Unable to build for IOS for weeks

I have been trying and trying without success for weeks...
Cannot build for ios. Android ok.
here are the errors:
Xcode build done.                                           125.1s
Failed to build iOS app
Error output from Xcode build:
↳
** BUILD FAILED **
Xcode's output:
↳
/ios/Pods/Bolts/Bolts/iOS/BFAppLinkReturnToRefererController.m:133:17: warning: block implicitly retains 'self'; explicitly mention 'self' to indicate this is intended behavior [-Wimplicit-retain-self]
_view.frame = CGRectMake(0.0, 0.0, CGRectGetWidth(_view.bounds), 0.0);
^
self->
/ios/Pods/Bolts/Bolts/iOS/BFAppLinkReturnToRefererController.m:133:67: warning: block implicitly retains 'self'; explicitly mention 'self' to indicate this is intended behavior [-Wimplicit-retain-self]
_view.frame = CGRectMake(0.0, 0.0, CGRectGetWidth(_view.bounds), 0.0);
^
self->
/ios/Pods/Bolts/Bolts/iOS/BFAppLinkReturnToRefererController.m:148:18: warning: block implicitly retains 'self'; explicitly mention 'self' to indicate this is intended behavior [-Wimplicit-retain-self]
[_view sizeToFit];
^
self->
ios/Pods/Bolts/Bolts/iOS/BFAppLinkReturnToRefererController.m:189:13: warning: block implicitly retains 'self'; explicitly mention 'self' to indicate this is intended behavior [-Wimplicit-retain-self]
/ios/Pods/Protobuf/objectivec/google/protobuf/Struct.pbobjc.m:19:10: warning: non-portable path to file '<protobuf/Struct.pbobjc.h>'; specified path differs in case from file name on disk [-Wnonportable-include-path]
#import <Protobuf/Struct.pbobjc.h>
^~~~~~~~~~~~~~~~~~~~~~~~~~
<protobuf/Struct.pbobjc.h>
1 warning generated.
ios/Pods/Protobuf/objectivec/google/protobuf/Timestamp.pbobjc.m:17:10: warning: non-portable path to file '<protobuf/Timestamp.pbobjc.h>'; specified path differs in case from file name on disk [-Wnonportable-include-path]
#import <Protobuf/Timestamp.pbobjc.h>
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<protobuf/Timestamp.pbobjc.h>
1 warning generated.
/ios/Pods/Protobuf/objectivec/google/protobuf/Type.pbobjc.m:19:10: warning: non-portable path to file '<protobuf/Type.pbobjc.h>'; specified path differs in case from file name on disk [-Wnonportable-include-path]
#import <Protobuf/Type.pbobjc.h>
This is just s snapshot. It is a very long error list.
Any idea what went wrong?

Hi there, got a couple questions for you:

what does your flutter doctor says?
you're showing some clang warnings, can you show us the error(s) that make the failure?
is this error caused by flutter_blue?

There is a known pod issue, mentioned in a few issues.  Look at https://github.com/pauldemarco/flutter_blue/issues/108 too see if this is the case for you.
My issue at least was in flutter_blue.podspec, where I had to change protoc = ENV['PWD'] + '/Pods/!ProtoCompiler/protoc'  to  protoc = "#(PODS_ROOT)/!ProtoCompiler/protoc"
NOTE the parantheses around PODS_ROOT. Try either {} or (). I had to use parantheses.

If this is the cause of this issue, then another temporary fix if you don't want to modify the podspec is to ln -s ios/Pods into your project root folder. This will create a symlink so your Pods can be found.

I read about all the above before and I tried everything. Finally, I did a lot of other stuffs that I read online and I believe it is a combination of

Pod deintegrate
Pod upadte
added config.build_settings['SWIFT_VERSION'] = '4.3' to podfile
Copy a missing runner.entitlement from another project

And finally, it build and ran. Thanks


bug

VSCode Version: Code 1.15.1 (41abd21afdf7424c89319ee7cb0445cc6f376959, 2017-08-16T17:16:27.618Z)
OS Version: Linux x64 4.2.0-42-generic
Extensions: none
(1 theme extensions excluded)

when  ctrl + f with a $ , why show as $

Reproduces without extensions: Yes/No

$ ==> '''$

\$

Do you have the Use Regular Expression option on?
$ is a keyword in regexp, so Code will escape it automatically for you.
Besides, you can edit your post.

I'm sorry I don't understand what's the issue here.

sorry, i mean the find field show as \$ but this does not  affect the search anyway


WHEREIN clause has no effects on command SCAN

Hi, @tidwall , WHEREIN is a so useful clause for me, so I try it right away. And I found that it works correctly on command NEAYBY, but it has no effects on SCAN .

hi @yorkxiao,
I'm not seeing a problem with SCAN and WHEREIN from my side.
Could you provide an example of the issue?

hi, @tidwall , thanks for you reply.
I'm sorry, I should provide an the exact case.
This problem will happen when SCAN command excute with WHEREIN and COUNT options. Here is a sample:
<IP_ADDRESS>:9851> SCAN lwlk.vehicle.loc.now wherein hangyeid 1 11 count
(integer) 313685
<IP_ADDRESS>:9851> nearby lwlk.vehicle.loc.now wherein hangyeid 1 11 count point 11<PHONE_NUMBER>9999999
(integer) 19668
<IP_ADDRESS>:9851> SCAN lwlk.vehicle.loc.now wherein hangyeid 1 12 count
(integer) 313689
<IP_ADDRESS>:9851> nearby lwlk.vehicle.loc.now wherein hangyeid 1 12 count point 11<PHONE_NUMBER>9999999
(integer) 5867

The problem maybe is a bug with the issue #203, I think.

Yes this looks like a a bug.

I just pushed an update to the master branch that fixes this issue. Let me know if you see further problems around this issue.  Thanks!


Using ffmpeg-cli-wrapper for streaming I need the -f switch to be applied to the output

The question
I would like to use the ffmpeg-cli-wrapper for simple streaming of a video. I am a first-time user of ffmpeg, and I came up with the below code.
From the error and from my examination (details below) it appears the -f {format} is applied to the input, but I need it applied to the output.
What am I doing wrong? How do I get a simple stream to work?
Example ffmpeg command
ffmpeg -i big_buck_bunny_720p_1mb.mp4 -f flv "rtmp://myserver.box/stream"

What you have tried
Using FFmpegExecutorTest as a roadmap, I came up with the following:
FFmpeg ffmpeg = new FFmpeg();
FFprobe ffprobe = new FFprobe();
FFmpegExecutor ffExecutor = new FFmpegExecutor(ffmpeg, ffprobe);
ExecutorService executor = Executors.newSingleThreadExecutor();

ClassLoader classLoader = getClass().getClassLoader();
String input = classLoader.getResource("data/big_buck_bunny_720p_1mb.mp4").getFile();
URI output = new URI("rtmp://myserver.box/stream");

FFmpegBuilder builder = new FFmpegBuilder();
builder.setInput(input);
builder.setFormat("flv");
builder.addOutput(output);
builder.build();
FFmpegJob job = ffExecutor.createJob(builder);
executor.submit(job).get();

When I run this, I get the following error:
14:24:53.692-0700 [pool-1-thread-1] [INFO] RunProcessFunction - ffmpeg -y -v error -f flv -i {redacted}/target/test-classes/data/big_buck_bunny_720p_1mb.mp4 rtmp://myserver.box/stream
{redacted}/target/test-classes/big_buck_bunny_720p_1mb.mp4: could not find codec parameters

However, if on the command line if I switch the -f and -i options around, so that the order is: -i <project>/target/test-classes/data/big_buck_bunny_720p_1mb.mp4 -f flv rtmp://myserver.box/stream then everything works as expected.

From documentation:

As a general rule, options are applied to the next specified file. Therefore, order is important, and you can have the same option on the command line multiple times. Each occurrence is then applied to the next input or output file.

Check out this streaming example of video.
https://github.com/bramp/ffmpeg-cli-wrapper/blob/master/src/test/java/net/bramp/ffmpeg/ExamplesTest.java#L40

Thanx for the help.
In order to get my usecase to work, I found the following:

The ordering is not intuitive. If I want the format to be applied to the output, the order must be like this:

FFmpegBuilder builder = new FFmpegBuilder(). //
        setFormat(inputFormat). // input
        setInput(input). //
        addOutput(output). //
        setFormat("flv"). // must come AFTER the output
        done();

Something like this will not work correctly, even though this might look like the order you would specify for ffmpeg on the CLI.
FFmpegBuilder builder = new FFmpegBuilder(). //
        setFormat(inputFormat). // input
        setInput(input). //
        setFormat("flv"). // output
        addOutput(output). //
        done();

Code form - not sure what to call this. The following will always give the wrong result!

FFmpegBuilder builder = new FFmpegBuilder();
builder.setFormat(inputFormat);
builder.setInput(input);
builder.addOutput(output);
builder.setFormat("flv");
builder.build();


remove loop as argument and update examples

This entirely removes the loop as an argument, from tests, examples, and sanic core. Since it's no longer going to be supported by asyncio (https://github.com/channelcat/sanic/issues/152#issuecomment-261316341) it doesn't make sense to go against the grain. All of the example modifications except the peewee one have been tested to work.
We'll probably want to issue a deprecation message for a few weeks before merging this.

I'm a fan but let's leave this up for at least a week so that people can voice their opinions about it as well. If we don't find a compelling reason not to merge this change in by the end of the week this will be rolled into 0.3.0.

Tagging some of the open issues that are related:
https://github.com/channelcat/sanic/issues/305
https://github.com/channelcat/sanic/issues/152

Please see #338 also.

It's definitely cleaner and less confusing to explicitly specify the loop everywhere. So :+1:
It would be good to have another similar example showing how to limit the number of concurrent coroutines running using Semaphore or however is best to do so.

@youknowone that should be addressed by https://github.com/channelcat/sanic/issues/275. I have a half finished PR for that.

@ubergarm your implementation looks like the best idea we have for that, as of now. I'll add it as an example.

While in general this seems to be a very good idea, it breaks applications where libraries need to use the same loop Sanic is running in. Let's assume I'm using an async database client which gets initialized before running the app, how is it supposed to get hold of the loop which is only created later? Do we need to use before_start for that? If so, this should at least be documented somehow.

@Tim-Erwin thanks for your review and feedback. Regarding:

it breaks applications where libraries need to use the same loop Sanic is running in

One possibility in that scenario is using this new feature:
https://github.com/channelcat/sanic/pull/348

Alright without a compelling argument against we will roll this into 0.3.0 scheduled for release tomorrow!

Wonderful. However, as mentioned in the before, it doesn't seem to be a good idea to create new connection pools in each request. Hence, we should not suggest to do that in our examples. While examples are just that - examples - they should give a good starting point. I think many will struggle with this part.

@Tim-Erwin I'm curious to see some performance statistics to back the claim that this is poor practice. While I agree, if you can avoid opening a connection for each query, that is better. However, It seems that passing the loop is not really supported by Python, so we need to move away from that. It's less of a performance concern than a sheer "this doesn't work" situation. You're welcome to create a PR or provide a suggestion to improve the examples. They're not going to be set in stone after this.

@r0fls The reason to use a connection pool is to avoid creating new connections over and over again, right? So even without any difference in performance creating a pool for just one request contradicts its purpose. I understand that passing the loop is no longer supported and that's a good thing, because IMHO the loop should be invisible as long as you don't intend to tinker with it.
In my current project with Sanic I'm explicitly creating the loop and passing it in because my DB client (which I create before running the app) has to use the same loop. So I'm really not an expert in how to do it differently. If I understand correctly we'd have to pass in a function before_start that's then provided the loop which we can use to init other libraries?

You have to remember that every context (process, thread) etc. has its own loop. You cannot share a loop over thread or process borders. So you need create a connection pool for every worker thread or process you have. From this point of view, the approach from @r0fls is correct.
But now the before_start function kicks in. Here you can create a connection per worker since it is called in the context of the worker (process). It shouldn't event need the loop argument as you can use get_event_loop.
But you are right, for a single request a connection pool is overkill since only one connection can be used.


