovered this Branch.

📝 Docs preview for commit 46dfd26d92d17f6c73f5e9010fa0cf60f02e5384 at: https://649a1d1921995415af357f36--fastapi.netlify.app

Thanks for your contribution! @jujumilk3 😄
And thanks for the review @peacekimjapan ☕


Android device without sleep

Hello.
I use AOSP 7.1.1 and it looks like dnscrypt-proxy do not allow device sleep for many hours. For example, my phone can use 1% of battery per 3-4 hours of deep sleep. When I installed dnscrypt this time stays around 30-60 minutes. So dnscrypt use ~10% every night. Of course I checked my theory before writing this issue.
I installed dnscrypt. Many hours without sleep. -10%.
I "killall dnscrypt" and fixed iptables. -3% for equal time period.
I could not find any information related to wakelocks and this issue in dumpsys batterystats. So i do not know what info i can give you.
Before this new nougat rom I had 5.1. And I hadn't any problems.
P..S. I use armv8 version compiled by myself without any changes.

Not sure Doze can handle root apps.

I recommend either Greenify or Purify. I personally use Greenify. Note that Greenify can use the Xposed framework, but no longer relies as heavily on Xposed as it once did.
Amplify requires Xposed and hasn't been updated in a while, but it allows you to exercise fine-grained control over wakelocks, alarms, and so forth. It can be combined with Greenify, and I believe it can be combined with Purify.
Having one of these tools or a similar tool installed is essentially required to obtain the best possible battery life, whether or not you're using dnscrypt-proxy.

@rmenessec
Are you really think this method is a good alternative to making changes to source code?
And of course this method will not work I think.
Maybe developer can answer us about what does he think

@avently , I'm not sure what you mean by "not work". If you mean that a doze-mode manager might cause dnscrypt-proxy to stop functioning temporarily... yes, that's possible. If that happens, either whitelist dnscrypt-proxy or simply uninstall the manager. You have nothing to lose but a few minutes of your time.
If using one of the managers I mentioned does not help or temporarily impairs dnscrypt-proxy, there's also DNS Manager, which uses the dnscrypt-proxy code, and may have a solution or work-around for the issue you're experiencing.
It doesn't bother me whether or not you use one of my suggestions. I was merely attempting to be helpful, since I see you've waited a few weeks for a response.  I mean the developer may know the best way to do what we need. This is temporary solution and external apps needed. That's why it's not good.
As I already mentioned on Android 5.1 Dnscrypt works fine. So why not to fix it in Android 7.1?

@avently Greenify works best with Xposed, but does not require it. It doesn't require root, either, but I believe some features will be unavailable without it.
I realize that the lack of Xposed will likely prevent you from using Amplify, yes; I wasn't sure if anything had changed since February.
I haven't tried Purify, but it doesn't mention root or Xposed requirements. Then again, it doesn't really describe how it works; Greenify may be the optimal choice, here. Well, that, or DNS Manager, depending on whether that has doze issues. DNS Manager or something similar may be a good idea in any case, in order to ensure that your DNS settings aren't overridden when you switch from one network to another. Override DNS can also do this, but doesn't include DNSCrypt support that I'm aware of.
I believe there are a couple of other doze-mode managers out there, but I haven't looked in a few months, and I don't recall what the requirements were.
It's possible that dnscrypt-proxy simply must always be running, short of integrating DNSCrypt support directly into Android; if it's not running, DNS requests can't be proxied through it. If your device is dozing and a network request involving DNS is triggered, dnscrypt-proxy would have to run at least briefly in order to proxy the request.

I did this a while ago, caveats mentioned aside it works ok most of the time.

@licaon-kter do you have the device that sleeps good on Android Nougat with your apk?

Forgot to test on N, just LL and MM. Feel free to test and report back :+1: :)

@rmenessec
Greenify doesn't work. I tested several nights.

@avently I'm sorry to hear that. I just became aware of one other no-Xposed solution (or workaround, if you prefer) that might be worth testing: Naptime.

@rmenessec already tried. Same result. Maybe in the next version of Android it will be fixed...

@avently Same result with DNS Manager instead of dnscrypt-proxy?

As dnscrypt-proxy

https://i.imgur.com/5p0ofDl.png
Marked spot was my first night with DNScrypt. Looks to me like it was able to deep sleep fine since % didn't move.

