ForwardDiff cannot determine ordering of Dual tags for Laplace distributions

I asked about my problem here: https://discourse.julialang.org/t/forwarddiff-cannot-determine-ordering-of-dual-tags/40345 and was directed to open an issue at Turing.
Here’s my MWE
@model function mwe(::Type{T} = Vector{Float64}) where {T}

    lvar ~ Uniform(5.0,15.0)

    u ~ filldist(truncated(Laplace(55.0, lvar), 10, Inf),10)

    return u

end

mwemodel = mwe()

chainmwe = sample(mwemodel, NUTS(.65),3)

The error is:
Cannot determine ordering of Dual tags ForwardDiff.Tag{Turing.Core.var"#f#7"{DynamicPPL.VarInfo{NamedTuple{(:lvar, :u),Tuple{DynamicPPL.Metadata{Dict{DynamicPPL.VarName{:lvar,Tuple{}},Int64},Array{Uniform{Float64},1},Array{DynamicPPL.VarName{:lvar,Tuple{}},1},Array{Float64,1},Array{Set{DynamicPPL.Selector},1}},DynamicPPL.Metadata{Dict{DynamicPPL.VarName{:u,Tuple{}},Int64},Array{Product{Continuous,Truncated{Laplace{Float64},Continuous,Float64},FillArrays.Fill{Truncated{Laplace{Float64},Continuous,Float64},1,Tuple{Base.OneTo{Int64}}}},1},Array{DynamicPPL.VarName{:u,Tuple{}},1},Array{Float64,1},Array{Set{DynamicPPL.Selector},1}}}},Float64},DynamicPPL.Model{var"###evaluator#333",(:T,),Tuple{Type{Array{Float64,1}}},(),DynamicPPL.ModelGen{var"###generator#334",(:T,),(:T,),Tuple{Type{Array{Float64,1}}}}},DynamicPPL.Sampler{NUTS{Turing.Core.ForwardDiffAD{40},(),AdvancedHMC.DiagEuclideanMetric},Turing.Inference.SamplerState{DynamicPPL.VarInfo{NamedTuple{(:lvar, :u),Tuple{DynamicPPL.Metadata{Dict{DynamicPPL.VarName{:lvar,Tuple{}},Int64},Array{Uniform{Float64},1},Array{DynamicPPL.VarName{:lvar,Tuple{}},1},Array{Float64,1},Array{Set{DynamicPPL.Selector},1}},DynamicPPL.Metadata{Dict{DynamicPPL.VarName{:u,Tuple{}},Int64},Array{Product{Continuous,Truncated{Laplace{Float64},Continuous,Float64},FillArrays.Fill{Truncated{Laplace{Float64},Continuous,Float64},1,Tuple{Base.OneTo{Int64}}}},1},Array{DynamicPPL.VarName{:u,Tuple{}},1},Array{Float64,1},Array{Set{DynamicPPL.Selector},1}}}},Float64}}}},Float64} and Nothing
...

and goes on for more characters than allowed in the body here. Not able to figure this out! Why isn’t this working?

You can avoid the issue by defining your model as
@model function mwe()
    lvar ~ Uniform(5.0,15.0)
    T = typeof(lvar)
    u ~ filldist(truncated(Laplace(55.0, lvar), T(10.0), T(Inf)), 10)
    return u
end

(Note that there is no reason to specify the input argument ::Type{T} since you have access to the type of lvar inside the function definition, and you didn't actually use T in your original version.) The underlying problem is the comparison in https://github.com/JuliaStats/Distributions.jl/blob/feae3ba9ada360d2980c901e7c737ca02bda7a31/src/truncate.jl#L116. Truncated normal distributions are handled differently and don't end up there, but for Laplace distributions Distributions falls back to Truncated. The comparison in this line errors if d.lower and d.upper are regular numbers but x is a dual number (i.e., lvar in your case). So the issue should be fixed properly upstream in Distributions, but hopefully for the time being the fix is helpful.

I think the proper fix upstream is just adding
Base.eltype(::Type{Laplace{T}}) where {T} = T

Then https://github.com/JuliaStats/Distributions.jl/blob/feae3ba9ada360d2980c901e7c737ca02bda7a31/src/truncate.jl#L25 should guarantee that the lower and upper bound of the truncated distribution are also dual numbers (as in the manual fix mentioned above). I'll quickly check if my intuition is correct.

I can confirm that defining eltype of Laplace fixes the problem.

Thanks for the replies! I could get around the problem by typecasting the limits to truncated as suggested.


Pigar won't run unless an unrelated directory exists

This problem has persisted even after uninstalling and reinstalling pigar.
The (slightly modified) output is below, when running pigar from my_project root.
$ python -m pigar -p requirements.txt -P my_project
Traceback (most recent call last):
  File "/Users/redacted/.pyenv/versions/3.8.6/bin/pigar", line 8, in <module>
    sys.exit(main())
  File "/Users/redacted/.pyenv/versions/3.8.6/lib/python3.8/site-packages/pigar/__main__.py", line 167, in main
    RequirementsGenerator(
  File "/Users/redacted/.pyenv/versions/3.8.6/lib/python3.8/site-packages/pigar/core.py", line 66, in generate
    self._package_root, self._ignores, self.installed_pkgs
  File "/Users/redacted/.pyenv/versions/3.8.6/lib/python3.8/site-packages/pigar/core.py", line 61, in installed_pkgs
    self._installed_pkgs = parse_installed_packages()
  File "/Users/redacted/.pyenv/versions/3.8.6/lib/python3.8/site-packages/pigar/parser.py", line 265, in parse_installed_packages
    mapping.update(_search_path(path))
  File "/Users/redacted/.pyenv/versions/3.8.6/lib/python3.8/site-packages/pigar/parser.py", line 305, in _search_path
    _file for _file in os.listdir(dev_dir)
FileNotFoundError: [Errno 2] No such file or directory: '/Users/redacted/redacted/another-repo'

I don't even know if I used it on another-repo. If I make an empty directory called another-repo in the desired path, it runs as desired, but obviously it's weird to have to keep an empty directory just sitting around.
If it matters, another-repo and my_project are sibling directories. This problem also persists across all of my projects, anywhere on my computer, not just this one case.

I have exactly the same problem.