Does anyone support the project?

Hello @exzos28
Let me answer this one.
The project is maintainable, but in slow mode. The reason behind it that in our company (Akveo) we do not have dedicated person who works on this project fulltime. So sometimes there might be large pauses between activities.
We are trying to figure out the better way to provide a support, but at the moment it will stay as is.
We are sorry about that.


npm install fails for latest 0.5.0 release

I tried switching my local forked install back to the upstream 0.5.0 version you just released (thanks btw!), and now I'm getting this error:
➜  npm i<EMAIL_ADDRESS>npm error code 1
npm error path <project>/node_modules/tree-sitter-swift
npm error command failed
npm error command sh -c node-gyp-build
npm error TOUCH Release/obj.target/node_modules/node-addon-api/node_addon_api_except.stamp
npm error   ACTION binding_gyp_tree_sitter_swift_binding_target_wait_for_tree_sitter node_modules/tree-sitter-cli
npm error gyp info it worked if it ends with ok
npm error gyp info using<EMAIL_ADDRESS>npm error gyp info using<EMAIL_ADDRESS>| darwin | arm64
npm error gyp info find Python using Python version 3.12.2 found at<EMAIL_ADDRESS>npm error gyp info spawn<EMAIL_ADDRESS>npm error gyp info spawn args [
npm error gyp info spawn args '/Users/telkins/.nvm/versions/node/v20.12.0/lib/node_modules/npm/node_modules/node-gyp/gyp/gyp_main.py',
npm error gyp info spawn args 'binding.gyp',
npm error gyp info spawn args '-f',
npm error gyp info spawn args 'make',
npm error gyp info spawn args '-I',
npm error gyp info spawn args '<project>/node_modules/tree-sitter-swift/build/config.gypi',
npm error gyp info spawn args '-I',
npm error gyp info spawn args '/Users/telkins/.nvm/versions/node/v20.12.0/lib/node_modules/npm/node_modules/node-gyp/addon.gypi',
npm error gyp info spawn args '-I',
npm error gyp info spawn args '/Users/telkins/Library/Caches/node-gyp/20.12.0/include/node/common.gypi',
npm error gyp info spawn args '-Dlibrary=shared_library',
npm error gyp info spawn args '-Dvisibility=default',
npm error gyp info spawn args '-Dnode_root_dir=/Users/telkins/Library/Caches/node-gyp/20.12.0',
npm error gyp info spawn args '-Dnode_gyp_dir=/Users/telkins/.nvm/versions/node/v20.12.0/lib/node_modules/npm/node_modules/node-gyp',
npm error gyp info spawn args '-Dnode_lib_file=/Users/telkins/Library/Caches/node-gyp/20.12.0/<(target_arch)/node.lib',
npm error gyp info spawn args '-Dmodule_root_dir=<project>/node_modules/tree-sitter-swift',
npm error gyp info spawn args '-Dnode_engine=v8',
npm error gyp info spawn args '--depth=.',
npm error gyp info spawn args '--no-parallel',
npm error gyp info spawn args '--generator-output',
npm error gyp info spawn args 'build',
npm error gyp info spawn args '-Goutput_dir=.'
npm error gyp info spawn args ]
npm error gyp info spawn make
npm error gyp info spawn args [ 'BUILDTYPE=Release', '-C', 'build' ]
npm error make: *** No rule to make target `../node_modules/tree-sitter-cli', needed by `aab630bda7ca2536d7051bf3c2d7e450d17166c1.intermediate'.  Stop.
npm error gyp ERR! build error
npm error gyp ERR! stack Error: `make` failed with exit code: 2
npm error gyp ERR! stack at ChildProcess.<anonymous> (/Users/telkins/.nvm/versions/node/v20.12.0/lib/node_modules/npm/node_modules/node-gyp/lib/build.js:209:23)
npm error gyp ERR! System Darwin 23.2.0
npm error gyp ERR! command "/Users/telkins/.nvm/versions/node/v20.12.0/bin/node" "/Users/telkins/.nvm/versions/node/v20.12.0/lib/node_modules/npm/node_modules/node-gyp/bin/node-gyp.js" "rebuild"
npm error gyp ERR! cwd <project>/node_modules/tree-sitter-swift
npm error gyp ERR! node -v v20.12.0
npm error gyp ERR! node-gyp -v v10.1.0
npm error gyp ERR! not ok

npm error A complete log of this run can be found in: /Users/telkins/.npm/_logs/2024-05-06T19_55_53_273Z-debug-0.log

Oddly this doesn't happen when I install the package locally with something like "file:../ which I see is what the test-npm-package project is doing. Running both the install and postinstall NPM scripts work for me too.

I narrowed it down to the custom actions being done in binding.gyp:
      "actions": [
          {
	      "action_name": "wait_for_tree_sitter",
	      "action": ["node", "scripts/wait-for-tree-sitter.js"],
	      "inputs": [],
	      "outputs": ["node_modules/tree-sitter-cli"]
	  },
          {
	      "action_name": "generate_header_files",
	      "inputs": [
	          "grammar.js",
		  "node_modules/tree-sitter-cli"
	      ],
	      "outputs": [
	          "src/grammar.json",
		  "src/node-types.json",
		  "src/parser.c",
		  "src/tree_sitter",
	      ],
	      "action": ["tree-sitter", "generate", "--no-bindings"],
	  }
      ]

I see the node-gyp build step produces a Makefile that tries to call tree-sitter-cli and fails, which is what the error message in the OP is referring to. I tried a few things but couldn't figure out a way to fix this using the custom GYP setup going on in this repo, as a workaround I'm using a fork in https://github.com/trevor-e/tree-sitter-swift/pull/3 which reverts gitignoring some of the generated files. I see in the README that you won't accept PRs that check in the generated files so this seems unlikely to get merged.

Oh neat I see there's actually a with-generated-files branch, I think once a 0.5.0 release is published on Github I can switch over to that for now.

This is very strange. I added that action because without it, npm install was flaky; the core issue is that tree-sitter-cli downloads its binary asynchronously at install time. But it seems like something doesn't work quite right...
I wonder if I should just switch NPM publishing over to the with-generated-files branch.

Yea I think switching to the 'with-generated-files' branch, at least for publishing, is probably the easiest. If you release 0.5.0 on GitHub I'm happy to test that out before publishing it to npm.

ok, just tagged the 0.5.0 release and updated the generated files branch. Let me know how your testing goes. I added a binding.gyp to that branch that doesn't try to generate files (since it doesn't need to :stuck_out_tongue:). Let me know how your testing goes and if there are any other files that need to be added.

Sorry for the delay! I tried the with-generated-files branch and I'm getting a new error:
npm error ../bindings/node/binding.cc:3:10: fatal error: 'nan.h' file not found
npm error #include "nan.h"

The binding.cc file looks stale since it's still referencing nan.h and not napi.h. I checked out the branch locally, ran tree-sitter generate, and see there are a bunch of changes that weren't committed. I think the write-generated-grammar.sh script might be out of date and needs to add some extra files like you said.

I still hit the same issue (from the original post). Tried with 0.5.0 and 0.6.0. Did anyone manage to solve the issue?

@dzenam you can try my fork branch here: github:trevor-e/tree-sitter-swift#telkins/verbose
I've been using that for the meantime, although it's fairly out of date by now.

Yup, that worked.
Appreciate the help @trevor-e!

I've solved this by first running npm install node-gyp.


Learn more about access permissions in Linux environments

What?
I would like to better understand  access permissions in Linux environments.

Caleb said it helps to write them all out...
User-group-world
chmod 754
Rwx-xr-r

Rxw-read write execute
XR- execute read
R - read

User can read write and execute
Group can read and execute
World can read


Automatic Single-User Roles

Potential Idea:
Add an automatic role for each user added that only appears in the Requests Table.  These roles will only include that one user and will show up with the column name "username (only)"
This would be helpful for testing cross-user authorization so that you wont need to make a specific role titled "only employee 1" to test an asset owned by employee 1

Added in v0.7


closeOnSelect=false does not work if onItemSelect triggers a $digest cycle

I have set closeOnSelect and closeOnDeselect to false. This was working Ok and the multi selector keeps opened as expected until I click on the drop down button or anywhere outside the selector.
The problem comes when I set  callbacks for onItemSelect and onItemDeselect. The action implemented in this functions  triggers an angular $diggest cycle and this in turn forces the multi selector to close.

duplicate #101


Update wrangler version when updating k8s 1.20

See: https://github.com/rancher/rancher/pull/29344

Moving out to 2.5.3 as discussed with @deniseschannon

@mrajashree Is this update needed for backup/restore feature?

@maggieliu the bug fix from wrangler isn't needed in backup-restore-operator because the operator doesn't use that function from wrangler. But we can still update wrangler version to the latest one in 2.5.4

Since backup-restore doesn't use this, this can be closed.

Since backup-restore doesn't use this, this can be closed.


Feedback from H0rb#2507

From H0rb :
suggestion I think we should have some kind of music-related channel. It would be nice.

From kedi hacker :
yes but you can open music yourself and it would sound better

This is what the offtopic channel is for


how to merge lora finetuned model with base model

I finetune llava-one-vision using lmms-lab/llava-onevision-qwen2-7b-ov by config --lora_enable True --lora_r 128 --lora_alpha 256 --mm_projector_lr 2e-5  and have checkpoint saved, how can i using this model for eval?

I tried merging using the following code, but I got garbled results, and I’m not sure if it’s an issue with my training or the merging process. If you’d like, we can discuss it further.
   tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name, overwrite_config=overwrite_config)

I've encountered the same issue. Full-parameter fine-tuning works perfectly, but LoRA fine-tuning produces garbled results. I'm attempting to migrate my project from the zjysteven/lmms-finetune repository, where LoRA fine-tuning works correctly.