I updated Android many times since March. Now I don't see any problems with dnscrypt. So I'm closing the issue.


Fatal error while fetching proposerduties

Client: sigp/lighthouse:v1.5.2 (connected to mainnet)
Chaind: wealdtech/chaind:0.4.2
Postgres: postgres:13.4
Running on docker.
The chaind container fails with the  following:
{"level":"debug","service":"blocks","impl":"standard","slot":1013688,"time":"2021-09-20T19:09:13Z","message":"No beacon block obtained for slot"}
{"level":"fatal","service":"proposerduties","impl":"standard","epoch":65992,"error":"failed to fetch metadata: failed to obtain metadata: tx is closed","time":"2021-09-20T19:09:13Z","message":"Failed to obtain metadata"}

Nothing else is logged after that.
The container is then restarted by the docker daemon only to fail again with the same error 5 minutes later.
Postgres logs this:
2021-09-20 19:12:31.737 UTC [23869] LOG:  unexpected EOF on client connection with an open transaction

around the time of the failure. Not sure if relevant or a red herring.
Downgrading to 0.3.5 resolved the issue.  Let me know how I can provide more details, chaind doesn't seem to log stack traces.

That's impossible to reproduce as is, I am closing it.


Konflux update test-component-pac-gjsxdt

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resource detection for 2nd gen of Cloud Functions works incorrectly

The current version of the library (3.10.0) incorrect detects resource as Cloud Run when the code runs on the 2nd generation of the Cloud Functions.
The problem is that in 2nd generation the environment has full set of Cloud Run environment variables which does not happened when running 1st generation of the Cloud Functions. As a result, the code detects Cloud Run before it reaches to if that detects Cloud Functions.

A quick fix would be to enforce the order in detecting resource type by validating for Cloud Functions before Cloud Run.


Add test and check codecov

Add tests for each method and setup codecov

Some tests have been added, but they could be improved and do not cover 100% of the code or all edge cases. Would be great to connect to codecov as welll


etc: default advertise addr should be empty.

@ngaut @c4pt0r @BusyJay @disksing @tiancaiamao

LGTM

Coverage decreased (-0.3%) to 81.757% when pulling a3c73bf688ae0c354361590ff90087b1376d8e07 on siddontang/fix-etc into b83326408abe90bd89c5cb99b88a8d6b6611cc5e on master.

LGTM


Would this project be good for diagnosing in mobile apps?

Hey there, first of all I would like to thank you for creating this project.
I'm part of a telemedicine company that has native iOS and Android apps, and we were wondering whether using this diagnostics tool would be a good approach for those apps. If not, is there any similar tool that can be easily embedded on Android and iOS?
Cheers!

Hey @sevgit, thanks for reaching out!
We do not believe that Twilio has anything like this app for iOS and Android at the moment unfortunately.
This Video Diagnostics App could be used for iOS/Android users to test connectivity with Twilio's servers, and their network qualities, but all of the device related tests will most likely not be as helpful as they are primarily used to make sure that a user has the correct browser permissions in place.
Hope that helps and have a great weekend!


Hey, added my project to the ecosystem of Base kindly approve

added logo and details about my project which works on Base ecosystem.
Kindly approve it to show it on your website
ultimatedeal.net

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Hi there! Thanks for building on Base! Since this app seems relatively new, please resubmit in 30 days so our team can review the progress as per our submission guidelines:

Active development and community engagement can be observed without issue


IUserStore doesn't respect to custom type with custom data provider

Hello,
Describe the bug
I tired to use Dapper with Identity, everything works, but I faced an issue. IUserStore and IRoleStore doesn't respect to provided primary key data type (I like to use int as primary keys's data type), here is what I did:
public class ApplicationUser : IdentityUser<int>
{
}

public class ApplicationRole : IdentityRole<int>
{
}

public class CustomUserStore :
    IUserStore<ApplicationUser>,
    ...

There error is:

'CustomUserStore' does not implement interface member 'IUserStore.FindByIdAsync(string, CancellationToken)'

Same for CustomRoleStore
For now I add FindByIdAsync(string, CancellationToken) and inside the method I convert data types, but I think this 2 interfaces should respect to the provided data type. I googled, but I could not find any solution.
To Reproduce
Steps to reproduce the behavior:

Create new ASP.NET Core web project (v2.2.x)
Try to use Dapper instead of EF for identity
With custom data type as UserId and RoleId
Error already mentioned.