Same here

Maybe because you have installed another-repo before?
What does this output?
python -c 'import sys, os;[list(map(lambda x: print(os.path.join(path, x)), os.listdir(path))) for path in sys.path if (os.path.isdir(path) and path.endswith(("site-packages", "dist-packages")))]' | grep 'link'

Checkout this version:
pip install git+https://github.com/damnever/pigar.git@main --upgrade


SparseSoftmaxCrossEntropyWithLogits error while calculating gradient in c++ mode

I'm trying to use the C++ API to train a CNN model. My last layer is using SparseSoftmaxCrossEntropyWithLogits. Here is the python code which generates the prototxt of the model:
import tensorflow as tf
from tensorflow.python.framework import ops
from tensorflow.python.framework import dtypes

import random  import numpy as np

NUM_CLASSES = 102  IMAGE_HEIGHT = 224  IMAGE_WIDTH = 224  BATCH_SIZE = 25  NUM_CHANNELS = 3  LEARNING_RATE = 0.0001

with tf.Session() as sess:

images_placeholder = tf.placeholder (tf.float32,
                                                shape=(BATCH_SIZE, IMAGE_HEIGHT,
                                                IMAGE_WIDTH, NUM_CHANNELS), name="input")
labels_placeholder = tf.placeholder (tf.float32,
                                                shape=(BATCH_SIZE), name="label")

    with tf.name_scope("conv1_1") as scope:
                 kernel = tf.Variable (tf.truncated_normal([3, 3, 3, 64], dtype=tf.float32, stddev=1e-2),
                                                  name="weights")
                 conv = tf.nn.conv2d (images_placeholder, kernel, [1, 1, 1, 1], padding='SAME')
                 biases = tf.Variable (tf.constant(0.0, shape=[64], dtype=tf.float32),
                                                  trainable=True, name='biases')
                 out = tf.nn.bias_add (conv, biases)
                 conv1_1 = tf.nn.relu (out, name=scope)

    pool1 = tf.nn.max_pool (conv1_1,
                            ksize=[1, 2, 2, 1],
                            strides=[1, 2, 2, 1],
                            padding='SAME',
                            name='pool1')

    with tf.name_scope('conv2_1') as scope:
                 kernel = tf.Variable (tf.truncated_normal([3, 3, 64, 128], dtype=tf.float32, stddev=1e-2),
                                                  name='weights')
                 conv = tf.nn.conv2d (pool1, kernel, [1, 1, 1, 1], padding='SAME')
                 biases = tf.Variable (tf.constant(0.0, shape=[128], dtype=tf.float32),
                                                  trainable=True, name='biases')
                 out = tf.nn.bias_add (conv, biases)
                 conv2_1 = tf.nn.relu (out, name=scope)

    pool2 = tf.nn.max_pool (conv2_1,
                            ksize=[1, 2, 2, 1],
                            strides=[1, 2, 2, 1],
                            padding='SAME',
                            name='pool2')

    with tf.name_scope('conv3_1') as scope:
                 kernel = tf.Variable(tf.truncated_normal([3, 3, 128, 256], dtype=tf.float32, stddev=1e-2),
                                                name='weights')
                 conv = tf.nn.conv2d(pool2, kernel, [1, 1, 1, 1], padding='SAME')
                 biases = tf.Variable(tf.constant(0.0, shape=[256], dtype=tf.float32),
                                                 trainable=True, name='biases')
                 out = tf.nn.bias_add(conv, biases)
                 conv3_1 = tf.nn.relu(out, name=scope)

    pool3 = tf.nn.max_pool (conv3_1,
                            ksize=[1, 2, 2, 1],
                            strides=[1, 2, 2, 1],
                            padding='SAME',
                            name='pool3')

    with tf.name_scope('conv4_1') as scope:
                 kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 512], dtype=tf.float32, stddev=1e-2),
                                                name='weights')
                conv = tf.nn.conv2d(pool3, kernel, [1, 1, 1, 1], padding='SAME')
                biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32),
                                                trainable=True, name='biases')
                out = tf.nn.bias_add(conv, biases)
                conv4_1 = tf.nn.relu(out, name=scope)

    pool4 = tf.nn.max_pool (conv4_1,
                            ksize=[1, 2, 2, 1],
                            strides=[1, 2, 2, 1],
                            padding='SAME',
                            name='pool4')

    with tf.name_scope('mentee_conv5_1') as scope:
                 kernel = tf.Variable(tf.truncated_normal([3, 3, 512, 512], dtype=tf.float32, stddev=1e-2),
                                                name='weights')
                 conv = tf.nn.conv2d(pool4, kernel, [1, 1, 1, 1], padding='SAME')
                 biases = tf.Variable(tf.constant(0.0, shape=[512], dtype=tf.float32), trainable=True,
                                                name='biases')
                 out = tf.nn.bias_add(conv, biases)
                 conv5_1 = tf.nn.relu(out, name=scope)

    pool5 = tf.nn.max_pool (conv5_1,
                            ksize=[1, 2, 2, 1],
                            strides=[1, 2, 2, 1],
                            padding='SAME',
                            name='pool5')

    with tf.name_scope('fc1') as scope:
                 shape = int(np.prod(pool5.get_shape()[1:]))
                 fc1w = tf.Variable(tf.truncated_normal([shape, 4096], dtype=tf.float32,
                                             stddev=1e-2), name='weights')
                 fc1b = tf.Variable(tf.constant(1.0, shape=[4096], dtype=tf.float32),
                                             trainable=True, name='biases')
                 pool5_flat = tf.reshape(pool5, [-1, shape])
                 fc1l = tf.nn.bias_add(tf.matmul(pool5_flat, fc1w), fc1b)
                 fc1 = tf.nn.relu(fc1l)
                 fc1 = tf.nn.dropout(fc1, 0.5)

    labels = tf.to_int64(labels_placeholder)
    cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits (labels=labels,
                        logits=fc1, name="xentropy")
    loss = tf.reduce_mean (cross_entropy, name='loss')

    optimizer = tf.train.AdamOptimizer (LEARNING_RATE)
    global_step = tf.Variable (0, name='global_step', trainable=False)
    train_op = optimizer.minimize (loss, global_step=global_step, name="train")

    init = tf.initialize_variables (tf.all_variables(), name='init_all_vars_op')
    tf.train.write_graph (sess.graph_def, "models/", "graph.pb", as_text=False)

Unfortunately when I call the c++ code to run the train_op node, it'll throw below error:
E tensorflow/core/common_runtime/executor.cc:594] Executor failed to create kernel. Invalid argument: NodeDef mentions attr 'message' not in Op<name=PreventGradient; signature=input:T -> output:T; attr=T:type>; NodeDef: gradients/xentropy/xentropy_grad/PreventGradient = PreventGradient[T=DT_FLOAT, message="Currently there is no way to take the second derivative of sparse_softmax_cross_entropy_with_logits due to the fused implementation\'s interaction with tf.gradients()", _device="/job:localhost/replica:0/task:0/cpu:0"](xentropy/xentropy:1)
I'm still confused whether the error comes from a bug inside the TF code or not.

Does it work if you train from within Python?
@skye, do you expect that a generated graphdef should be runnable for training in c++?

Basically yes. I have seen C++ examples (even I tried it myself) that can train a graph. To me it seems like gradient calculation functions generally does work on C++ mode. But still based on the description, that gradient ops are only available in python, I'm not sure how they are being ported from python to C++, when we wrap the whole TF core as a shared object library.

Some gradients certainly are not available, but you are generating the graph first, and the weird thing is it is remarking about a 2nd derivate. Which suggests you model is double differentiating somewhere or there is a bug in the C++ layer.

Alright, so here I have one more question. If I have a model with specific operators, which gradients have not been implemented in C++ version, does it mean that during training it's gonna fail? Or it's silently continue execution with no update on the variables? I'm asking this because I'm trying to validate how much using C++ interface is practical for training models!

If the gradient is not implemented in C++, we don't register a kernel, so it will definitely give you a "no kernel found" type error.