bro, can you take a look at the config configuration that Lora has fine tuned? Why does it show that the weight shapes do not match when I merge Lora weights.
This is an error in my output：
You are using a model of type siglip to instantiate a model of type clip_vision_model. This is not supported for all configurations of models and can yield errors.
Traceback (most recent call last):
File "/4T/WK/MyProjects/LLaVA-NeXT/scripts/archived/merge_lora_weights.py", line 22, in
merge_lora(args)
File "/4T/WK/MyProjects/LLaVA-NeXT/scripts/archived/merge_lora_weights.py", line 8, in merge_lora
tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, device_map="cpu")
File "/4T/WK/MyProjects/LLaVA-NeXT/llava/model/builder.py", line 87, in load_pretrained_model
model = LlavaQwenForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True,config=lora_cfg_pretrained,attn_implementation=attn_implementation, **kwargs)
File "/home/wk/anaconda3/envs/llava-next/lib/python3.10/site-packages/transformers/modeling_utils.py", line 3404, in from_pretrained
model = cls(config, *model_args, **model_kwargs)
File "/4T/WK/MyProjects/LLaVA-NeXT/llava/model/language_model/llava_qwen.py", line 55, in init
self.model = LlavaQwenModel(config)
File "/4T/WK/MyProjects/LLaVA-NeXT/llava/model/language_model/llava_qwen.py", line 43, in init
super(LlavaQwenModel, self).init(config)
File "/4T/WK/MyProjects/LLaVA-NeXT/llava/model/llava_arch.py", line 41, in init
self.vision_tower = build_vision_tower(config, delay_load=delay_load)
File "/4T/WK/MyProjects/LLaVA-NeXT/llava/model/multimodal_encoder/builder.py", line 23, in build_vision_tower
return CLIPVisionTower(vision_tower, args=vision_tower_cfg, **kwargs)
File "/4T/WK/MyProjects/LLaVA-NeXT/llava/model/multimodal_encoder/clip_encoder.py", line 24, in init
self.load_model()
File "/4T/WK/MyProjects/LLaVA-NeXT/llava/model/multimodal_encoder/clip_encoder.py", line 41, in load_model
self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name, device_map=device_map)
File "/home/wk/anaconda3/envs/llava-next/lib/python3.10/site-packages/transformers/modeling_utils.py", line 3531, in from_pretrained
) = cls._load_pretrained_model(
File "/home/wk/anaconda3/envs/llava-next/lib/python3.10/site-packages/transformers/modeling_utils.py", line 4009, in _load_pretrained_model
raise RuntimeError(f"Error(s) in loading state_dict for {model.class.name}:\n\t{error_msg}")
RuntimeError: Error(s) in loading state_dict for CLIPVisionModel:
size mismatch for vision_model.embeddings.patch_embedding.weight: copying a param with shape torch.Size([1152, 3, 14, 14]) from checkpoint, the shape in current model is torch.Size([768, 3, 32, 32]).
size mismatch for vision_model.embeddings.position_embedding.weight: copying a param with shape torch.Size([729, 1152]) from checkpoint, the shape in current model is torch.Size([50, 768]).
size mismatch for vision_model.encoder.layers.0.self_attn.k_proj.weight: copying a param with shape torch.Size([1152, 1152]) from checkpoint, the shape in current model is torch.Size([768, 768]).
size mismatch for vision_model.encoder.layers.0.self_attn.k_proj.bias: copying a param with shape torch.Size([1152]) from checkpoint, the shape in current model is torch.Size([768]).
size mismatch for vision_model.encoder.layers.0.self_attn.v_proj.weight: copying a param with shape torch.Size([1152, 1152]) from checkpoint, the shape in current model is torch.Size([768, 768]).
size mismatch for vision_model.encoder.layers.0.self_attn.v_proj.bias: copying a param with shape torch.Size([1152]) from checkpoint, the shape in current model is torch.Size([768]).
size mismatch for vision_model.encoder.layers.0.self_attn.q_proj.weight: copying a param with shape torch.Size([1152, 1152]) from checkpoint, the shape in current model is torch.Size([768, 768]).
size mismatch for vision_model.encoder.layers.0.self_attn.q_proj.bias: copying a param with shape torch.Size([1152]) from checkpoint, the shape in current model is torch.Size([768]).
size mismatch for vision_model.encoder.layers.0.self_attn.out_proj.weight: copying a param with shape torch.Size([1152, 1152]) from checkpoint, the shape in current model is torch.Size([768, 768]).
size mismatch for vision_model.encoder.layers.0.self_attn.out_proj.bias: copying a param with shape torch.Size([1152]) from checkpoint, the shape in current model is torch.Size([768]).
size mismatch for vision_model.encoder.layers.0.layer_norm1.weight: copying a param with shape torch.Size([1152]) from checkpoint, the shape in current model is torch.Size([768]).
size mismatch for vision_model.encoder.layers.0.layer_norm1.bias: copying a param with shape torch.Size([1152]) from checkpoint, the shape in current model is torch.Size([768]).
size mismatch for vision_model.encoder.layers.0.mlp.fc1.weight: copying a param with shape torch.Size([4304, 1152]) from checkpoint, the shape in current model is torch.Size([3072, 768]).
size mismatch for vision_model.encoder.layers.0.mlp.fc1.bias: copying a param with shape torch.Size([4304]) from checkpoint, the shape in current model is torch.Size([3072]).
size mismatch for vision_model.encoder.layers.0.mlp.fc2.weight: copying a param with shape torch.Size([1152, 4304]) from checkpoint, the shape in current model is torch.Size([768, 3072]).
size mismatch for vision_model.encoder.layers.0.mlp.fc2.bias: copying a param with shape torch.Size([1152]) from checkpoint, the shape in current model is torch.Size([768]).

It seems that you have loaded the wrong vision tower ckpt. Can you provide the config.json and your input parameter for merge_lora_weights.py

Here is the configuration for my models - lmms lab - llava review qwen2-0.5b-ov:
{
"_name_or_path": "/4T/WK/MyProjects/LLaVA-NeXT/llava-model/models--lmms-lab--llava-onevision-qwen2-0.5b-ov",
"architectures": [
"LlavaQwenForCausalLM"
],
"attention_dropout": 0.0,
"mm_newline_position":"one_token",
"bos_token_id": 151643,
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 896,
"image_aspect_ratio": "anyres_max_9",
"image_crop_resolution": null,
"image_grid_pinpoints": [
[
384,
384
],
[
384,
768
],
[
384,
1152
],
[
384,
1536
],
[
384,
1920
],
[
384,
2304
],
[
768,
384
],
[
768,
768
],
[
768,
1152
],
[
768,
1536
],
[
768,
1920
],
[
768,
2304
],
[
1152,
384
],
[
1152,
768
],
[
1152,
1152
],
[
1152,
1536
],
[
1152,
1920
],
[
1152,
2304
],
[
1536,
384
],
[
1536,
768
],
[
1536,
1152
],
[
1536,
1536
],
[
1536,
1920
],
[
1536,
2304
],
[
1920,
384
],
[
1920,
768
],
[
1920,
1152
],
[
1920,
1536
],
[
1920,
1920
],
[
1920,
2304
],
[
2304,
384
],
[
2304,
768
],
[
2304,
1152
],
[
2304,
1536
],
[
2304,
1920
],
[
2304,
2304
]
],
"image_split_resolution": null,
"image_token_index": 151646,
"initializer_range": 0.02,
"intermediate_size": 4864,
"max_position_embeddings": 32768,
"max_window_layers": 24,
"mm_hidden_size": 1152,
"mm_patch_merge_type": "spatial_unpad",
"mm_projector_lr": null,
"mm_projector_type": "mlp2x_gelu",
"mm_resampler_type": null,
"mm_spatial_pool_mode": "bilinear",
"mm_tunable_parts": "mm_vision_tower,mm_mlp_adapter,mm_language_model",
"mm_use_im_patch_token": false,
"mm_use_im_start_end": false,
"mm_vision_select_feature": "patch",
"mm_vision_select_layer": -2,
"mm_vision_tower": "google/siglip-so400m-patch14-384",
"mm_vision_tower_lr": 2e-06,
"model_type": "qwen2",
"num_attention_heads": 14,
"num_hidden_layers": 24,
"num_key_value_heads": 2,
"pos_skipping_range": 4096,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000.0,
"sliding_window": 32768,
"tie_word_embeddings": true,
"tokenizer_model_max_length": 32768,
"tokenizer_padding_side": "right",
"torch_dtype": "bfloat16",
"transformers_version": "4.40.0.dev0",
"use_cache": true,
"use_mm_proj": true,
"use_pos_skipping": false,
"use_sliding_window": false,
"vision_tower_pretrained": null,
"vocab_size": 151936
}
According to the official issue mentioned, I have changed the "model_type" from "llava" to "qwen2" to facilitate fine-tuning of the model.
This is the parameter input for my merger lora_weights. py
python /4T/WK/MyProjects/LLaVA-NeXT/scripts/archived/merge_lora_weights.py
--model-path /4T/WK/MyProjects/LLaVA-NeXT/checkpoints/lora_model/llava-onevision-._llava-model_models--google--siglip-so400m-patch14-384-._llava-model_models--Qwen--Qwen2-0.5B-Instruct-ov_stage_video_detail_lora
--model-base /4T/WK/MyProjects/LLaVA-NeXT/llava-model/models--lmms-lab--llava-onevision-qwen2-0.5b-ov
--save-model-path /4T/WK/MyProjects/LLaVA-NeXT/checkpoints/lora_merge_model/test/
But I think the problem may have been with CLIPVisionTower instead of SigLipVisionTower when the model was loaded during fine-tuning from the beginning. Because there is a situation where the dimensions do not correspond when merging models.Do you know how this was modified?

This is the config that my Lora has fine tuned
{
"_name_or_path": "./llava-model/models--lmms-lab--llava-onevision-qwen2-0.5b-ov",
"add_faster_video": false,
"add_time_instruction": false,
"architectures": [
"LlavaQwenForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151645,
"faster_token_stride": 10,
"force_sample": false,
"hidden_act": "silu",
"hidden_size": 896,
"image_aspect_ratio": "anyres_max_9",
"image_crop_resolution": null,
"image_grid_pinpoints": [
[
384,
384
],
[
384,
768
],
[
384,
1152
],
[
384,
1536
],
[
384,
1920
],
[
384,
2304
],
[
768,
384
],
[
768,
768
],
[
768,
1152
],
[
768,
1536
],
[
768,
1920
],
[
768,
2304
],
[
1152,
384
],
[
1152,
768
],
[
1152,
1152
],
[
1152,
1536
],
[
1152,
1920
],
[
1152,
2304
],
[
1536,
384
],
[
1536,
768
],
[
1536,
1152
],
[
1536,
1536
],
[
1536,
1920
],
[
1536,
2304
],
[
1920,
384
],
[
1920,
768
],
[
1920,
1152
],
[
1920,
1536
],
[
1920,
1920
],
[
1920,
2304
],
[
2304,
384
],
[
2304,
768
],
[
2304,
1152
],
[
2304,
1536
],
[
2304,
1920
],
[
2304,
2304
]
],
"image_split_resolution": null,
"image_token_index": 151646,
"initializer_range": 0.02,
"intermediate_size": 4864,
"max_position_embeddings": 32768,
"max_window_layers": 24,
"mm_hidden_size": 1152,
"mm_newline_position": "grid",
"mm_patch_merge_type": "spatial_unpad",
"mm_projector_lr": 5e-05,
"mm_projector_type": "mlp2x_gelu",
"mm_resampler_type": null,
"mm_spatial_pool_mode": "bilinear",
"mm_spatial_pool_stride": null,
"mm_tunable_parts": "mm_vision_tower,mm_mlp_adapter,mm_language_model",
"mm_use_im_patch_token": false,
"mm_use_im_start_end": false,
"mm_vision_select_feature": "patch",
"mm_vision_select_layer": -2,
"mm_vision_tower": "./llava-model/models--google--siglip-so400m-patch14-384",
"mm_vision_tower_lr": 2e-06,
"model_type": "qwen2",
"num_attention_heads": 14,
"num_hidden_layers": 24,
"num_key_value_heads": 2,
"pos_skipping_range": 4096,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000.0,
"sliding_window": 32768,
"tie_word_embeddings": true,
"tokenizer_model_max_length": 32768,
"tokenizer_padding_side": "right",
"torch_dtype": "bfloat16",
"transformers_version": "4.40.0.dev0",
"use_cache": true,
"use_mm_proj": true,
"use_pos_skipping": false,
"use_sliding_window": false,
"vision_tower_pretrained": null,
"vocab_size": 151936
}

You may take a look of the build_vision_tower function , line#23 should be executed based on your config: return SigLipVisionTower(vision_tower, vision_tower_cfg=vision_tower_cfg, **kwargs). Clearly, there is something wrong with the vision tower building process.
In addition, have you checked the trainable params when do lora fine tune? I found that if your print out all trainable params at this line llava/train/train.py#L1695, just before training, it will show up many unwanted params that should be freezed. So, I would suggest you to re-order the train code a bit to avoid this error.

I've encountered the same issue. Full-parameter fine-tuning works perfectly, but LoRA fine-tuning produces garbled results. I'm attempting to migrate my project from the zjysteven/lmms-finetune repository, where LoRA fine-tuning works correctly.

I've encountered the same issue. Have you resolved it?

Quote reply

@zhudongwork Not yet. However, I believe this lora bug is correlated to at least two issues I found,

In addition, have you checked the trainable params when do lora fine tune? I found that if your print out all trainable params at this line llava/train/train.py#L1695, just before training, it will show up many unwanted params that should be freezed. So, I would suggest you to re-order the train code a bit to avoid this error.

@zhudongwork Not yet. I'm currently working on something more urgent. However, I believe this LoRA bug is related to at least two issues I've identified:

There are problems with the trainable parameters in the above reply.
The PEFT version should be updated to 0.12.0.
Please let me know if you discover anything further. I will continue to debug this issue as soon as I am available.

What was the GPU usage like during your LoRA fine-tuning? I'm fine-tuning a 7B model with multiple GPUs, and each card is using over 75+ GB of GPU. Moreover, the trained model weights are also quite large, which doesn't seem to match the resource consumption expected from a typical LoRA fine-tuning. Have you encountered a similar issue?