Expected behavior
IUserStore and IRoleStore should respect to the provided data type some how.
Screenshots

Additional context
VS 2017 v15.9.3
.NET Core v2.2.101
Thanks.

@blowdart Thanks for the point, obviously I didn't look at hole project (Identity I mean) and I'm not pro as you are, as far as I know it can be happened but maybe I'm wrong, so you saying, it won't be supported by Identity?

@blowdart I tried UserStoreBase, the string userId still there, so it does't help in this issue.

@HaoK can you give some guidance here?

This is by design, you can persist the primary key as whatever you want, but the manager apis always use string for the ids, so your stores need to implement FindById which does a lookup via string id

@HaoK by design! you should re-design then, when you accept custom type, all parts should respect that type. I just see hard-coded type there without any acceptable reason, but your are the boss.


Ensures filename parameter to FileResponse set for StaticFiles response.

Passing a filename is required for mimetype detection, without which static files were always served with content-type: application/octet-stream
PR Checklist

[ ] Have you followed the guidelines in CONTRIBUTING.md?
[ ] Have you got 100% test coverage on new code?
[ ] Have you updated the prose documentation?
[ ] Have you updated the reference documentation?

converted to draft as missing a test and I've run out of time for now.. will do in a few hours time.


Handle special @'s

I suspect special @'s like @here, @channel, or @<group> would break things

tested all 3 of those and they all register as non-mentions


WIP: Placeholder for KEP-4222 doc updates.

https://github.com/kubernetes/enhancements/issues/4222

/sig api-machinery

/close


Allow user to easily add a system message to each message

Currently user has to create a custom message converter class (yuck) or prepend to the user message, which isn't really correct.

In my mind this is a significant gap. Maybe we do something like:

messsage_transform = [AddSystemMessage("You are the best coding assistant around"), AlpacaToMessages()]

Addressed by #1366


CAMEL-20503: camel-http OAuth2 support for caching / refreshing tokens

Description

added caching oauth2 tokens for http component, and configuration parameters for caching
token is cached for combination of credentials and host: if username/password or authentication host changes, token will be queried again.

Tracking
https://issues.apache.org/jira/browse/CAMEL-20503

/component-test camel-http

Oh @kulagaIA you need to add the new option to the exclude list in WebsocketEndpoint and rebuild the source code, as this should only be for camel-http only.

Oh @kulagaIA you need to add the new option to the exclude list in WebsocketEndpoint and rebuild the source code, as this should only be for camel-http only.

done


System overview: Ignore RC pre-releases for RTL

What
RTL has tags also for RC (release candidate) pre-releases, you want to ignore them when checking for latest version.
Currently, without this change, will think v0.12.4-rc1 is the latest, after - v0.12.3.
How
Ignore git tags that contain "rc".
Scope

[ ] significant change to core configuration
[ ] independent bonus guide
[X] simple bug fix

Test & maintenance
Just check before / after bash oneliner result.

Anyone has more comments or plan to test this?

Anyone has more comments or plan to test this?

I tried to test it a few days ago but I encountered an issue while installing RTL so can't test it until my issue is resolved...
However, as @mktech-gh tested it, I could approve and merge it now if that's ok with you?
Also, what do you think of using your command in the main guide as well? as there is the same issue there.

updated the welcome script according to @kristapsk change and can confirm it works, i.e RTL is shown as updated

Awesome, thanks a log! :tada:


Config.js needs a rework

Things aren't objects that could be, like the player variables are all just prefixed by player, rather than being part of an object.
Makes parsing it unwieldy.

The game code is getting a mostly complete rewrite so this issue is no longer relevant.