@saman-aghazadeh Could you please share the sources from where you could create a graph in c++
I am trying to create a graph in c++ from python code available at https://github.com/wolfib/image-classification-CIFAR10-tf/blob/master/softmax.py but unable to do so as I coudn't find few of the python tensorflow function in C++ , for Eg tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=labels_placeholder))

It has been 14 days with no activity and the awaiting response label was assigned. Is this still an issue? Please update the label and/or status accordingly.

Nagging Awaiting Response: It has been 14 days with no activityand the awaiting response label was assigned. Is this still an issue?

Nagging Awaiting Response: It has been 14 days with no activityand the awaiting response label was assigned. Is this still an issue?

Nagging Awaiting Response: It has been 14 days with no activityand the awaiting response label was assigned. Is this still an issue?

It has been 14 days with no activity and the awaiting response label was assigned. Is this still an issue?

It has been 201 days with no activity and the awaiting response label was assigned. Is this still an issue?

We are closing this issue for now due to lack of activity. Please comment if this is still an issue for you. Thanks!

This issue is reproducible with the python API as well.
In my case, I have added an additional term to the loss function (gradient of the cross entropy with respect to the input). In this case, second derivative of sparse_softmax_cross_entropy_with_logits needs to be computed during backprop and the following error is thrown:
LookupError: Gradient explicitly disabled. Reason: b"Currently there is no way to take the second derivative of sparse_softmax_cross_entropy_with_logits due to the fused implementation's interaction with tf.gradients()"
It is surprising that support for second order derivative is still missing for some of the key operations in TF.

It has been 14 days with no activity and the awaiting response label was assigned. Is this still an issue?

@tensorflowbutler Yes, this is still an issue.

Nagging Assignees @skye, @aselle: It has been 14 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

Nagging Assignees @skye, @aselle: It has been 29 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

I am getting this error in Python as well, similar to @vipinpillai. Is there any update on this issue? Is there any short-term workaround for it?

@mmkamani7 Can you check with recent TF versions (TF1.14.0 or TF-nightly, 'TF 2.0.0rc0`) and let us know whether it was resolved or not. There were several improvements in the last couple of months. Thanks!

Closing due to lack of recent activity. Please update the issue when new information becomes available, and we will reopen the issue. Thanks!

When I use jupyter with tf2.0beta, there has no problem. But when I use tf2.0.0, the error 'LookupError: Gradient explicitly disabled. ' appears.

When I use jupyter with tf2.0beta, there has no problem. But when I use tf2.0.0, the error 'LookupError: Gradient explicitly disabled. ' appears.

Did you have solved this problem?,I met the same error that is "LookupError: Gradient explicitly disabled. Reason: b"Currently there is no way to take the second derivative of sparse_softmax_cross_entropy_with_logits due to the fused implementation's interaction with tf.gradients()""

This issue is reproducible with the python API as well.
In my case, I have added an additional term to the loss function (gradient of the cross entropy with respect to the input). In this case, second derivative of sparse_softmax_cross_entropy_with_logits needs to be computed during backprop and the following error is thrown:
LookupError: Gradient explicitly disabled. Reason: b"Currently there is no way to take the second derivative of sparse_softmax_cross_entropy_with_logits due to the fused implementation's interaction with tf.gradients()"
It is surprising that support for second order derivative is still missing for some of the key operations in TF.

Excuse me, can you tell me how to deal with this error? Look forward to you reply, thanks


[BUG] cds build plugin code is missing in npm package

Is there an existing issue for this?

[X] I have searched the existing issues

Nature of Your Project
JavaScript, TypeScript
Current Behavior
Running cds build --for typescriptresults in
[ERROR] No provider found for build task 'typescript'. Ensure that all required dependencies have been added and 'npm install' has been executed.

Expected Behavior
cds-typer typescript plugin should be found and be executable.
Steps To Reproduce
No response
Environment
- **OS**: Windows 10
- **Node**: v20.13.1
- **npm**: 10.8.0
- **cds-typer**: 0.21.0
- **cds**: 7.9.1

Repository Containing a Minimal Reproducible Example
No response
Anything else?
It seems that cds-plugin.js is missing in npm package:

Hi Daniel,
great catch! I will release a patch for this asap.
Best,
Daniel


Issue casting string to literal with custom datatype using *

As found in the docs I tried to create a xsd:date literal by running:
let lit_date = "2023-11-15" * xsd::date;

But I am getting this error from the compiler:

mismatched types
expected struct sophia::sophia_api::term::LanguageTag<&str>
found struct sophia::sophia_api::ns::NsTerm<'static>

I also tried to do this from within the sophia_rs project (by cloning it, and doing this datatype casting somewhere in the tests), and it did not throw the error.
I am not sure if there is something missing in the code (maybe a trait that needs to be public?), or if I am missing something in my code (like an import?)

This is currently only available from the github version; it is not included on the latest released version on crates.io. Sorry for the confusion.


Drop cargo update handling from CI

This makes sense in lalrpop, where we ship a Cargo.lock, but also a library, but here with only a library and no shipped Cargo.lock, our testing will just end up with the latest semver compatible dependency versions regardless of whether we run cargo update first or not.

I recently learned that the guidance for Cargo.lock files for libraries has been updated.
The main concern is around MSRV I think? Plus random unintended breakages. https://github.com/rust-lang/cargo/pull/12382 https://doc.rust-lang.org/nightly/cargo/faq.html#why-have-cargolock-in-version-control

Ah, so to be clear, I'm not advocating for any changes at this time. I remember this crate and Lalrpop relying on cargo's guidance on lockfiles previously and I want to keep this updated guidance in mind going forward.

As far as I can tell, reading the PR and updated guidance, it seems like the purported benefits of checking in Cargo.lock are all for the project maintainers and contributors, rather than for project users. Users still get the latest semver compatible transitive dependencies, with feature resolution ignoring our Cargo.lock. So the decision to check in a Cargo.lock is ultimately about project development having stable CI, better git bisect etc. Does that match your understanding?

This is my understanding as well. It's worth noting atleast 2 things that have changed over time. The rise in MSRV support and usage in the Rust ecosystem which has gotten better tooling from cargo but is still rather inconsistent in terms of peoples usage/upgrading/breakage. Additionally the rise in workflow automation support(like Dependabot) for Rust which automates the tracking and maintenance of lockfiles in repositories.

All that said, in this particular case, with lalrpop as the only user of this project, and lalrpop having a committed lockfile, maybe the user breakage benefit is pretty minor, since lalrpop will test both the locked and fully updated versions in its CI regardless. But on the other side, we don't have external contributors to get confused by broken CI, and I doubt we're going to spend a ton of time git bisecting issues in this project. So neither way has a strong case. My vote is leave it as is, but I'm open to other opinions if you disagree

I think our current usage of lockfiles is judicious and a good place to be in. But what I think the trend is towards having lockfiles and automatically keeping them up to date so that there is no distinction/gap between our locked versions and fully updated versions. The main change/possible benefit for users is us front-running our users in terms of version updates. This way, if there is an unintended breakage, we clearly see it in ci and can see if we need to patch. This alludes to the idea mentioned in one of the links of a distributed crater run in the ecosystem.
Basically, lets continue as we are. In the future, we may set up a lockfile here or a more aggressive lockfile updating workflow in lalrpop if this comes up in practice.


run and test sample using windows environment

Hi,
I needed help on running and testing this application in windows operating system. I'm totally not sure what to do first and not knowing anything about python. Is there someone would explain me steps on running this apps. Thanks in advance

hi, do find the  solution ? I look for this solution


fix(nuxt): fix ssr log querySelector

🔗 Linked issue
fix https://github.com/nuxt/nuxt/issues/28859

📚 Description
This PR fixes ssr logs not being logged in console

❤


[Bug]: "There are uncommitted changes" in eval time travel for swe-bench when specifying "main"

Is there an existing issue for the same bug?