In addition, have you checked the trainable params when do lora fine tune? I found that if your print out all trainable params at this line llava/train/train.py#L1695, just before training, it will show up many unwanted params that should be freezed. So, I would suggest you to re-order the train code a bit to avoid this error.

@zhudongwork Not yet. I'm currently working on something more urgent. However, I believe this LoRA bug is related to at least two issues I've identified:

There are problems with the trainable parameters in the above reply.
The PEFT version should be updated to 0.12.0.
Please let me know if you discover anything further. I will continue to debug this issue as soon as I am available.

I use the peft==0.12.0

That's way more VRAM consumption than expected. The exact VRAM usage  of my experiment has lost. However, you can try this code snippet at llava/train/train.py#L1695, you will find out the param issue.
def count_trainable_parameters(model):
    lora_params = []
    non_lora_params = []

    for name, param in model.named_parameters():
        if param.requires_grad:
            if 'lora' in name.lower():
                lora_params.append((name, param))
            else:
                non_lora_params.append((name, param))

    lora_param_count = sum(param.numel() for _, param in lora_params)
    non_lora_param_count = sum(param.numel() for _, param in non_lora_params)

    print("Trainable parameters summary:")
    print(f"  LoRA parameters: {lora_param_count:,}")
    print(f"  Non-LoRA parameters: {non_lora_param_count:,}")
    print(f"  Total trainable parameters: {lora_param_count + non_lora_param_count:,}")

    print("\nDetails:")
    print("LoRA parameters:")
    for name, param in lora_params:
        print(f"  {name}: shape={param.shape}, numel={param.numel():,}")

    print("\nNon-LoRA parameters:")
    for name, param in non_lora_params:
        print(f"  {name}: shape={param.shape}, numel={param.numel():,}")

# Usage
count_trainable_parameters(model)

To solve this, you could just move following code block which init lora params
    if training_args.lora_enable:
        from peft import LoraConfig, get_peft_model

        lora_config = LoraConfig(
            r=training_args.lora_r,
            lora_alpha=training_args.lora_alpha,
            target_modules=find_all_linear_names(model),
            lora_dropout=training_args.lora_dropout,
            bias=training_args.lora_bias,
            task_type="CAUSAL_LM",
        )
        if training_args.bits == 16:
            if training_args.bf16:
                model.to(torch.bfloat16)
            if training_args.fp16:
                model.to(torch.float16)
        rank0_print("Adding LoRA adapters...")
        model = get_peft_model(model, lora_config)

    if training_args.gradient_checkpointing:
        if hasattr(model, "enable_input_require_grads"):
            model.enable_input_require_grads()
        else:

            def make_inputs_require_grad(module, input, output):
                output.requires_grad_(True)

            model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)

to just before data module init function data_module = make_supervised_data_module(tokenizer=tokenizer, data_args=data_args)
It will solve the trainable lora param issue.

🎉 I might have solved this problem.

I rewrote the merge method, you can use this method instead of load_pretrained_model in LLaVA-NeXT/llava/model/builder.py
In addition, it is best to include the words llava, qwen and lora in the weight name. For example, my name is llava_next_endo_qwen_lora. Because in most cases, the llava series will search for the corresponding model based on the name.

def lxr_load_llava_next_ov(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto", attn_implementation="flash_attention_2", customized_config=None, overwrite_config=None, **kwargs):
    kwargs["device_map"] = device_map
    kwargs["torch_dtype"] = torch.float16

    tokenizer = AutoTokenizer.from_pretrained(model_path)
    model = LlavaQwenForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, **kwargs)

    rank0_print(f"Model Class: {model.__class__.__name__}")
    image_processor = None

    if "llava" in model_name.lower():
        mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
        mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True)
        if mm_use_im_patch_token:
            tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
        if mm_use_im_start_end:
            tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
        model.resize_token_embeddings(len(tokenizer))

        vision_tower = model.get_vision_tower()
        if not vision_tower.is_loaded:
            vision_tower.load_model(device_map=device_map)
        if device_map != "auto":
            vision_tower.to(device="cuda", dtype=torch.float16)
        image_processor = vision_tower.image_processor

    if hasattr(model.config, "max_sequence_length"):
        context_len = model.config.max_sequence_length
    elif hasattr(model.config, "max_position_embeddings"):
        context_len = model.config.max_position_embeddings
    elif hasattr(model.config, "tokenizer_model_max_length"):
        context_len = model.config.tokenizer_model_max_length
    else:
        context_len = 2048

    return tokenizer, model, image_processor, context_len

model_path = os.path.expanduser(args.model_path)
model_name = get_model_name_from_path(model_path)
from llava.model.builder import lxr_load_llava_next_ov,lxr_load_pretrained_model
tokenizer, model, image_processor, context_len = lxr_load_pretrained_model(model_path, args.model_base, model_name)
......
parser = argparse.ArgumentParser()
parser.add_argument("--model-path", type=str, default="LLaVA-NeXT/checkpoints/llava_next_endo_qwen_lora")
parser.add_argument("--model-base", type=str, default="LLaVA-NeXT/llava-onevision-qwen2-7b-ov")

Good Luck!
If you find it useful, please give me a thumbs up.

lxr_load_llava_next_ov

Good job! I carefully reviewed the load_pretrained_model function and noticed that when the model_name includes "lora", there is a lack of processing logic for the Qwen series of models. Therefore, I added support for Qwen in load_pretrained_model to enable compatibility with Llava-One.

elif "qwen" in model_name.lower():
    from llava.model.language_model.llava_qwen import LlavaQwenConfig
    if overwrite_config is not None:
        llava_cfg = LlavaQwenConfig.from_pretrained(model_path)
        rank0_print(f"Overwriting config with {overwrite_config}")
        for k, v in overwrite_config.items():
            setattr(llava_cfg, k, v)
        model = LlavaQwenForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, config=llava_cfg, **kwargs)
    else:
        model = LlavaQwenForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, **kwargs)

elif "qwen" in model_name.lower():
    from llava.model.language_model.llava_qwen import LlavaQwenConfig
    if overwrite_config is not None:
        llava_cfg = LlavaQwenConfig.from_pretrained(model_path)
        rank0_print(f"Overwriting config with {overwrite_config}")
        for k, v in overwrite_config.items():
            setattr(llava_cfg, k, v)
        model = LlavaQwenForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, config=llava_cfg, **kwargs)
    else:
        model = LlavaQwenForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, **kwargs)

same as this pull  https://github.com/LLaVA-VL/LLaVA-NeXT/pull/277/files

@lxr-1204  where is the function lxr_load_pretrained_model ?

elif "qwen" in model_name.lower():
    from llava.model.language_model.llava_qwen import LlavaQwenConfig
    if overwrite_config is not None:
        llava_cfg = LlavaQwenConfig.from_pretrained(model_path)
        rank0_print(f"Overwriting config with {overwrite_config}")
        for k, v in overwrite_config.items():
            setattr(llava_cfg, k, v)
        model = LlavaQwenForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, attn_implementation=attn_implementation, config=llava_cfg, **kwargs)
    else:
        model = LlavaQwenForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, attn_implementation=attn_implementation, **kwargs)

same as this pull https://github.com/LLaVA-VL/LLaVA-NeXT/pull/277/files

Doesn't this just omit the lora_config entirely? You're just not setting the config flag. At this point are we will using the lora weights at all?

elif "qwen" in model_name.lower():
    from llava.model.language_model.llava_qwen import LlavaQwenConfig
    if overwrite_config is not None:
        llava_cfg = LlavaQwenConfig.from_pretrained(model_path)
        rank0_print(f"Overwriting config with {overwrite_config}")
        for k, v in overwrite_config.items():
            setattr(llava_cfg, k, v)
        model = LlavaQwenForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, attn_implementation=attn_implementation, config=llava_cfg, **kwargs)
    else:
        model = LlavaQwenForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, attn_implementation=attn_implementation, **kwargs)

same as this pull https://github.com/LLaVA-VL/LLaVA-NeXT/pull/277/files

@zhudongwork Doesn't this just omit the lora_config entirely? You're just not setting the config flag. At this point are we will using the lora weights at all?

I mean it seems to have worked. I just don't understand why :sweat_smile:

@lxr-1204 where is the function lxr_load_pretrained_model ?函数 lxr_load_pretrained_model 在哪里？

Right Here！https://github.com/LLaVA-VL/LLaVA-NeXT/issues/253#issuecomment-2406434035


Make eslint tests run in ember test --server

Is it possible to make test'em run eslint the way it currently runs jshint? And ideally not run jshint?

Works for me, do you have the latest version of the .eslintrc file?

Perhaps not will check tomorrow and get back to you. Thanks for the fast
response!
On Wed, 20 May 2015 at 23:00 Jonathan Kingston<EMAIL_ADDRESS>wrote:

Works for me, do you have the latest version of the .eslintrc file?
—
Reply to this email directly or view it on GitHub
https://github.com/jonathanKingston/ember-cli-eslint/issues/16#issuecomment-104052684
.

I had an issue with:
"rules": [

Should have been:
"rules": {

Tired coding does this to me.
Anyway there is some issues with the package I am ironing out before we hit 1.0.0 so certainly willing to wait until this is resolved too.

The .eslintrc file looks okay.
I'm only running version 0.2.4 do i need to be on the beta?

Also just to clarify i get the output correctly when building or starting to run the tests...

But as you can see it doesn't actually fail in the in the testem runner. It reports all 241 tests passed.

@chrisgame worth trying the beta as I say, it is basically working fine.
npm install ember-cli-eslint@latest; ember generate ember-cli-eslint;

Then for the output are you using the testGenerator?

@jonathanKingston Yep, i've followed the instructions in the read me.
With the beta version i get the following...

I've pushed up and clean ember cli app with only the addon installed and the steps taken on the readme so you can see if i'm doing anything stupid. You can find it here
https://github.com/chrisgame/clean-install-ember-cli-eslint

@chrisgame looks like you need to run:
npm install --save-dev eslint-config-ember

I will add this to a common issues section in the README; the .eslintrc points to it which you can totally get rid of if you choose but hopefully are pretty sane defaults (Which I am open to hearing feedback on too here: https://github.com/jonathanKingston/eslint-config-ember).

@jonathanKingston :blush: I get it now! Works like a dream, thanks for your help.


Globe icons in dashboard do not work

I use your Internal Asset plugin.
It makes the assets under "internal" folder private.
However, the links on globe icons in Craft dashboard to the assets are broken because the url does not contain "internal" folder.
A screenshot is attached.
I would like to ask if it is a bug or a specification, and if you could fix it.

Hi @Kota-M
Thank you for reporting this. Could you please make sure that you are using the latest version of the plugin 2.3.1 with Craft CMS 3.
The plugin actually creates a volume, that should automatically prefix all urls with internal, independent of whether they are used in the admin panel or on the site directly. Could you please post one of the links from the globe icon? Just to see what is actually used as link there.

Yes, I use Internal Assets 2.3.1 and Craft 3.1.33.
I am sorry to say that I cannot post the exact url here, but it is something like this.
{siteURL}/Document/RegionToronto/Kota-M_33fe784e-3413-4bda-ab3e-ee4ee6d4bf5f/0709-1.JPG

I'm also experiencing this bug. However, I'm not 100% I've setup the asset paths in the craft control panel properly. I can't access the files that I've put outside the webroot.

This issue should no longer occur in the new Craft 4 version of the plugin.


Using Yourls in Intranet while urls remain accessible to public

I'm running Yourls (v 1.7.1) as private installation and I would like to make it accessible through EZproxy so that only users in our network or using their single sign on can create new URLs. But I also want the created URLs to be accessible to the public without any need to login.
I'm not sure how to accomplish this.

Your question is completely related to your proxy and firewall configuration. No relation with YOURLS.
Please ask your sysadmin.


The argument type 'File/1/' can't be assigned to the parameter type 'File/2/'.

This issue is still not fixed in the latest version i.e  2.0.4
flutter run -d chrome
Launching lib\main.dart on Chrome in debug mode...
lib/screens/myprofilescreen.dart:93:51: Error: The argument type 'File/1/' can't be assigned to the parameter type
'File/2/'.