Nitrokey FIDO2 update not working

Nitrokey FIDO2 update fails on fetching the binaries.
Tested commit: 0.3.2.nitrokey-44-g6384dbc (current tip of daringer/36-fido_08 branch);
178       DEBUG urllib3.connectionpool Starting new HTTPS connection (1): api.github.com:443
420       DEBUG urllib3.connectionpool https://api.github.com:443 "GET /repos/Nitrokey/nitrokey-fido2-firmware/releases/latest HTTP/1.1" 200 1808
422       DEBUG       root print: Critical error:
422       DEBUG       root print: Failed to determine latest release (url)
422       DEBUG       root print: assets:
422       DEBUG       root print: ('nitrokey-fido2-firmware-2.1.0-all-to_flash.hex', 'https://github.com/Nitrokey/nitrokey-fido2-firmware/releases/download/2.1.0.nitrokey/nitrokey-fido2-firmware-2.1.0-all-to_flash.hex')
422       DEBUG       root print: ('nitrokey-fido2-firmware-2.1.0-app-to_sign.hex', 'https://github.com/Nitrokey/nitrokey-fido2-firmware/releases/download/2.1.0.nitrokey/nitrokey-fido2-firmware-2.1.0-app-to_sign.hex')

jip confirmed, and the reason is that a ".json" file is expected as an asset, like for the last releases.
will this be skipped for this an future releases ?

Ok, the issue is the reporter here apparently :D Forget to add it.
Uploaded and tested on Fedora 31. Closing as fixed.


WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details

Hi All,
i'm trying to train train my own models with the tensorflow 2.2, by using predefined models used this link but i'm facing the above error. Can anyone please let me know anyone was able to run the pretrained models on tensorflow 2.2??
thanks in advance.

@Deepthi-Jain
Please, fill issue template..
Please, let us know which pretrained model you are using and share related code .Thanks!

Having similar issue with efficientdet_d1_coco17_tpu-32 pre-trained model.

@ravikyram I'm having the same issue with TF 2.3.0 Object Detection and Efficientnet D1

@ravikyram I'm having the same issue with TF 2.3.0 Object Detection and Efficientnet D1
this will fix it
fine_tune_checkpoint_type: "fine_tune" instead of fine_tune_checkpoint_type: "detection"

I had the same problem. Changing  fine_tune_checkpoint_type: "classification"  to  fine_tune_checkpoint_type: "detection" in the config file, solved my problem. After solving the above problem, training job work but my model did not converge so I decreased the leraning_rate and momentum. After decreasing the learning_rate and momentum my model converged.
NOTE: If you got a resource exhausted error (OOM error) decrease the batch_size in the config file.
The final config file is like below:
# SSD with Resnet 50 v1 FPN feature extractor, shared box predictor and focal
# loss (a.k.a Retinanet).
# See Lin et al, https://arxiv.org/abs/1708.02002
# Trained on COCO, initialized from Imagenet classification checkpoint
# Train on TPU-8
#
# Achieves 34.3 mAP on COCO17 Val

model {
  ssd {
    inplace_batchnorm_update: true
    freeze_batchnorm: true
    num_classes: 3
    box_coder {
      faster_rcnn_box_coder {
        y_scale: 10.0
        x_scale: 10.0
        height_scale: 5.0
        width_scale: 5.0
      }
    }
    matcher {
      argmax_matcher {
        matched_threshold: 0.5
        unmatched_threshold: 0.5
        ignore_thresholds: false
        negatives_lower_than_unmatched: true
        force_match_for_each_row: true
        use_matmul_gather: true
      }
    }
    similarity_calculator {
      iou_similarity {
      }
    }
    encode_background_as_zeros: true
    anchor_generator {
      multiscale_anchor_generator {
        min_level: 3
        max_level: 7
        anchor_scale: 4.0
        aspect_ratios: [1.0, 2.0, 0.5]
        scales_per_octave: 2
      }
    }
    image_resizer {
      fixed_shape_resizer {
        height: 640
        width: 640
      }
    }
    box_predictor {
      weight_shared_convolutional_box_predictor {
        depth: 256
        class_prediction_bias_init: -4.6
        conv_hyperparams {
          activation: RELU_6,
          regularizer {
            l2_regularizer {
              weight: 0.0004
            }
          }
          initializer {
            random_normal_initializer {
              stddev: 0.01
              mean: 0.0
            }
          }
          batch_norm {
            scale: true,
            decay: 0.997,
            epsilon: 0.001,
          }
        }
        num_layers_before_predictor: 4
        kernel_size: 3
      }
    }
    feature_extractor {
      type: 'ssd_resnet50_v1_fpn_keras'
      fpn {
        min_level: 3
        max_level: 7
      }
      min_depth: 16
      depth_multiplier: 1.0
      conv_hyperparams {
        activation: RELU_6,
        regularizer {
          l2_regularizer {
            weight: 0.0004
          }
        }
        initializer {
          truncated_normal_initializer {
            stddev: 0.03
            mean: 0.0
          }
        }
        batch_norm {
          scale: true,
          decay: 0.997,
          epsilon: 0.001,
        }
      }
      override_base_feature_extractor_hyperparams: true
    }
    loss {
      classification_loss {
        weighted_sigmoid_focal {
          alpha: 0.25
          gamma: 2.0
        }
      }
      localization_loss {
        weighted_smooth_l1 {
        }
      }
      classification_weight: 1.0
      localization_weight: 1.0
    }
    normalize_loss_by_num_matches: true
    normalize_loc_loss_by_codesize: true
    post_processing {
      batch_non_max_suppression {
        score_threshold: 1e-8
        iou_threshold: 0.6
        max_detections_per_class: 100
        max_total_detections: 100
      }
      score_converter: SIGMOID
    }
  }
}