[X] I have checked the troubleshooting document at https://opendevin.github.io/OpenDevin/modules/usage/troubleshooting
[X] I have checked the existing issues.

Describe the bug
When I run evaluation for swe-bench with the following command, I get the following error:
$ bash ./evaluation/swe_bench/scripts/run_infer.sh eval_${LLM_MODEL} main
Start to checkout opendevin version to main, but keep current evaluation harness
There are uncommitted changes, please stash or commit them first

I was a bit confused by this, as I would've expected this to work. @li-boxuan I don't know if you have an idea of what might be going wrong here?
Current OpenDevin version
3d33bc68137cb408f13dc937e0ca3051a2521142

Installation and Configuration
make build

Model and Agent
No response
Operating System
No response
Reproduction Steps
No response
Logs, Errors, Screenshots, and Additional Context
No response

It turns out that this was because npm had updated a dependency and frontend/package-lock had been modified.
Running git reset --hard HEAD fixed the issue.


Informational tasks

[x] Update _gallery (put proper image name, title and subtitle)
[x] Update _pages (be careful of indentation when putting html)
[x] Update images and put proper names, make sure that if the names are changed you change it in all places in the site folder
[x] Update logo.svg to a logo of your choice
[x] Music clip embeds
[x] Make sure images are not super high definition (it will slow down the website)
[x] Make work videos aligned properly (they are currently all aligned on the left)
[ ] Put proper title for images
[x] Resize images so that webpage loads faster

[ ] Make work videos aligned properly (they are currently all aligned on the left)
[ ] Put proper title to images
[ ] Resize images so that webpage loads faster

[ ] remove title from gallery conditionally
[ ] resume download pops up in a new tab
[ ] change background colour of the website https://www.thesitewizard.com/css/background-colour.shtml
[ ] order the social buttons as gmail, insta, youtube (right side and bottom)


Inaccurate

Hi there,
I have tried using your library on the humble branch, but it appears to be inaccurate by about 20cm.
I am using a ZED2i camera, and giving the rectified image of the right camera lens as input.
FYI when I use the zed_aruco_localization example package, it gives detections with +- 1cm error.
Is there something I should tune?
I am using 6X6_50 dictionary.

when using rectified images, make sure to set image_is_rectified parameter to true. I forgot to add this parameter to example config. Let me know if it fixes the accuraccy.

Ah I have already set that parameter

Is the offset from the actual position stable? Are the axes properly aligned with the marker on /aruco_tracker/debug images?

The offset is stable.
z-axis:

detected offset: ~0.15m

x-axis:

detected offset: ~0.1m

y-axis:

detected offset: ~0.01m

The axes seem aligned

.

How are you estimating the error?
Could you try opening RViz, adding TF display and Camera display and checking if the axes are correctly overlayed on the camera images?

I am estimating the error by measuring with a tape measure the ground truth, and then looking at the output of /aruco_opencv/aruco_detections (the z-axis mainly for now)
In this example, it is off by 10cm.
The axes look to be correctly overlayed.

Also the camera appears to be perfectly level horizontally:

It also does not seem to make a difference if I use rectified or un-rectified images.

I also noticed that zed_aruco_localization have a refine parameter that I think uses the corners of the marker to improve detection. I noticed that when I turn this parameter off, I get pretty much the same result as ros_aruco_opencv, and when it is turned on I get an error of only ~3cm.

Ah, I have found that setting this parameter to "1" (for subpixel mode) makes it accurate for my example to ~1.5cm !

Ah, I have found that setting this parameter to "1" (for subpixel mode) makes it accurate for my example to ~1.5cm !

That's interesting! I used the contour corner refinement as it resulted in more stable positions and was (I think) faster. I never actually measured the pose accuracy though. If you are using subpixel refinement, you can also experiment with cornerRefinementWinSize, cornerRefinementMaxIterations and cornerRefinementMinAccuracy parameters as they are applicable in this case.

But for distances at around 2m, I am now getting ~10cm error. But increasing the resolution to 1080 reduces the error back to ~2cm. So I guess it is more or less fine tuning parameters.

This package just passes the images and detection parameters to OpenCV library that's installed on the system, so I don't have much influence on the algorithms it uses. Though, with enough feedback, I might come up with better default detection parameters.


add emoji to log level name for iOS

because we can't tweak Xcode 8 to use color in console
https://github.com/robbiehanson/XcodeColors/issues/88

This seems like something an individual logging implementation should do, not the base log crate. This will also break parsing since LOG_LEVEL_NAMES is used for that as well.

@sfackler Yeah, agree.
please see also https://github.com/sfackler/log4rs/issues/44 😄

Updated, only add method to get emoji
Not sure if this should be in this base log crate,
but I think it may be useful for multiple log implementations
Usage example maybe like this:
https://github.com/sfackler/log4rs/pull/45

How standard are these choices of emoji?

It is compatible with https://github.com/SwiftyBeaver/SwiftyBeaver

This doesn't need to be provided in log. I agree with @sfackler that individual logging implementations can select emojis if they want.
As far as I can tell there isn't a standard but :x::warning::information_source: is probably the most widely recognizable set and works way better in monochrome.

Just a heads up, if you use this in your logging in Xcode you'll get some pretty buggy behaviors trying to use debugger commands as well as some pretty serious performance hits on the logging since Xcode's console text layout engine doesn't seem to like rendering emoji with fixed width fonts.
One bug that's really annoying with this is the autocomplete menus keep flickering and disappearing when using emojis in logging statements.
We ended up removing them from our implementation it was so bad, but YMMV.


Made rounding in convert_image_dtype for numbers close to zero

Fixes #48701

@googlebot I fixed it.

@googlebot I fixed it.

@pointhex can you please check build failures ?

@pointhex can you please check build failures ?

Hi, I will try to do it by the end of the next week. I have and will have a bit busy week. is it ok?

Hi @kkimdev,
I see that tests, that I've broked, are fixed now.
But for Linux GPU link is broken for me.
About Windows Bazel GPU, looks like some infrastructure error.
Could you check plz or tell me what I should do with that?

Here are the internal errors, @pointhex can you please verify ? Thanks!
Traceback (most recent call last):
File "/tensorflow/python/ops/image_ops_test.py", line 4648, in testConvertBetweenFloat32AndInt8SmallNumbers
self.assertEqual(x0.numpy(), 0)
File "/tensorflow/python/framework/ops.py", line 402, in getattr
self.getattribute(name)
AttributeError: 'Tensor' object has no attribute 'numpy'

Here are the internal errors, @pointhex can you please verify ? Thanks!
Traceback (most recent call last):
File "/tensorflow/python/ops/image_ops_test.py", line 4648, in testConvertBetweenFloat32AndInt8SmallNumbers
self.assertEqual(x0.numpy(), 0)
File "/tensorflow/python/framework/ops.py", line 402, in getattr
self.getattribute(name)
AttributeError: 'Tensor' object has no attribute 'numpy'

Hi @gbaned,
I did it another way. Hope it will help.