'File/1/' is from 'package:universal_io/src/io/file.dart'
('/D:/flutter/.pub-cache/hosted/pub.dartlang.org/universal_io-2.0.4/lib/src/io/file.dart').
'File/2/' is from 'dart:io'.
: new FileImage(_imageFile),
^
lib/screens/CreatePostScreen.dart:163:46: Error: The argument type 'File/1/' can't be assigned to the parameter type
'File/2/'.
'File/1/' is from 'package:universal_io/src/io/file.dart'
('/D:/flutter/.pub-cache/hosted/pub.dartlang.org/universal_io-2.0.4/lib/src/io/file.dart').
'File/2/' is from 'dart:io'.
child: _image != null ? Image.file(_image) : null,
^
lib/screens/EditPostScreen.dart:155:46: Error: The argument type 'File/1/' can't be assigned to the parameter type
'File/2/'.
'File/1/' is from 'package:universal_io/src/io/file.dart'
('/D:/flutter/.pub-cache/hosted/pub.dartlang.org/universal_io-2.0.4/lib/src/io/file.dart').
'File/2/' is from 'dart:io'.
child: _image != null ? Image.file(File(_image.path)) : null,

Hi! Package universal_html has a test case for File and it runs without problems in browsers. Does your application (or some dependency) accidentally contain directive import 'package:universal_io/src/io/file.dart';?

Here's a workaround you can use for now (and maybe this will help lead you to the root cause @terrier989):
Explicitly define the data type for your File as dynamic, then pass it to Image.file(). Here's a working example of how I'm using it:
dynamic file = File(source.path);
provider = FileImage(file);

There is a conflict between different files.
To resolve this import io package as

import 'dart: io' as i;

and declare file variable as

i.File imageFile;

I found the issue universal_io is working fine for me but Image.file is not universal so I used Image.memory(         File(alternativeLocalImagePath).readAsBytesSync())

Perhaps this helps:
I am trying to convert my firebase project to a web app. The conflict is the universal file vs. the FirebaseStorage dart.io and the following is where I get stuck:
../../Developer/flutter/.pub-cache/hosted/pub.dartlang.org/firebase_storage-10.0.5/lib/src/reference.dart:128:52: Error: The argument type 'File/*1*/' can't be assigned to the parameter type 'File/*2*/'.
 - 'File/*1*/' is from 'package:universal_io/src/io/file.dart' ('../../Developer/flutter/.pub-cache/hosted/pub.dartlang.org/universal_io-2.0.4/lib/src/io/file.dart').
 - 'File/*2*/' is from 'dart:io'.
    return UploadTask._(storage, _delegate.putFile(file, metadata));

My import is import 'package:universal_io/io.dart';

same problem , any news ?

Assuming you are using XFile, there is a bug in fromData as documented here.  That was the cause of my error, not related to universal_io


Do not render cache after preloaded response

Fix a race condition where BrowserAdapter#visitStarted renders cache after preloaded content is rendered.
The BrowserAdapter has the following:
export class BrowserAdapter implements Adapter {
  //...
  visitStarted(visit) {
    visit.issueRequest() //=> Async send request
    visit.changeHistory()
    visit.goToSamePageAnchor()
    visit.loadCachedSnapshot() //=> render cached snapshot while waiting for async request
  }

When visit.issueRequest() takes longer than 1ms, then visit.loadCachedSnapshot() renders the cache first.  When it renders immediately (as in a preloaded response) then visit.loadCachedSnapshot() renders last.
Fixes https://github.com/hotwired/turbo/issues/447

@seanpdoyle - can you review this PR and let me know if I need to change anything?

@ghiculescu  Yes, we do use the iOS adapter.  What issue are you experiencing?

It was a screen flicker, but it ended up being unrelated and due to us not creating a new view controller on each visit - apparently yiu really need to do that. So not this issue at all.
That said... does that code I linked look like it could cause the issue this PR addresses?

That said... does that code I linked look like it could cause the issue this PR addresses?

I haven't tested this particular bug on mobile, but it looks to follow the same older pattern and quite possibly has the same bug.  Are you going to submit a PR to suggest an update to follow the same pattern as this MR?

I can, but I'm not confident it's an issue yet. Do you have a test page I can use to replicate it?

@ghiculescu - See the steps to reproduce and demo app linked in the original https://github.com/hotwired/turbo/issues/447 issue


Update travis

As 0.12 is released, no reason to build on unstable 0.11.
I also added some https://coveralls.io support. If you register there (free for FOSS), just removing the comment in .travis.yml should be enough to submit there.
If you use coveralls, I can update #473 to include a coverage badge as well :smile:

Ha we used Coveralls for a while, and then had to remove it because they were causing too many build failures because of unreliable servers.
We removed it here: https://github.com/tbranyen/backbone.layoutmanager/commit/06a9f053cf178384066a1caefffc45ed204505fc
I've since used it on other projects and it's been fine lately, so I'd be okay with adding it back.

Oh, didn't know that.
I can provide separate PRs if you wanna think about it some, while still building on more relevant versions of Node?

Looks like coveralls is already configured.  It should pull from the travis build token for authing.

Want be to remove the comment from .travis.yml?

@tbranyen GH seems to be having trouble. Actually broke a build because Bower barfed all over.
Would you be interested in a PR the removes the dependency on bower for tests, instead using npm?

@SimenB I thought i already did that.  Definitely fix whatever you find, because Bower should not be a testing dependency right now.

You have to run bower install to be able to run the tests. Had to check .travis.yml to figure out why phantom just hung

@tbranyen #476

@tbranyen Updated to actually submit to coveralls. Seems to work fine. Added badge to readme as well.

Perfect!


"This page is live on _____.notion.site" Banner...

This banner is present on all of my pages on my fruition site. It seems like it is also present on others' fruition sites. My only guess is that this is Notion's attempt to stop people from using things like fruition which pull notion page content and display it elsewhere.
Any other thoughts? I'll work on some CSS code to hide it. Curious to hear other people's opinions.

Here is what I got:
/* removing 'This page is live on ____.notion.site.' banner */
div.notion-cursor-listener > div:first-child > header:first-child > div:nth-child(2) {
    display: none !important;
}

Seems like you can't remove divs by the text they contain in CSS but you can in Javascript. I tried briefly but couldn't get it to work. Does anyone else have experience with that? It would be a much better solution.

I'll keep an eye out for responses, I'd like to remove that div too.

I have the same issue too and it wasn't there till like the day before, it is super annoying, would like to sort this out asap, I've sent an email on this to stephen as well, let's see what happens.

+1 :(

I noticed that the URL you get if you click "view site" is different, and does not show the banner. I'm not sure how to change that in the code since it's concatenated and I'm not fluent enough with code to parse it out, but I take it that would fix the root cause.

I noticed that the URL you get if you click "view site" is different, and does not show the banner. I'm not sure how to change that in the code since it's concatenated and I'm not fluent enough with code to parse it out, but I take it that would fix the root cause.
E.G., in my case the code is using https://www.notion.so/thiswholecity/The-Best-Made-Company-Archive-3673bfb8289a4dd0933e2fac245acab3
but the "View site" link is using https://thiswholecity.notion.site/The-Best-Made-Company-Archive-3673bfb8289a4dd0933e2fac245acab3

UPDATE: I found how to do it in another issue here, but it doesn't seem to fix this issue, or maybe there's another spot in the code that makes the difference.

I noticed that the URL you get if you click "view site" is different, and does not show the banner. I'm not sure how to change that in the code since it's concatenated and I'm not fluent enough with code to parse it out, but I take it that would fix the root cause.
E.G., in my case the code is using https://www.notion.so/thiswholecity/The-Best-Made-Company-Archive-3673bfb8289a4dd0933e2fac245acab3
but the "View site" link is using https://thiswholecity.notion.site/The-Best-Made-Company-Archive-3673bfb8289a4dd0933e2fac245acab3

UPDATE: I found how to do it in another issue here, but it doesn't seem to fix this issue, or maybe there's another spot in the code that makes the difference.

So it doesn't really fix the issue, right?

Yeah, I think that distinction between URL formats is important

Above is the cloudflare code which pulls from the notion.so/<user_name>/<page_id> and which has the "This page is live on ..." banner
When I am logged in on the web the banner shows as well:

I think this banner is supposed to just make it clearer when editing a Notion page that is published and to view the site settings.
When I am not logged in on the web the same link automatically redetects to the <user_name>.notion.site/<page_id> link which which does not have the banner
To solve this we have to edit the code to pull from <user_name>.notion.site/<page_id> instead of how it is now.
I can work on that later this week but let me know if anyone figures it out in the mean time.

Ok, so there must be another place in the code that pulls from the notion.so link instead of the <user_name>.notion.site link if that other fix doesn't solve it?

https://x.com/NotionHQ/status/1734637142964629545

https://x.com/NotionHQ/status/1734637142964629545

Left them a comment about it, thanks for pointing to it.

Here is what I got:
/* removing 'This page is live on ____.notion.site.' banner */
div.notion-cursor-listener > div:first-child > header:first-child > div:nth-child(2) {
    display: none !important;
}

Seems like you can't remove divs by the text they contain in CSS but you can in Javascript. I tried briefly but couldn't get it to work. Does anyone else have experience with that? It would be a much better solution.

This does the trick. Hopefully we can get a more direct fix too.

Ok, so there must be another place in the code that pulls from the notion.so link instead of the <user_name>.notion.site link if that other fix doesn't solve it?

I changed every instance of notion.so to my username site and I still had to use the CSS fix, so not sure what the deal is.

Ok for some reason the banner is gone now.... even without the CSS. I'm not sure what did it... Is it gone for anyone else?

Ok for some reason the banner is gone now.... even without the CSS. I'm not sure what did it... Is it gone for anyone else?

Yeah, it went away for me too, I Think they sorted it out

Awesome, good work team. I'll reopen if the issue returns

If you make a duplicate of the page that has it it won’t have it on there yes it ducks to lose the original but after you delete it and stuff it still won’t be there!


[FLINK-12726][table-common] Fix ANY type serialization

What is the purpose of the change
Uses the correct methods for type serializer serialization.
Brief change log
See commit messages.
Verifying this change
This change is already covered by existing tests, such as LogicalTypesTes.
Does this pull request potentially affect one of the following parts:

Dependencies (does it add or upgrade a dependency): no
The public API, i.e., is any changed class annotated with @Public(Evolving): yes
The serializers: no
The runtime per-record code paths (performance sensitive): no
Anything that affects deployment or recovery: JobManager (and its components), Checkpointing, Yarn/Mesos, ZooKeeper: no
The S3 file system connector: no

Documentation

Does this pull request introduce a new feature? no
If yes, how is the feature documented? not applicable

@flinkbot approve all

Thanks for updating the Javadoc for TypeSerializerSnapshot.
LGTM from my side, once Travis gives green.


CSGOWinBig Discord?

Seems like a Discord/Slack may be more effective for anyone who's having trouble setting up the script. We should make one for people who are new to CSGOWinBig and reserve the "Issues" section for ACTUAL CODE ISSUES and try to keep it from being used as a forum (spams everybody's inbox + hard to sort through actual issues in the code vs. "HOW DO I SETUP SCRIPT????")

@andrewda
maybe add some wiki pages how to simple set up the site and some possible fixes for the known issues

when i log in to my bot it just  say "lanch the bot" then write help for commands" then it dont log in someone have any ideas why this issue happen? it worked before until i changed to my friends bot

Following guides should be enough to set up everything...

Thinking is good aswell

@TheAnthonyNL  Could you help me with my bot? 2 different people have tried to help me @GreYzZ  And @mx0c my bot wont start we do it correcly.. we have tested many different things but it dont work! please add me here; http://steamcommunity.com/id/aaaaaKung


GRCh37 samtools ERROR????