train_config: {
  fine_tune_checkpoint_version: V2
  fine_tune_checkpoint: "./test_data/ssd_resnet50/checkpoint/ckpt-0"
  fine_tune_checkpoint_type: "detection"
  batch_size: 10
  sync_replicas: true
  startup_delay_steps: 0
  replicas_to_aggregate: 5
  use_bfloat16: true
  num_steps: 100000
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
  data_augmentation_options {
    random_crop_image {
      min_object_covered: 0.0
      min_aspect_ratio: 0.4
      max_aspect_ratio: 3.0
      min_area: 0.5
      max_area: 1.0
      overlap_thresh: 0.0
    }
  }
  optimizer {
    momentum_optimizer: {
      learning_rate: {
        cosine_decay_learning_rate {
          learning_rate_base: .0005
          total_steps: 100000
          warmup_learning_rate: .0001
          warmup_steps: 1000
        }
      }
      momentum_optimizer_value: 0.5
    }
    use_moving_average: false
  }
  max_number_of_boxes: 100
  unpad_groundtruth_tensors: false
}

train_input_reader: {
  label_map_path: "./test_images/label_map.pbtxt"
  tf_record_input_reader {
    input_path: "./test_images/train.record-00000-of-00001"
  }
}

eval_config: {
  metrics_set: "coco_detection_metrics"
  use_moving_averages: false
}

eval_input_reader: {
  label_map_path: "./test_images/label_map.pbtxt"
  shuffle: false
  num_epochs: 1
  tf_record_input_reader {
    input_path:  "./test_images/val.record-00000-of-00001"
  }
}

I had the same problem. Changing fine_tune_checkpoint_type: "classification" to fine_tune_checkpoint_type: "detection" in the config file, solved my problem. After solving the above problem, training job work but my model did not converge so I decreased the leraning_rate and momentum. After decreasing the learning_rate and momentum my model converged.
NOTE: If you got a resource exhausted error (OOM error) decrease the batch_size in the config file.
The final config file is like below:
# SSD with Resnet 50 v1 FPN feature extractor, shared box predictor and focal
# loss (a.k.a Retinanet).
# See Lin et al, https://arxiv.org/abs/1708.02002
# Trained on COCO, initialized from Imagenet classification checkpoint
# Train on TPU-8
#
# Achieves 34.3 mAP on COCO17 Val