Here are the internal errors, @pointhex can you please verify ? also can you please address  pylint errors ? Thanks!
Traceback (most recent call last):
File "/tensorflow/python/ops/image_ops_test.py", line 918, in testHalfSaturation
self.assertAllEqual(y_tf, y_np)
File "/tensorflow/python/framework/test_util.py", line 1275, in decorated
return f(*args, **kwds)
File "/tensorflow/python/framework/test_util.py", line 2976, in assertAllEqual
np.testing.assert_array_equal(a, b, err_msg="\n".join(msgs))
File "/py/numpy/testing/_private/utils.py", line 931, in assert_array_equal
verbose=verbose, header='Arrays are not equal')
File "/py/numpy/testing/_private/utils.py", line 840, in assert_array_compare
raise AssertionError(msg)
AssertionError:
Arrays are not equal
not equal where = (array([0, 1]), array([1, 0]), array([1, 0]))
not equal lhs = array([180, 136], dtype=uint8)
not equal rhs = array([181, 135], dtype=uint8)
Mismatched elements: 2 / 12 (16.7%)
Max absolute difference: 255
Max relative difference: 1.40883978
x: array([[[  7,   9,  13],
[140, 180, 226]],
...
y: array([[[  7,   9,  13],
[140, 181, 226]],
...

Here are the internal errors, @pointhex can you please verify ? also can you please address pylint errors ? Thanks!
Traceback (most recent call last):
File "/tensorflow/python/ops/image_ops_test.py", line 918, in testHalfSaturation
self.assertAllEqual(y_tf, y_np)
File "/tensorflow/python/framework/test_util.py", line 1275, in decorated
return f(*args, **kwds)
File "/tensorflow/python/framework/test_util.py", line 2976, in assertAllEqual
np.testing.assert_array_equal(a, b, err_msg="\n".join(msgs))
File "/py/numpy/testing/_private/utils.py", line 931, in assert_array_equal
verbose=verbose, header='Arrays are not equal')
File "/py/numpy/testing/_private/utils.py", line 840, in assert_array_compare
raise AssertionError(msg)
AssertionError:
Arrays are not equal
not equal where = (array([0, 1]), array([1, 0]), array([1, 0]))
not equal lhs = array([180, 136], dtype=uint8)
not equal rhs = array([181, 135], dtype=uint8)
Mismatched elements: 2 / 12 (16.7%)
Max absolute difference: 255
Max relative difference: 1.40883978
x: array([[[ 7, 9, 13],
[140, 180, 226]],
...
y: array([[[ 7, 9, 13],
[140, 181, 226]],
...

The main problem is that I changed that for CPU from 180 to 181 but for some reason on GPU it computes as 180. @gbaned Could you tell me if you have some ideas on how I can fix that?

@kkimdev Can you please assist on above comments from @pointhex. Thanks!

I guess the test was affected by your convert_image_dtype change, could you try printing intermediate values in your changes using tf.print and compare CPU and GPU?

I guess the test was affected by your convert_image_dtype change, could you try printing intermediate values in your changes using tf.print and compare CPU and GPU?

I'm sure that it because of my change:) I will try to do it by print. But still, it strange a bit. Maybe, floating-point arithmetic shows some differences.

@pointhex  Any update on this PR? Please. Thanks!

@pointhex Any update on this PR? Please. Thanks!

Unfortunately, I don't have. I tried to set up a build on my machine for GPU. But it still doesn't work. I will be on vacation for 2 weeks. So this time I will not have time for work on this commit( So if it hot and can't wait for me. So someone can finish it.
I still think that it is some CPU and GPU computing problem.

@kkimdev Can you please assist on above comments from @pointhex. Thanks!

@kkimdev Can you please assist on above comments from @pointhex. Thanks!

I'm working on the problem. I managed to install cuda, but getting some errors during builds. Hope it will be solved. And I will check on my machine.

Check with cuda.
used two commands:
bazel test --config=cuda --repo_env=CC=clang-12  //tensorflow/python:image_ops_test
bazel test --repo_env=CC=clang-12  //tensorflow/python:image_ops_test
and now tests passes.
Is it right way?

Hi @kkimdev
I see that some builds are broken again, it looks like an infrastructure problem.
Could you please restart or check it?
Thank you!


[Autocomplete] Support ChipProps prop

[x] I have followed (at least) the PR section of the contributing guide.

Closes #18885

@ChrisWiles Thank you


Move most headers to a common headers struct

This change would move most headers that are received back from the Cosmos API to a CommonHeaders struct that can be deserialized. The thought behind this is that even for endpoints where it seems like certain headers always come back #1022 showed that this can change. We should probably be pretty lax about what headers we do accept. This allows us to centralize this decision making within Cosmos.
Thoughts? @ctaggart @bmc-msft

I like the unification for parsing into a central area when possible, though I wonder if it might be more usable if the headers were still available at the top level, rather than having to go through obj.common.  Instead of a nested object, perhaps this could be written as a macro that adds the headers to the struct and another that adds the parsing?

Closing this as we think about a better way to reduce the boiler plate here.


throwing "Unexpected EOF" in version 1.2

Where decompressing gzipped data, throws "Unexpected EOF" in InflaterINputStream.Fill() Line: 493
Test code:
byte[] oCompressed = oData.Compress(); // oData is a byte[],size: 415,164 bytes
byte[] oDecompressed = oCompressed.Decompress(); // throws exception
Compress/Decompress methods follow:
  static public byte[] Compress(this byte[] oValue)
  {
     if (oValue == null)
        return null;

     using (MemoryStream oClearStream = new MemoryStream(oValue))
     using (MemoryStream oCompresedStream = new MemoryStream())
     using (GZipOutputStream oGZipOutputStream = new GZipOutputStream(oCompresedStream, 1048576))
     {
        oClearStream.CopyTo(oGZipOutputStream, 1048576);
        byte[] oCompressed = oCompresedStream.ToArray();

        return oCompressed;
     }
  }

  static public byte[] Decompress(this byte[] oValue)
  {
     if (oValue == null)
        return null;

     using (MemoryStream oCompressedStream = new MemoryStream(oValue))
     using (MemoryStream oDecompresedStream = new MemoryStream())
     using (GZipInputStream oGZipInputStream = new GZipInputStream(oCompressedStream, 1048576))
     {
        oGZipInputStream.CopyTo(oDecompresedStream, 1048576);
        return oDecompresedStream.ToArray();
     }
  }

Expected behavior
It should be capable of decompressing anything it compresses.
Actual behavior
throw new SharpZipBaseException("Unexpected EOF");
Version of SharpZipLib
v1.2.0
Obtained from (only keep the relevant lines)

Downloaded from GitHub

I think you'll need to Finish() or Close/Dispose the GZipOutputStream before reading the data from oCompresedStream, otherwise it won't contain the footer information for the GZip stream (which will otherwise be written when the stream is disposed, which wil be after oCompressed is created).