Hi,
Some help please?
I just installed bcbio, as follows:
wget https://raw.github.com/chapmanb/bcbio-nextgen/master/scripts/bcbio_nextgen_install.py
python bcbio_nextgen_install.py /usr/local/share/bcbio --tooldir=/usr/local
--genomes GRCh37 --aligners bwa --aligners bowtie2
There is a config yaml in /hpf/tools/centos6/bcbio/20180111/config that might be helpful.
[apple@data3 config]$ cat install-params.yaml
aligners:

bwa
bowtie2
rtg
twobit
datatarget:
variation
rnaseq
smallrna
genomes:
GRCh37
isolate: false
tooldir: /hpf/tools/centos6/bcbio/20180111
toolplus: []

THE PROBLEM:
When I run this command:
bcbio_nextgen.py /hpf/largeprojects/pray/rbaldwin/project1/GIAB/bcbio_system.yaml /hpf/largeprojects/pray/rbaldwin/project1/GIAB/NA12878/config/NA12878.yaml -n 10
I get this error:
[2018-01-31T16:41Z] System YAML configuration: /hpf/largeprojects/pray/rbaldwin/project1/GIAB/bcbio_system.yaml
[2018-01-31T16:41Z] Resource requests: bwa, sambamba, samtools; memory: 5.00, 5.00, 5.00; cores: 8, 8, 8
[2018-01-31T16:41Z] Configuring 1 jobs to run, using 8 cores each with 40.1g of memory reserved for each job
[2018-01-31T16:41Z] Timing: organize samples
[2018-01-31T16:41Z] multiprocessing: organize_samples
[2018-01-31T16:41Z] Using input YAML configuration: /hpf/largeprojects/pray/rbaldwin/project1/GIAB/NA12878/config/NA12878.yaml
[2018-01-31T16:41Z] Checking sample YAML configuration: /hpf/largeprojects/pray/rbaldwin/project1/GIAB/NA12878/config/NA12878.yaml
[2018-01-31T16:41Z] Downloading GRCh37 samtools from AWS
Traceback (most recent call last):
File "/hpf/tools/centos6/bcbio/20180111/bin/bcbio_nextgen.py", line 241, in
main(**kwargs)
File "/hpf/tools/centos6/bcbio/20180111/bin/bcbio_nextgen.py", line 46, in main
run_main(**kwargs)
File "/hpf/tools/centos6/bcbio/20180111/anaconda/lib/python2.7/site-packages/bcbio/pipeline/main.py", line 43, in run_main
fc_dir, run_info_yaml)
File "/hpf/tools/centos6/bcbio/20180111/anaconda/lib/python2.7/site-packages/bcbio/pipeline/main.py", line 87, in _run_toplevel
for xs in pipeline(config, run_info_yaml, parallel, dirs, samples):
File "/hpf/tools/centos6/bcbio/20180111/anaconda/lib/python2.7/site-packages/bcbio/pipeline/main.py", line 120, in variant2pipeline
[x[0]["description"] for x in samples]]])
File "/hpf/tools/centos6/bcbio/20180111/anaconda/lib/python2.7/site-packages/bcbio/distributed/multi.py", line 28, in run_parallel
return run_multicore(fn, items, config, parallel=parallel)
File "/hpf/tools/centos6/bcbio/20180111/anaconda/lib/python2.7/site-packages/bcbio/distributed/multi.py", line 86, in run_multicore
for data in joblib.Parallel(parallel["num_jobs"], batch_size=1)(joblib.delayed(fn)(x) for x in items):
File "/hpf/tools/centos6/bcbio/20180111/anaconda/lib/python2.7/site-packages/joblib/parallel.py", line 779, in call
while self.dispatch_one_batch(iterator):
File "/hpf/tools/centos6/bcbio/20180111/anaconda/lib/python2.7/site-packages/joblib/parallel.py", line 625, in dispatch_one_batch
self._dispatch(tasks)
File "/hpf/tools/centos6/bcbio/20180111/anaconda/lib/python2.7/site-packages/joblib/parallel.py", line 588, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "/hpf/tools/centos6/bcbio/20180111/anaconda/lib/python2.7/site-packages/joblib/_parallel_backends.py", line 111, in apply_async
result = ImmediateResult(func)
File "/hpf/tools/centos6/bcbio/20180111/anaconda/lib/python2.7/site-packages/joblib/_parallel_backends.py", line 332, in init
self.results = batch()
File "/hpf/tools/centos6/bcbio/20180111/anaconda/lib/python2.7/site-packages/joblib/parallel.py", line 131, in call
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "/hpf/tools/centos6/bcbio/20180111/anaconda/lib/python2.7/site-packages/bcbio/utils.py", line 52, in wrapper
return apply(f, *args, **kwargs)
File "/hpf/tools/centos6/bcbio/20180111/anaconda/lib/python2.7/site-packages/bcbio/distributed/multitasks.py", line 403, in organize_samples
return run_info.organize(*args)
File "/hpf/tools/centos6/bcbio/20180111/anaconda/lib/python2.7/site-packages/bcbio/pipeline/run_info.py", line 70, in organize
item = add_reference_resources(item, remote_retriever)
File "/hpf/tools/centos6/bcbio/20180111/anaconda/lib/python2.7/site-packages/bcbio/pipeline/run_info.py", line 164, in add_reference_resources
data["reference"] = genome.get_refs(data["genome_build"], aligner, data["dirs"]["galaxy"], data)
File "/hpf/tools/centos6/bcbio/20180111/anaconda/lib/python2.7/site-packages/bcbio/pipeline/genome.py", line 226, in get_refs
galaxy_config, data)
File "/hpf/tools/centos6/bcbio/20180111/anaconda/lib/python2.7/site-packages/bcbio/pipeline/genome.py", line 172, in _get_ref_from_galaxy_loc
cur_ref = download_prepped_genome(genome_build, data, name, need_remap)
File "/hpf/tools/centos6/bcbio/20180111/anaconda/lib/python2.7/site-packages/bcbio/pipeline/genome.py", line 310, in download_prepped_genome
raise ValueError("Could not find reference genome file %s %s" % (genome_build, name))
ValueError: Could not find reference genome file GRCh37 samtools

Can you report the contents of this file?
/hpf/largeprojects/pray/rbaldwin/project1/GIAB/NA12878/config/NA12878.yaml

rbaldwin@qlogin5 config]$ cat NA12878.yaml
details:

algorithm:
aligner: bwa
mark_duplicates: true
realign: gatk
recalibrate: gatk
variantcaller: gatk-haplotype
analysis: variant2
description: NA12878_S1_L001
files:

/localhd/32297704/NA12878/NA12878_S1_L001_R1_001.fastq.gz
/localhd/32297704/NA12878/NA12878_S1_L001_R2_001.fastq.gz
genome_build: GRCh37
metadata:
batch: batch1
phenotype: normal
sex: female
upload:
dir: /hpf/largeprojects/pray/rbaldwin/project1/GIAB/final
fc_name: NA12878
upload:
dir: ../final

Robert;
Sorry about the issue, it looks like your command points to an alternative bcbio_system.yaml file: /hpf/largeprojects/pray/rbaldwin/project1/GIAB/bcbio_system.yaml  instead of the one that was created as part of your installation. This would result in bcbio trying to find the genome data relative to this file and is likely the cause of your issue.
My suggestion would be to use the standard file installed, which I think should be in /hpf/tools/centos6/bcbio/20180111/galaxy/bcbio_system.yaml if you need to adjust it. To do this, you don't need to reference anything on the command line and can just point to your sample YAML file:
bcbio_nextgen.py /hpf/largeprojects/pray/rbaldwin/project1/GIAB/NA12878/config/NA12878.yaml -n 10

Hope that fixes things for you and get the analysis running.

o.k thanks.
It says in the documentation that it is o.k to pass an alternative system config file to bcbio-nextgen.py script. But it looks like that may only be o.k provided that the alternative system.yaml is in the same folder as the default one, or are you saying that you should just not use the alternative system config at all?
The problem is that I don't have permission to edit or create files in the /hpf/tools/centos6/ path. But I may be able to move files there, I don't know. I'll have to figure this out. In any case, I'll try it with the default first and see how it goes.
thanks for your help - Robert

Robert;
Thanks much for this feedback. The alternative file does work but is a bit tricky as it also needs to point at the data files. The preferred way to do this is to define the resource changes you need directly in your sample YAML file, which would take precedence over the default system YAML:
https://bcbio-nextgen.readthedocs.io/en/latest/contents/configuration.html#sample-or-run-specific-resources
I'd be happy to make the documentation more helpful in pointing folks at this. Where specifically were you reading? I can work on tackling that to make sure it's more clear. Thank you again for this really helpful feedback and hope this gets your analysis running.

I was reading the documentation on the web page, at the very top of the Configuration section.

Robert -- thanks again for this feedback. I've modified the documentation here to put more emphasis on configuring your sample information and tweaking there, to avoid sending anyone down the wrong path going forward. Please let us know if you run into any other issues.


[stable/weave-scope] Add ingress to weave-scope-frontend

What this PR does / why we need it:

This PR adds Ingress to weave-scope-frontend
Add ingress.yaml, update values.yaml, README.md and Chart.yaml

Which issue this PR fixes

fixes #14049

Checklist

[x] DCO signed
[x] Chart Version bumped
[x] Variables are documented in the README.md
[x] Title of the PR starts with chart name (e.g. [stable/chart])

/assign @fbarl

Thanks for your PR @abhijit838!
Taking a quick look at the code, it looks good to me, but I'd like @bboreham or @dholbach to make a final call on ingress.yaml as I don't feel sufficiently qualified.
/assign @bboreham
/assign @dholbach

/assign @dholbach

/approve

/ok-to-test

/lgtm


Bound all OrdinaryDiffEq <6.34 to DiffEqBase <6.109.0

This PR was generated by the following script:
import Pkg
import RegistryTools
import TOML

const Compress = RegistryTools.Compress

ode_name = "OrdinaryDiffEq"
ode_versions_str = "<6.34"
ode_versions = Pkg.Types.semver_spec(ode_versions_str)
debase_name = "DiffEqBase"
debase_constraint_str = "<6.109.0"
debase_constraint = Pkg.Types.semver_spec(debase_constraint_str)

branch_name = "dpa/ode-debase-compat"
commit_message = "Bound all $(ode_name) $(ode_versions_str) to $(debase_name) $(debase_constraint_str)"

mktempdir() do dir
    cd(dir) do
        run(`git clone git@github.com:JuliaRegistries/General.git`)
        cd("General") do
            run(`git checkout master`)
            run(`git checkout -B $(branch_name)`)
            registry = TOML.parsefile("Registry.toml")
            ode_infos = [x for x in values(registry["packages"]) if x["name"] == ode_name]
            ode_info = only(ode_infos)
            ode_path = ode_info["path"]
            ode_compat_file = joinpath(ode_path, "Compat.toml")
            compat = Compress.load(ode_compat_file)
            for (k, v) in compat
                if k in ode_versions
                    old_compat_str = v[debase_name]
                    old_compat = Pkg.Types.VersionSpec(old_compat_str)
                    new_compat = intersect(old_compat, debase_constraint)
                    new_compat_str = string(new_compat)
                    v[debase_name] = new_compat_str
                end
            end
            Compress.save(ode_compat_file, compat)
            run(`git --no-pager diff HEAD`)
            run(`git add -A`)
            run(`git commit -m "$(commit_message)"`)
            run(`git push -f origin $(branch_name)`)
        end
    end
end

cc: @ChrisRackauckas @chriselrod

Well part of this is that I'm ripping off bandaids right now.  is just too obvious. So we've been on a rampage the last 6 months to do things like, rip out the nonlinear solver code that is in DiffEqBase and only half used, with it then calling halfway to OrdinaryDiffEq and NonlinearSolve, to actually just being internal to OrdinaryDiffEq and NonlinearSolve (which is what this was). All of the interfaces are getting documented and tested (see https://docs.sciml.ai/SciMLBase/stable/). But some of this requires acknowledging that one package used some internal that it should've have, ripping it out and making it use the now public API, and suffering the consequences. Otherwise we would've had like 6 breaking updates to a package with 300 downstreams this summer, even though almost all of the issues were just to one package.
But, I think we're just about done with that project. The last piece is https://docs.sciml.ai/SciMLOperators/stable/, but that will have a slower deprecation because DiffEqOperators are more user-facing.