model {
  ssd {
    inplace_batchnorm_update: true
    freeze_batchnorm: true
    num_classes: 3
    box_coder {
      faster_rcnn_box_coder {
        y_scale: 10.0
        x_scale: 10.0
        height_scale: 5.0
        width_scale: 5.0
      }
    }
    matcher {
      argmax_matcher {
        matched_threshold: 0.5
        unmatched_threshold: 0.5
        ignore_thresholds: false
        negatives_lower_than_unmatched: true
        force_match_for_each_row: true
        use_matmul_gather: true
      }
    }
    similarity_calculator {
      iou_similarity {
      }
    }
    encode_background_as_zeros: true
    anchor_generator {
      multiscale_anchor_generator {
        min_level: 3
        max_level: 7
        anchor_scale: 4.0
        aspect_ratios: [1.0, 2.0, 0.5]
        scales_per_octave: 2
      }
    }
    image_resizer {
      fixed_shape_resizer {
        height: 640
        width: 640
      }
    }
    box_predictor {
      weight_shared_convolutional_box_predictor {
        depth: 256
        class_prediction_bias_init: -4.6
        conv_hyperparams {
          activation: RELU_6,
          regularizer {
            l2_regularizer {
              weight: 0.0004
            }
          }
          initializer {
            random_normal_initializer {
              stddev: 0.01
              mean: 0.0
            }
          }
          batch_norm {
            scale: true,
            decay: 0.997,
            epsilon: 0.001,
          }
        }
        num_layers_before_predictor: 4
        kernel_size: 3
      }
    }
    feature_extractor {
      type: 'ssd_resnet50_v1_fpn_keras'
      fpn {
        min_level: 3
        max_level: 7
      }
      min_depth: 16
      depth_multiplier: 1.0
      conv_hyperparams {
        activation: RELU_6,
        regularizer {
          l2_regularizer {
            weight: 0.0004
          }
        }
        initializer {
          truncated_normal_initializer {
            stddev: 0.03
            mean: 0.0
          }
        }
        batch_norm {
          scale: true,
          decay: 0.997,
          epsilon: 0.001,
        }
      }
      override_base_feature_extractor_hyperparams: true
    }
    loss {
      classification_loss {
        weighted_sigmoid_focal {
          alpha: 0.25
          gamma: 2.0
        }
      }
      localization_loss {
        weighted_smooth_l1 {
        }
      }
      classification_weight: 1.0
      localization_weight: 1.0
    }
    normalize_loss_by_num_matches: true
    normalize_loc_loss_by_codesize: true
    post_processing {
      batch_non_max_suppression {
        score_threshold: 1e-8
        iou_threshold: 0.6
        max_detections_per_class: 100
        max_total_detections: 100
      }
      score_converter: SIGMOID
    }
  }
}

train_config: {
  fine_tune_checkpoint_version: V2
  fine_tune_checkpoint: "./test_data/ssd_resnet50/checkpoint/ckpt-0"
  fine_tune_checkpoint_type: "detection"
  batch_size: 10
  sync_replicas: true
  startup_delay_steps: 0
  replicas_to_aggregate: 5
  use_bfloat16: true
  num_steps: 100000
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
  data_augmentation_options {
    random_crop_image {
      min_object_covered: 0.0
      min_aspect_ratio: 0.4
      max_aspect_ratio: 3.0
      min_area: 0.5
      max_area: 1.0
      overlap_thresh: 0.0
    }
  }
  optimizer {
    momentum_optimizer: {
      learning_rate: {
        cosine_decay_learning_rate {
          learning_rate_base: .0005
          total_steps: 100000
          warmup_learning_rate: .0001
          warmup_steps: 1000
        }
      }
      momentum_optimizer_value: 0.5
    }
    use_moving_average: false
  }
  max_number_of_boxes: 100
  unpad_groundtruth_tensors: false
}

train_input_reader: {
  label_map_path: "./test_images/label_map.pbtxt"
  tf_record_input_reader {
    input_path: "./test_images/train.record-00000-of-00001"
  }
}

eval_config: {
  metrics_set: "coco_detection_metrics"
  use_moving_averages: false
}

eval_input_reader: {
  label_map_path: "./test_images/label_map.pbtxt"
  shuffle: false
  num_epochs: 1
  tf_record_input_reader {
    input_path:  "./test_images/val.record-00000-of-00001"
  }
}

Still having the same issue on faster r-cnn resnet 101

I had the same problem. Changing fine_tune_checkpoint_type: "classification" to fine_tune_checkpoint_type: "detection" in the config file, solved my problem. After solving the above problem, training job work but my model did not converge so I decreased the leraning_rate and momentum. After decreasing the learning_rate and momentum my model converged.
NOTE: If you got a resource exhausted error (OOM error) decrease the batch_size in the config file.
The final config file is like below:
# SSD with Resnet 50 v1 FPN feature extractor, shared box predictor and focal
# loss (a.k.a Retinanet).
# See Lin et al, https://arxiv.org/abs/1708.02002
# Trained on COCO, initialized from Imagenet classification checkpoint
# Train on TPU-8
#
# Achieves 34.3 mAP on COCO17 Val