That appears to be the problem, rookie mistake.
Thanks for your time.


BaseCollectiveExecuter::StartAbort Out of range: End of Sequence

Hi,
While training Tiny Yolo on VOC dataset, in the end of each epoch I am getting the error "BaseCollectiveExecuter::StartAbort Out of range: End of Sequence". Also training early stops after 4 epochs. The terminal outputs are attached. Note that I am using ubuntu 18.04 with TF2.0.
Also while using eager mode training, all went right.

I think it's logical to add the repeat() function while creating your training and validation dataset.

Hi @AnaRhisT94,
So while loading TFRecords, using dataset.map(....).repeat() will resolve this issue? Please guide thoroughly as I am new to Tensorflow and wanted to investigate in depth details.
Thanks

Hi @AnaRhisT94,
So while loading TFRecords, using dataset.map(....).repeat() will resolve this issue? Please guide thoroughly as I am new to Tensorflow and wanted to investigate in depth details.
Thanks

Best thing it to try and see. Should work though :)

Another option is switching from "fit" to "eager_tf".

@AnaRhisT94 Did adding the .repeat() resolve this for you? and have you been able to train a modal that did detects something (even inaccuratly)?

Hi @NiklasWilson ,
adding .repeat() and passing steps_per_epoch in model.fit() solves the issue for me. Because of the busy routine I did not able to test the model. will let you know about detection once I test the model.

@muhammad-maaz-confiz Awesome! I will add that to my code tonight and test myself too. While eagerly awaiting the results of tests :)

Side note this "early stops after 4 epochs" can happen with or without this error. It is related to this line of code.
            ReduceLROnPlateau(verbose=1),             EarlyStopping(patience=3, verbose=1),
You can modify the patience value to decrease the premature stopping.
I have change mine to
  ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1)     EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)

Thanks @NiklasWilson and @AnaRhisT94 ,
While repeating the dataset and adding steps_per_epoch in model.fit() seems to resolve the issue for initial epochs and the issue reappeared after a couple of epochs (3 epochs for me). What could be gone wrong? Also note that the training automatically stops after 9 epochs and no message regarding early stopping. Thanks

@muhammad-maaz-confiz
This problem went away for me when I put the repeat here
    train_dataset = train_dataset.map(lambda x, y: (         dataset.transform_images(x, FLAGS.size),         dataset.transform_targets(y, anchors, anchor_masks, 80))).repeat()

By repeating the training dataset and specifying steps_per_epoch and validation_steps in model.fit(), I am able to get rid of this error/warning.

I believe this error doesn't actually affect training, it's likely a bug from tensorflow


Youki fails when some unused capability is missing

I am trying to run youki in a container context (think Docker in Docker), and it fails to run if the container doesn't have all the capabilities enabled.
In particular, this also happens if some capabilities added in kernel 5.8 / 5.9 are not present (like CAP_BPF or CAP_CHECKPOINT_RESTORE). I haven't tested in a post-5.3 and pre-5.9 kernel, but I think it would hit the same issue.
Note that runc does work in the same situation.
Please see the attachment for reproduction steps: reproduction.zip

The issue is caused by reset_effective setting all the capabilities listed in caps::all(), which includes the capabilities added in kernel 5.8 / 5.9.
I don't understand why this is needed, in either in container_init_process or set_id.

@jprendes Thank you for the bug report. dind is a special case where we have not tested before. So I am not surprised that it does not work out of box. Also thank you for the PR, but I believe the reset capabilities has a purpose. It's been a while since I touched that part of the code, so I don't recall exactly the reason off the top of my head.
I believe the first step to address this issue is to set up an integration test like we do for containerd and k8s. Then we can start to explore how to consistently support the dind usecase.
In addition, it would be good to understand why runc works whereas we fail in this case.

The issue is caused by reset_effective setting all the capabilities listed in caps::all(), which includes the capabilities added in kernel 5.8 / 5.9.

This sounds like a bug to me. May be caps::all() is not the right choice here.

@yihuaf thanks for looking into this.
I've simplified the reproductions to not involve docker. Just plain youki and plain runc running inside a container.
reproduction.zip
Additionally, doing an strace -f --trace=capset ... on youki and runc, I see that youki 4 calls to capset:
capset({version=_LINUX_CAPABILITY_VERSION_3, pid=0}, {effective=ALL, permitted=ALL, inheritable=0}) = 0
capset({version=_LINUX_CAPABILITY_VERSION_3, pid=0}, {effective=1<<CAP_KILL|1<<CAP_NET_BIND_SERVICE|1<<CAP_AUDIT_WRITE, permitted=ALL, inheritable=0}) = 0
capset({version=_LINUX_CAPABILITY_VERSION_3, pid=0}, {effective=1<<CAP_KILL|1<<CAP_NET_BIND_SERVICE|1<<CAP_AUDIT_WRITE, permitted=1<<CAP_KILL|1<<CAP_NET_BIND_SERVICE|1<<CAP_AUDIT_WRITE, inheritable=0}) = 0
capset({version=_LINUX_CAPABILITY_VERSION_3, pid=0}, {effective=1<<CAP_KILL|1<<CAP_NET_BIND_SERVICE|1<<CAP_AUDIT_WRITE, permitted=1<<CAP_KILL|1<<CAP_NET_BIND_SERVICE|1<<CAP_AUDIT_WRITE, inheritable=1<<CAP_KILL|1<<CAP_NET_BIND_SERVICE|1<<CAP_AUDIT_WRITE}) = 0

while runc does only one call (the same as the last call from youki):
capset({version=_LINUX_CAPABILITY_VERSION_3, pid=0}, {effective=1<<CAP_KILL|1<<CAP_NET_BIND_SERVICE|1<<CAP_AUDIT_WRITE, permitted=1<<CAP_KILL|1<<CAP_NET_BIND_SERVICE|1<<CAP_AUDIT_WRITE, inheritable=1<<CAP_KILL|1<<CAP_NET_BIND_SERVICE|1<<CAP_AUDIT_WRITE}) = 0


Add workload profile to Container App Environment

Is there an existing issue for this?

[X] I have searched the existing issues

Community Note

Please vote on this issue by adding a :thumbsup: reaction to the original issue to help the community and maintainers prioritize this request
Please do not leave "+1" or "me too" comments, they generate extra noise for issue followers and do not help prioritize the request
If you are interested in working on this issue or have submitted a pull request, please leave a comment

Description
This module does not support container app environment workload profiles as defined in the AzureRM module:
A workload_profile block supports the following:
name - (Required) The name of the workload profile.
workload_profile_type - (Required) Workload profile type for the workloads to run on. Possible values include Consumption, D4, D8, D16, D32, E4, E8, E16 and E32.
maximum_count - (Required) The maximum number of instances of workload profile that can be deployed in the Container App Environment.
minimum_count - (Required) The minimum number of instances of workload profile that can be deployed in the Container App Environment.
Support should be added folloeing the AzureRM definition:
New or Affected Resource(s
azurerm_container_app_environments
Potential Configuration file
container_app_environments = {
  cae1 = {
    name               = "container app with profile"
    region             = "region1"
    resource_group_key = "rg_re1"

    workload_profile {
      name                  = "caetest"
      workload_profile_type = "D4"
      minimum_count         = 1
      maximum_count         = 2
    }
    tags = {
      environment = "testing"
    }
  }
}

References
No response

👍 We need this feature as soon as possible


Focus ring clipped by hover styles

          > Yeah, the dark theme styles will need to be fixed. I think it looks okay in light theme, though — minus one minor issue where the hover color clips the focus ring, which I was going to submit a separate PR for.

ah, yeah, I see the focus ring issue now. FWIW that happens on the version switcher too, please do fix both (in a separate PR is OK with me):

As for unifying the style of the nav links dropdown with the version switcher, I would only be a fan of doing that if it simplifies the code. Otherwise, I don't feel strongly that the styles for the two things need to be the same, since they are not semantically the same, as I mention in the PR description.