Was JuliaRegistries deleting my comment intentional?
It was a comment on why I kept running into these issues while seemingly few other people had, or at least few other people reported issues.
More typically, I'm on older fixed versions of everything (but then you have to be careful to use that same manifest when building the image, which is probably what I should have done), or on bleeding edges of everything.
But I was using an odd-ball assortment of recent and old versions, thereby testing combinations of packages together that few other people probably encountered.
Anyway, I agree that it is good to rip this bandaid off.
Consistent APIs, etc, are important. In an ideal world, things are consistent enough that people can guess what the API without having to look things up.
Consistent APIs are much easier to remember; remembering a couple patterns and rules is way easier than memorizing a litany of conventions.


Type-checking errors on fallthrough attributes and AllowedComponentProps

Consider a simple component using a Field from the vee-validate library:
<script setup lang="ts">
import { Field } from 'vee-validate';
</script>

<template>
  <Field id="name" name="name" value="Cedric" />
</template>

Note that Field has an id, which is not a prop but a fallthrough attribute, so the resulting HTML is:
<input id="name" name="name">

The problem is vue-tsc (v1.6.5) does not compile, as id is not recognized:
npm run type-check
src/App.vue:6:10 - error TS2345: Argument of type '{ id: string; name: string; value: string; }' is not assignable to parameter of type '{ label?: string | undefined; as?: string | Record<string, any> | undefined; bails?: boolean | undefined; uncheckedValue?: any; validateOnInput?: boolean | undefined; validateOnChange?: boolean | undefined; ... 21 more ...; onVnodeUnmounted?: ((vnode: VNode<...>) => void) | ... 1 more ... | undefined; }'.
  Object literal may only specify known properties, and 'id' does not exist in type '{ label?: string | undefined; as?: string | Record<string, any> | undefined; bails?: boolean | undefined; uncheckedValue?: any; validateOnInput?: boolean | undefined; validateOnChange?: boolean | undefined; ... 21 more ...; onVnodeUnmounted?: ((vnode: VNode<...>) => void) | ... 1 more ... | undefined; }'.

6   <Field id="name" name="name" value="Cedric" />

This only happens with strictTemplates: true in vueCompilerOptions.
I used to get around this by using AllowedComponentProps:
declare module '@vue/runtime-core' {
  interface AllowedComponentProps {
    id?: string;
  }
}

but this does not do the trick here, as now Field is not found at all:
src/App.vue:6:4 - error TS2339: Property 'Field' does not exist on type '{}'.

You can find a small repro here: https://stackblitz.com/edit/github-ekza7z?file=src/App.vue
npm i && npm run type-check

So my questions are:

is AllowedComponentProps the correct way to handle the case of attributes here?
if so, as it does break the compilation by not finding Field anymore, is there an issue with vue-tsc?
or is it something that the vee-validate author could do something about so it would work out of the box?

Updated repro using Vue playground
https://play.vuejs.org/#eNp9UctOwzAQ/BXLl15Kc+itBCSoigQHQNCjL1G8DS5+yV6HSFH+nXVCU5BQT/bOzI5n1z2/837VJuAbXsY6KI8sAibPdGWbG8ExCn4rrDLeBWQ9e1CgJRvYITjDFi3AVVtpJSuExbWwZTF5UAcVCMZrYqhirJw6lSRTWxkQnOXjXJFPyuUWZFA1AQX1lcVswpcUpnb2oJrVMTpLiftsLHjtjFcawotH5SwF3rCRyVyltft6GjEMCZYnvP6A+vMf/Bi7jAn+GiBCaCnZzGEVGsCJ3r0/Q0f3mTROJk3qC+QbRKdTzjjJ7pOVFPuXbkz7OO5a2WYfdx2CjaehctCsHEa94PRt2wujn+OuV+v5iYi0Xdz/bPWvr7ADH74B/JCvXQ==

Not sure i this is the intended behavior :(

I've had this exact issue - it was complaining about fallthrough props only on generic components; Field seems to be one too.
Updating vue-tsc to its current version seems to fix it. Your Stackblitz repro no longer fails its type-check after using vue-tsc 1.8.8.

@jpoep Thanks for checking this. It still fails with v1.8.8 if the project has global components declared
I updated the Stackblitz https://stackblitz.com/edit/github-ekza7z?file=env.d.ts,package.json
/// <reference types="vite/client" />
import type App from './src/App.vue';

declare module '@vue/runtime-core' {
  interface GlobalComponents {
    App: typeof App;
  }
  interface AllowedComponentProps {
    id?: string;
  }
}

This issue will be fixed by #3487, which makes strictTemplates: false work with generic components. If you enable that option, #3443 will occur - but they are different issues.


Diacritics not displayed in when building .Rmd files

I am unable to produce diacritic chars when rendering .Rmd to html using the build function from R-Box. I am running SubimeText build 3126 on Mac OS X 10.12.2.
---
output: html_document
---

# Probando tildes

diacrítico español íéáóú

This is the result.

If I open the file in RStudio and use the knit button the diacritics show up.

To add to this issue, I find that if I render the document from the console using...
rmarkdown::render(input = './diactritics.Rmd', encoding = "UTF-8")

All the diacritics show up. It is only when I use the build function of R-box inside SublimeText that I get NA instead of the desired characters.

I am now in vacation, will look into this next week, thanks for letting me know of it


The https protocol can't be downloaded in stages, but http can be, how to solve it, the official website demo is also like this

Attach (recommended) or Link to PDF file here:
Configuration:

Web browser and its version:
Operating system and its version:
PDF.js version:
Is a browser extension:

Steps to reproduce the problem:
1.
2.
What is the expected behavior? (add screenshot)
What went wrong? (add screenshot)
Link to a viewer (if hosted on a site other than mozilla.github.io/pdf.js or as Firefox/Chrome extension):

https://mozilla.github.io/pdf.js/web/viewer.html

It's basically impossible to understand what this issue is about, since it contains nothing more than a title!
If you want to report an issue, or ask for help, you cannot leave the entire ISSUE_TEMPLATE.md completely blank, but you must provide all of the requested information. (In particular, please make sure that the "Steps to Reproduce" section is clear and easy to follow.)

thanks


react-hooks/exhaustive-deps in an if statement?

I create a canvas object from another library and store it in a ContextProvider's state to pass it down to components. I also use components to set certain modes of that object, but that only works after the canvas has been initialized, because I cant access canvas.someMode on null, so I have to check if the canvas has been created to decide whether to set another state.
eslint is complaining about react-hooks/exhaustive-deps when I have a state in an if statement. Why is this? No one changes a value in an if clause.
useEffect(() => {
    if(canvas) {
        canvas.isDrawingMode = drawMode;
        console.log("canvas.isDrawingMode: ", canvas.isDrawingMode);
    }
}, [drawMode]); //complains 'canvas' is not in the deps

I can not put canvas into deps here, because I gonna have multiple useEffect calls on different mode changes and I only want that useEffect call to be ran that has the right mode change in deps.

This is eslint-plugin-react, not eslint-plugin-react-hooks.

I was send here by eslint. Can't you just leave me alone?


toComposer

Doing as suggested in #14 making a toComposer function. Seems to work alright. I'm not sure about if it's a function that would be intuitive for the public to use.

Great work once again!
I agree that "public" public doesn't really have a great use, but obviously we require this for the inline functionality.
I'm thinking that i might make an interface out of the base and use that. I'm thinking that ISeq<> is as good a name as any. I'm thinking that it will derive from IEnumerable<>.
Anyway I'll have a play around in that space and see if I can make something pretty.


Replace quadratic-time string concatenation with linear-time joins.

Here's a micro-benchmark comparing string concatenation with list appending:
$ python3 -m timeit \
  -s 'def test():' \
  -s '  x = ""' \
  -s '  for _ in range(99999): x += "a"' \
  -s '  return x' \
  'test()'
10 loops, best of 5: 23.7 msec per loop

$ python3 -m timeit \
  -s 'def test():' \
  -s '  x = []' \
  -s '  add = x.append' \
  -s '  for _ in range(99999): add("a")' \
  -s '  return "".join(x)' \
  'test()'
20 loops, best of 5: 14.8 msec per loop

Seems like this would particularly help when rendering videos.

Thank you very much, especially for including the test, very helpful.


can't find string_view

Hi, thanks for your excellent work! But I use cmake+vs 2015 , it produce an error "can't find <string_view>" . I know it the feature of c++17, so I want to ask: how to fix it or which version can compile with vs2015?

Hi @Huang88888888Fei,
There are various non-standard implementations of std::string_view, e.g. https://github.com/martinmoene/string-view-lite.
However, I'm not sure that it will help you much, if you don't have a C++17 compliant compiler already. OpenXLSX uses other C++17 features, for example "constexpr if", which is a language-level feature rather than a library feature, and hence not easily replaceable.
Is there a reason that you want to use VS2015 rather than the latest version?

Thanks for your reply. I think you are right. If I don't use c++17, there are a lot of changes to be made. I'll just install the latest version.


Problem with code generation in RobCoGen

I know that this isn't an issue with the control toolbox itself, but with the generation step in RobCoGen, but I don't really know where to file an issue elsewhere.
I am trying to generate the files on Ubuntu 18.04 with openjdk-8-jre installed with the compatible RobCoGen version 0.4ad.0.
When trying to generate the C++ code everything works except the generation of the coordinate Transforms and the geometric jacobian. I tried the example.kindsl provided in the folder and a few other robots descriptions. I also reinstalled java and tried it inside a Docker Container to be sure that there is no conflict between different java versions on my laptop. In the Docker Container I tried openjdk-8-jre and openjdk-11-jre.
I get the following errors
When trying to get the C++ coordinate Transform:
Exception in thread "main" java.lang.NullPointerException
at iit.dsl.maxdsl.utils.MaximaConversionUtils.cleanMatrixRowOutput(Unknown Source)
at iit.dsl.maxdsl.utils.MaximaConversionUtils.getMatrix(Unknown Source)
at iit.dsl.coord.generator.maxima.converter.MaximaConverter.parseTransform(Unknown Source)
at iit.dsl.coord.generator.cpp.TransformsClasses.localClassesDefinitions(Unknown Source)
at iit.dsl.coord.generator.cpp.EigenFiles.implementationsFileContent(Unknown Source)
at iit.dsl.generator.cpp.kinematics.Transforms.generate(Unknown Source)
at iit.dsl.generator.cpp.Generator.generateTransforms(Unknown Source)
at iit.robcogen.KinDSLWrapper.generate(Unknown Source)
at iit.robcogen.Generator.main(Unknown Source)

When trying to get the geometric jacobian:
Exception in thread "main" java.lang.NullPointerException
at iit.dsl.maxdsl.utils.MaximaConversionUtils.cleanMatrixRowOutput(Unknown Source)
at iit.dsl.maxdsl.utils.MaximaConversionUtils.getMatrix(Unknown Source)
at iit.dsl.generator.maxima.Converter.getJacobianText(Unknown Source)
at iit.dsl.generator.cpp.kinematics.Jacobians.localClassesDefinitions(Unknown Source)
at iit.dsl.generator.cpp.kinematics.Jacobians.sourceFile(Unknown Source)
at iit.dsl.generator.cpp.Generator.generateJacobiansFiles(Unknown Source)
at iit.robcogen.KinDSLWrapper.generate(Unknown Source)
at iit.robcogen.Generator.main(Unknown Source)

Maybe someone else already had the same issue and could point me into the right direction. I tried to generate the code with the newer version 0.5.1 and it worked, but I don't know about the compatibility between the newer version 0.5.1 and the control toolbox.
kind regards

After writing with Marco Frigerio he showed me that this error is due to an old bug that was fixed in the newer version of RobCoGen, but not in 0.4ad.0.
Simply removing a "/" sign from the framework.properties configuration resolved this issue, like:
generator.maxima.libs.path = ../etc/maxima-libs (do NOT use "/" at the end).
I am just writing this in case somebody else runs into the same problem when trying to use the control toolbox with RobCoGen 0.4ad.0

hi I get the problem that looks a liitle similar to yours, but I still cannot compile successfully in 0.5.1. If I enter 1, 4, the error is:
robot203@robot203:~/robcogen/building/0build$ ./robcogen.sh ../etc/example.kindsl
 0 - CTDSL	- The coordinate transforms description file .ctdsl