model {
  ssd {
    inplace_batchnorm_update: true
    freeze_batchnorm: true
    num_classes: 3
    box_coder {
      faster_rcnn_box_coder {
        y_scale: 10.0
        x_scale: 10.0
        height_scale: 5.0
        width_scale: 5.0
      }
    }
    matcher {
      argmax_matcher {
        matched_threshold: 0.5
        unmatched_threshold: 0.5
        ignore_thresholds: false
        negatives_lower_than_unmatched: true
        force_match_for_each_row: true
        use_matmul_gather: true
      }
    }
    similarity_calculator {
      iou_similarity {
      }
    }
    encode_background_as_zeros: true
    anchor_generator {
      multiscale_anchor_generator {
        min_level: 3
        max_level: 7
        anchor_scale: 4.0
        aspect_ratios: [1.0, 2.0, 0.5]
        scales_per_octave: 2
      }
    }
    image_resizer {
      fixed_shape_resizer {
        height: 640
        width: 640
      }
    }
    box_predictor {
      weight_shared_convolutional_box_predictor {
        depth: 256
        class_prediction_bias_init: -4.6
        conv_hyperparams {
          activation: RELU_6,
          regularizer {
            l2_regularizer {
              weight: 0.0004
            }
          }
          initializer {
            random_normal_initializer {
              stddev: 0.01
              mean: 0.0
            }
          }
          batch_norm {
            scale: true,
            decay: 0.997,
            epsilon: 0.001,
          }
        }
        num_layers_before_predictor: 4
        kernel_size: 3
      }
    }
    feature_extractor {
      type: 'ssd_resnet50_v1_fpn_keras'
      fpn {
        min_level: 3
        max_level: 7
      }
      min_depth: 16
      depth_multiplier: 1.0
      conv_hyperparams {
        activation: RELU_6,
        regularizer {
          l2_regularizer {
            weight: 0.0004
          }
        }
        initializer {
          truncated_normal_initializer {
            stddev: 0.03
            mean: 0.0
          }
        }
        batch_norm {
          scale: true,
          decay: 0.997,
          epsilon: 0.001,
        }
      }
      override_base_feature_extractor_hyperparams: true
    }
    loss {
      classification_loss {
        weighted_sigmoid_focal {
          alpha: 0.25
          gamma: 2.0
        }
      }
      localization_loss {
        weighted_smooth_l1 {
        }
      }
      classification_weight: 1.0
      localization_weight: 1.0
    }
    normalize_loss_by_num_matches: true
    normalize_loc_loss_by_codesize: true
    post_processing {
      batch_non_max_suppression {
        score_threshold: 1e-8
        iou_threshold: 0.6
        max_detections_per_class: 100
        max_total_detections: 100
      }
      score_converter: SIGMOID
    }
  }
}

train_config: {
  fine_tune_checkpoint_version: V2
  fine_tune_checkpoint: "./test_data/ssd_resnet50/checkpoint/ckpt-0"
  fine_tune_checkpoint_type: "detection"
  batch_size: 10
  sync_replicas: true
  startup_delay_steps: 0
  replicas_to_aggregate: 5
  use_bfloat16: true
  num_steps: 100000
  data_augmentation_options {
    random_horizontal_flip {
    }
  }
  data_augmentation_options {
    random_crop_image {
      min_object_covered: 0.0
      min_aspect_ratio: 0.4
      max_aspect_ratio: 3.0
      min_area: 0.5
      max_area: 1.0
      overlap_thresh: 0.0
    }
  }
  optimizer {
    momentum_optimizer: {
      learning_rate: {
        cosine_decay_learning_rate {
          learning_rate_base: .0005
          total_steps: 100000
          warmup_learning_rate: .0001
          warmup_steps: 1000
        }
      }
      momentum_optimizer_value: 0.5
    }
    use_moving_average: false
  }
  max_number_of_boxes: 100
  unpad_groundtruth_tensors: false
}

train_input_reader: {
  label_map_path: "./test_images/label_map.pbtxt"
  tf_record_input_reader {
    input_path: "./test_images/train.record-00000-of-00001"
  }
}

eval_config: {
  metrics_set: "coco_detection_metrics"
  use_moving_averages: false
}

eval_input_reader: {
  label_map_path: "./test_images/label_map.pbtxt"
  shuffle: false
  num_epochs: 1
  tf_record_input_reader {
    input_path:  "./test_images/val.record-00000-of-00001"
  }
}

Still having the same issue on faster r-cnn resnet 101

Still having the same issue on mobilnet as well