IDK if it will simplify the code, but I think it makes the UX better for mouse users. The More Dropdown currently has gutters where there is no hover effect and no effect of clicking; IMO that's not a good thing to have in a dropdown menu. I also think that it's OK for two things that are technically semantically different to have similar appearance given that their interaction mechanisms are the same --- in both cases you click (or press enter when focused) to reveal some options, navigate those options with mouse or arrow keys or TAB, select an option with click or enter, and abort by clicking outside the menu or hitting escape.
Originally posted by @drammock in https://github.com/pydata/pydata-sphinx-theme/issues/1414#issuecomment-1682844661

This should be fixed in #1564


Location List Issue - Vim 7.4, patches 1-1707

I am using the latest version of Vim, built from source, and the location list appears broken when attempting to use it to jump to a location of an error/warning returned from ESLint.
When I press enter on a line in the location list, my cursor just returns to the last position it was in the buffer before I jumped to the location list split.
I also get an error:
E926: Current location list was changed

Not sure if this is an issue Vim needs to fix, or if they changed something with how location lists are handled and Syntastic needs a fix.

:+1: http://superuser.com/questions/1059194/vim-syntastic-not-jumping-to-the-error
Something definitely changed with vim; old versions are working fine. Also lnext and lprev still work, just not selecting from the list.
I haven't seen any vim issues raised on it yet.

Yes, this behaviour was introduced by Vim 7.4.1592, but it isn't clear the bug is in Vim, or that it should be addressed there.  The patch 1592 is a bug fix, and it solves a real crash.  On the other hand, what syntastic does on the relevant code path is a mess, and it's definitely wrong in some scenarios.  I made several attempts over the years to fix this, but the limitations in Vim's API basically mean that fixing some scenario breaks others.  Until patch 1592 the broken scenarios were just less common.  I'll try again, but I'm not particularly confident the result is going to be substantially better this time. shrug

Duplicate of #1127.

Please try 7e986f1 and report any problems.  Segfaults might be on the plate too (well, hopefully not).

I am testing it out, so far it seems to work great, thanks!

Sorry for the late reply -- working flawlessly for me on vim 7.4.1707.

@lcd047 I'm getting this error when opening a file for the first time through the locationlist:
Error detected while processing function <SNR>114_BufEnterHook[5]..342[20]..269[2]..270[5]..330[2]..329:               line    8:                                                                                E121: Undefined variable: b:syntastic_changedtick

@luan Right, please upgrade to d9814c5.

Not fixed, see the end of #1127 for updates.


[swagger][Container Instance] Username is required even if user assigned identity is passed

Package Name: @azure/arm-containerinstance
Package Version: 7.1.0
Operating system: Linux x86-64
[x] nodejs

version: 17

[ ] browser

name/version:

[x] typescript

version: 4.6.2

Is the bug related to documentation in

[ ] README.md
[ ] source code documentation
[ ] SDK API docs on https://docs.microsoft.com

Describe the bug
Spawning a container group using client.containerGroups.createOrUpdate requires passing username in imageRegistryCredentials (type ImageRegistryCredential), but this field should not be required if identity is present.
To Reproduce
Try to call client.containerGroups.createOrUpdate with only an identity:
const principalPath = '/subscriptions/...';

await someContainerClient.containerGroups.createOrUpdate('some-group', 'some-name', {
    containers: containers,
    name: name,
    osType: 'Linux',
    sku: 'Standard',
    location: this.azureContainerInstanceConfig.instanceRegion,
    identity: {
        type: 'UserAssigned',
        userAssignedIdentities: {
            [principalPath]: {},
        },
    },
    imageRegistryCredentials: [
        {
            server: 'some-server',
            identity: principalPath,
        },
    ],
    restartPolicy: 'OnFailure'
})

The types will complain that username must be present.
Expected behavior
The types do not complain about username if identity is present, since the two are mutually exclusive.
Additional context
If you pass undefined as unknown as string to circumvent the error, the serializer will complain that username must not be null or undefined. If you set it to an actual value, Azure will complain that you cannot combine username with identity. The only workaround so far is to pass the empty string '' as username value - Azure seems to ignore it, and the SDK does not complain.

Label prediction was below confidence level 0.6 for Model:ServiceLabels: 'Container Registry:0.30219007,Container Instances:0.2790609,Azure.Identity:0.1704673'

@NoTuxNoBux Thanks for reporting this issue! We reproduced in local environment and we think this is a valid issue and are contacting the service team to fix it. Will let you know once we have a release to fix it!

@NoTuxNoBux We released a new version for this fix -<EMAIL_ADDRESS>Could you have a try?

@MaryGao Thanks for the update. I've created an internal ticket to do so, but it might take a while before I get to it. (If you prefer to close this ticket in the meantime, please feel free to do so, I can always respond later if it turns out not to be fixed, or create a new ticket.)

close it as it should have been resolved. @NoTuxNoBux feel free to reopen it if you still found any issues about it. Thanks


Match msg.sender to tx.origin during multicall

Allows reading contract data with restricted access

Proposed solution may break the functionality on some chains [source]
It's better to add possibility to configure 'from'.
I'm closing this PR.


About datasets

We are really interested in your wonderful work. If possible, could you please share your datasets with Baiduyun, because I don't have access to the datasets address you provided.

Hi CaineGu, thank you for remind me the expired link issue. I have updated it and it can work now.  It's on OneDrive. Please see if you can download it with reasonable speed. I am also trying to upload it to Bariduyun. Thank you again for your interest to our work.

Hi CaineGu, the link has been updated.

Hi, I can not find the dataset link for 3D-EPN or PCN now. Could you please share the download link?

Hi, I can not find the dataset link for 3D-EPN or PCN now. Could you please share the download link?

Thank you for bringing this to my attention. I have updated the ReadMe file.


Map and Fat Arrow Function

Description
Describe the Map method and Fat arrow function to make code easier
Domain
MERN
Type of Contribution
Documentation
Code of Conduct

[X] I follow Contributing Guidelines & Code of conduct of this project.

/assign

MERN/Topic/Frontend/React/Fat Arrow Function and  Map method


Support using = self.inner for Debug

There are instances in the std library where a Debug implementation forwards to a single field, e.g. the impl for File. We don't currently support forwarding for Debug, but I think we could support this, e.g.:
# use impl_tools::autoimpl;
#[autoimpl(Debug using self.0)]
struct Thing<T: std::fmt::Debug>(T);

This impl would print the wrong type name.