 1 - ALLMx	- [All the Maxima targets]
 2 - TMx	- Maxima coordinate transforms
 3 - JMx	- Maxima geometric Jacobians

 4 - ALLC++	- [All the C++ targets]
 5 - CC++	- C++ common code
 6 - TC++	- C++ coordinate Transform
 7 - JC++	- C++ geometric Jacobians
 8 - DC++	- C++ common code for dynamics
 9 - IDC++	- C++ Inverse-Dynamics implementation
10 - FDC++	- C++ Forward-Dynamics implementation
11 - JSIMC++	- C++ Joint-Space-Inertia-Matrix calculation
12 - MKC++	- CMake file for C++ code

13 - ALLMt	- [All the Matlab targets]
14 - CMt	- Matlab common code
15 - TMt	- Matlab coordinate transforms
16 - JMt	- Matlab geometric Jacobians
17 - IDMt	- Matlab Forward/Inverse-Dynamics implementation
18 - JSIMMt	- Matlab Joint-Space-Inertia-Matrix calculation

19 - SL	- [All the SL targets]
20 - SLR	- SL, robot code (includes the robot model .dyn)
21 - SLU	- SL, robot-user code

22 - RB	- [All the robot models]
23 - RB_ROS	- XML URDF format
24 - RB_Feath	- Matlab structure for Featherstone's code
25 - RB_SDFAST	- SD/FAST model descritpion

26 - Test	- Support code to launch the Octave tests for Octave/C++

27 - Reload	- Reload the input files
28 - Quit	- Exit robcogen

What would you like to generate? Please enter the integer code:
1,4
Exception in thread "main" java.util.InputMismatchException
	at java.util.Scanner.throwFor(Scanner.java:864)
	at java.util.Scanner.next(Scanner.java:1485)
	at java.util.Scanner.nextInt(Scanner.java:2117)
	at java.util.Scanner.nextInt(Scanner.java:2076)
	at iit.robcogen.Generator.main(Unknown Source)

If I enter 4,the error is:
0 - CTDSL	- The coordinate transforms description file .ctdsl
1 - ALLMx	- [All the Maxima targets]
2 - TMx	- Maxima coordinate transforms
3 - JMx	- Maxima geometric Jacobians
4 - ALLC++	- [All the C++ targets]
5 - CC++	- C++ common code
6 - TC++	- C++ coordinate Transform
7 - JC++	- C++ geometric Jacobians
8 - DC++	- C++ common code for dynamics
9 - IDC++	- C++ Inverse-Dynamics implementation
10 - FDC++	- C++ Forward-Dynamics implementation
11 - JSIMC++	- C++ Joint-Space-Inertia-Matrix calculation
12 - MKC++	- CMake file for C++ code
13 - ALLMt	- [All the Matlab targets]
14 - CMt	- Matlab common code
15 - TMt	- Matlab coordinate transforms
16 - JMt	- Matlab geometric Jacobians
17 - IDMt	- Matlab Forward/Inverse-Dynamics implementation
18 - JSIMMt	- Matlab Joint-Space-Inertia-Matrix calculation
19 - SL	- [All the SL targets]
20 - SLR	- SL, robot code (includes the robot model .dyn)
21 - SLU	- SL, robot-user code
22 - RB	- [All the robot models]
23 - RB_ROS	- XML URDF format
24 - RB_Feath	- Matlab structure for Featherstone's code
25 - RB_SDFAST	- SD/FAST model descritpion
26 - Test	- Support code to launch the Octave tests for Octave/C++
27 - Reload	- Reload the input files
28 - Quit	- Exit robcogen
What would you like to generate? Please enter the integer code:
4
Exception in thread "main" java.lang.RuntimeException: Could not find Maxima source file: '/tmp/gen/maxima/Fancy_transforms6D' (did you forget to generate it?)
	at iit.dsl.coord.generator.maxima.converter.MaximaConverter.setModel(Unknown Source)
	at iit.dsl.coord.generator.maxima.converter.Utils.getConversionInfo(Unknown Source)
	at iit.dsl.coord.generator.cpp.LocalClasses.localClassesDefinitions(Unknown Source)
	at iit.dsl.coord.generator.cpp.ContainerClass.sourceFile(Unknown Source)
	at iit.dsl.generator.cpp.kinematics.Transforms.generate(Unknown Source)
	at iit.dsl.generator.cpp.Generator.generateTransforms(Unknown Source)
	at iit.robcogen.KinDSLWrapper.generate(Unknown Source)
	at iit.robcogen.KinDSLWrapper.generate(Unknown Source)
	at iit.robcogen.Generator.main(Unknown Source)

could you help me?


Optimize cammand depracation

Since the php artisan optimize command is deprecated (the file is pretty much empty and does nothing), shouldn't it be deleted permanently?
https://github.com/laravel/framework/blob/master/src/Illuminate/Foundation/Console/OptimizeCommand.php

This is not a bug. You can open a proposal in the laravel/internals repo

This is not a proposal of a new feature. This is a deprecation and removal notice.


Installing while working directory is inside a Dev Drive displays an error

Windows Version
Microsoft Windows [Version 10.0.26100.2454]
WSL Version
<IP_ADDRESS>
Are you using WSL 1 or WSL 2?

[x] WSL 2
[ ] WSL 1

Kernel Version
<IP_ADDRESS>-1
Distro Version
OracleLinux_7_9
Other Software
No response
Repro Steps

create a Dev Drive that is mounted at $HOME/Repositories
open a Terminal in a directory in the Dev Drive
run wsl --install OracleLinux_7_9

Expected Behavior
Output is as follows, without errors:
Installing: Oracle Linux 7.9
Oracle Linux 7.9 has been installed.
Launching Oracle Linux 7.9...
Installing, this may take a few minutes...
Please create a default UNIX user account. The username does not need to match your Windows username.
For more information visit: https://aka.ms/wslusers
Enter new UNIX username:

Actual Behavior
Output is as follows, containing the error:

<3>WSL (11) ERROR: CreateProcessCommon:551: chdir(/mnt/c/Users/antho/Repositories/tests/wsl) failed 5

Installing: Oracle Linux 7.9
Oracle Linux 7.9 has been installed.
Launching Oracle Linux 7.9...
Installing, this may take a few minutes...
<3>WSL (11) ERROR: CreateProcessCommon:551: chdir(/mnt/c/Users/antho/Repositories/anthonyvdotbe) failed 5
Please create a default UNIX user account. The username does not need to match your Windows username.
For more information visit: https://aka.ms/wslusers
Enter new UNIX username:

Diagnostic Logs
Follow-up issue for #10851

WslLogs-2024-12-05_13-30-01.zip

This was a Windows issue that has since been fixed. You should update your Windows install and this issue should go away.


File path slashes not working in Windows

Describe the bug
When in Windows, the resulting file path in the Edit Variable preview is missing all folder slashes in Windows.
Example: "C:\Users\someuser\secret_file.env" becomes "C:Userssomeusersecret_file.env"
This causes an error: "ENOENT: no such file or directory, open 'C:Userssomeusersecret_file.env'"
To Reproduce
Steps to reproduce the behavior:

Add environment variable in Base Environment
Tie variable to dotenv file
Note that it outputs the correct value from dotenv file when in Manage Environments
Close Manage Environments
Attempt to use variable in a request (it doesn't seem to matter where)
See the error in preview "ENOENT: no such file or directory, open [file location without slashes]"

Expected behavior
I would expect the Edit Variable to pull in the value of the variable with no error.
Screenshots

Desktop (please complete the following information):

OS: Windows 10 (Windows_NT x64 10.0.19044) & 11
Insomnia Version: 2023.5.0
Insomnia Build date: 8/14/2023
Electron: 25.2.0
Node: 18.15.0
Node ABI: 116
V8: <IP_ADDRESS>-electron.0
Architecture: x64

Additional context
I notice that the error is different if you use a dotenv in a Sub Environment. It doesn't bother to output the file path at all.
Also, I've tried the same project and same file in Linux, and this plugin works fine.

I can confirm, this has impacted me as well on Windows.

Seems that this repo is not maintained anymore.
I have created a fork of this plugin and fixed the issue in the plugin. Unfortunately, it seems that Insomnia has a general issue with escaping. So I changed the type of the .env file parameter to a string, to be able to enter an adapted path with an own escape sequence. For Windows instead of backslashses forward slashes must be used. And these forward slashes must be escaped with a backslash.
Forked plugin: https://www.npmjs.com/package/insomnia-plugin-fpf-dotenv (installation via npm package insomnia-plugin-fpf-dotenv).

@pfefferf Thank you for raising this and for your patience. I haven't been able to respond for a while.
But I can also confirm that this problem is no longer in the latest version (10.1.0)
I will close this issue for now, but happy to look at it again if the problem persists.

Hi
Version: Insomnia 10.1.1
Build date: 24/10/2024
OS: Windows_NT x64 10.0.22631
Electron: 31.4.0
Node: 20.16.0
Node ABI: 125
V8: <IP_ADDRESS>-electron.0
Architecture: x64
Plugin ver.: 1.2.0
we can confirm that this still happens. Just installed this plugin, so please bear with us. What additional info can we provide?
Edit tag dialog, for a variable in base environment:

Variable used in Auth tab of a folder:

Edit variable dialog. Actual path is "D:\Temp\.env.development":


Validate the experiment YAML

We should validate the YAML to make sure the user has provided a validate schema. Also, it would be nice to have custom types using something like kwalify where we could even validate the Metadata. This would be particularly useful for something like Matlab support and finding the correct Matlab binary path!

Added as of 9a10643747.


Add unused imports linter to engine presubmit

These are frequently caught by internal infra during rolls. We should run an unused imports linter in the engine as part of android presubmit.

I think I'm going to close this as a dupe of flutter/flutter#19031.

Actually my mistake, this is really a duplicate of #42534. Just using google-java-format would remove unused imports. I think the linter issue I mentioned earlier should be closed.


[HUDI-5637] Add Kryo for hive sync

Change Logs
After https://github.com/apache/hudi/commit/a70355f44571036d7f99b3ca3cb240674bd1cf91 kryo was added explicitly shaded in a few bundles. But, it missed hudi-hive-sync-bundle. Due to that, hive sync using run_sync_tool woild fail due to java.lang.NoClassDefFoundError: com/esotericsoftware/kryo/KryoSerializable. This PR fixes it. Note that we need to add explicitly in the bundle pom because in the parent pom kryo-shaded is declared in provided scope.
Impact
Fix hive sync.
Risk level (write none, low medium or high below)
low
Documentation Update
Describe any necessary documentation update if there is any new feature, config, or user-facing change

The config description must be updated if new configs are added or the default value of the configs are changed
Any new feature or user-facing change requires updating the Hudi website. Please create a Jira ticket, attach the
ticket number here and follow the instruction to make
changes to the website.

Contributor's checklist

[ ] Read through contributor's guide
[ ] Change Logs and Impact were stated clearly
[ ] Adequate tests were added if applicable
[ ] CI passed

@alexeykudinkin @codope don't we want to include kryo in aws, gcp, datahub-sync, and timeline-server bundles too? these all could run as standalone app

Again it is deltastreamer multiwriter testUpsertsContinuousModeWithMultipleWritersForConflicts which is failing. It's known to be flaky and under investigation. I am going to land this PR without wasting another CI cycle.

@alexeykudinkin @codope don't we want to include kryo in aws, gcp, datahub-sync, and timeline-server bundles too? these all could run as standalone app. In #7702, we mainly want to exclude kryo from spark and utilities bundles, right?

I verified hudi-aws-bundle by running glue sync in standalone mode. I was able to run the sync and query table through Athena. This suggests that AwsGlueCatalogSyncTool is not touching HoodieRecord which is where KryoSerializable is needed. That said, we should test other bundles and put up a fix if needed.

@xushiyan we should not add it to bundles that are being mixed in w/ bundles that are NOT shading (Spark)