In php github workflow not use sdl2 lib binding

Hi, I just saw your comment on top of ycombinator (congrats!), checking out this repo I notice that in all github workflow we are using sdl2 lib binding, but in php github workflow I didn't see it being use (It is still actively maintained at https://github.com/Ponup/php-sdl).
That may yield why php is roughly the same speed with python (it is faster in my experience).
I've never use SDL2 for php so I don't yet know how, creating this issue for tracking (as I might try SDL with php on weekend).

That looks like it should work - hopefully it's as simple as deleting the empty stub functions in _sdl.php and installing the sdl.so extension and fingers crossed it all runs first time :)

sadly, I don't think it work, I got segfault when try php-sdl lib
(No debugging symbols found in php)
(gdb) run src/main.php -S -H -p 1 -t opus5.gb
Starting program: /usr/bin/php src/main.php -S -H -p 1 -t opus5.gb
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".

Program received signal SIGSEGV, Segmentation fault.
0x0000555555850627 in zend_fetch_resource ()

Here is short reproduction:
rosettaboy.Dockerfile
FROM ubuntu:latest

ARG DEBIAN_FRONTEND=noninteractive

RUN apt update && apt upgrade -y

RUN apt install software-properties-common -y

RUN add-apt-repository ppa:ondrej/php

RUN apt install php php-xml php-cgi php-pgsql php-swoole php-pear php-dev libsdl2-dev vim git wget gdb -y

RUN pecl install sdl-beta

RUN echo "extension=sdl.so" >> /etc/php/8.1/cli/php.ini

RUN git clone https://github.com/shish/rosettaboy

RUN cd /rosettaboy/php/ && wget https://github.com/sjl/cl-gameboy/blob/master/roms/opus5.gb\?raw\=true -O opus5.gb

docker build -t rosettaboy-php -f rosettaboy.Dockerfile .

docker run -it --cap-add=SYS_PTRACE --security-opt seccomp=unconfined rosettaboy-php /bin/bash

I'm out of wit atm, maybe escalate to php-sdl maintainer ?


Private Workbooks Deprecation Jul 2021

Hello, can this work be put on hold please?

“Save as private workbook” is quite useful,  and we would like it to remain.
Test workbooks will fill up the shared area unnecessary otherwise.
Probably better to provide a choice instead of removing it completely.

The git doc does not explain the reason for this change or value (2nd time the document is published, first one in November).

No explanation on role changes for users and work required for admins.

It seems a user role for the update to workbooks as a whole does not exist. Can a document on workbook access be created please?

Thanks

Working with PM and @tonykslee to update the docs with better explanations.
Removing private workbooks simplifies a lot of confusing experiences and support issues that customers are having with private workbooks compared to full Azure Resource based Workbooks.
The simplest way to keep things separate is to save test workbooks to other subscriptions or resource groups.  An upcoming change to the workbook gallery will make it easier to filter to specific resource groups to filter to specific resource groups.
There are existing documents about workbook  Access control here:
https://docs.microsoft.com/en-us/azure/azure-monitor/visualize/workbooks-access-control
There are no changes to the behaviors of roles, or access control with these changes.


Can u plz give some idea about resuming training if interrupted in between

Hello wenxuan. I hope you recognize me. I had raised an issue earlier about predictions on brats 2020 data.
I  need your help in resuming the training. I am training model on google colab which shuts after 12 hours. So I want to resume it from the same point where it gets stopped.

Hi,gjkaur,I remebered you,bro. Our training code already achieves the goal of resuming the training. To save the model weight of the intermediate process, all you have to do is adjusting this parameter ('--save_freq', default=1000, type=int).


Unable to interpret commands in a group/chat

I have read:

Usage information
Help information

I am using the latest version of the library.
I am using the dummy code for /echo command:
// Matches "/echo [whatever]"
bot.onText(/\/echo (.+)/, (msg, match) => {
  // 'msg' is the received Message from Telegram
  // 'match' is the result of executing the regexp above on the text content
  // of the message
  const resp = match[1]; // the captured "whatever"

  // send back the matched "whatever" to the chat
  bot.sendMessage(chatId, resp);
});

I also added my bot as admin in a chat/group.
Expected Behavior
While executing the command /echo I would expect to have receive the message. But It isn't returning nothing. It only returns if I send the command in pvt to the bot.
I didn't found any issue or documentation about this issue.
Did I miss anything?

Try /echo hello (note the hello)

Same thing. It's only triggered if I talk privately to the bot :/
I also registered the new command with BotFather.

I have noticed there is no chatId variable in your code block. Try this:
// Matches "/echo [whatever]"
bot.onText(/\/echo (.+)/, (msg, match) => {
  // 'msg' is the received Message from Telegram
  // 'match' is the result of executing the regexp above on the text content
  // of the message

  const chatId = msg.chat.id;
  const resp = match[1]; // the captured "whatever"

  // send back the matched "whatever" to the chat
  bot.sendMessage(chatId, resp);
});

Also make sure you disable bot privacy. See this info for more details.

Actually I didn't use chatId in the method because I already have it outside the method. This is not the reason, otherwise I would get an error.
I am not getting any error, it simply doesn't work if I post the command in a chat/group.

For debugging purposes, add the following snippet,
bot.on("text", function(msg) {
    console.log("Received message");
    console.log("  chat ID:", msg.chat.id);
    console.log("  text:", msg.text);
});

Now try sending the following messages in the chat/group:

/echo hello
/echo@BOTNAME hello (where BOTNAME is the bot's username)
hello

Now provide us with the output (if any) you receive.

Hi,
I am facing the same issue actually.
Using the latest code. Bot only gets the bot.on('message') or bot.onText() when chatting 1 on 1 with the bot.
But once added to a group the bot does not respond/get anything at all.
I do have console logs, and none are showing when receiving or sending messages via group chats.
I tried your documents and the bot is added as admin with full rights.
I tried capturing any message and send a response, no luck in group chat (but works in 1on1 chat).
latest nodejs, latest bot code, dev and running on osx 10.12.6
As reported, 1on1 chat is fine, but nothing happens in group chats.
Api and token is working (used postman to check api response).

disable bot privacy

Im facing same problem. It does not send message to the group. Im using the message.chat.id property.

Yes, for get all messages (include the commands without write the bot username) in a group is important disable the bot privacy in botfather.
In the other case the bot only get updates if the command contains the bot username. Example: /mycommand@my_bot


Autocomplete: allow "falsely" input values

See https://github.com/primer/react/pull/2127 for original PR

I dropped a commit that was in the original PR (https://github.com/primer/react/pull/2127/commits/6f5285674314d2cabfd2518f6fbb46d8815ddbd0) that had changes related to https://github.com/primer/react/pull/2116

Merging without reviews because the original PR was already reviewed


[📚] Social sign in: Facebook docs are outdated

In https://firebase.flutter.dev/docs/auth/social#facebook the package flutter_facebook_auth is used. FacebookAuth.instance.login() no longer returns AccessToken but it returns LoginResult which has a accessToken member as per the package documentation

Can i take up this issue?


Update DocBlock in Redis facade

add @mixin \Illuminate\Redis\RedisManager instead @method static \Illuminate\Redis\Connections\Connection connection(string $name = null) for IDE suggesting

I think this raises other issues.

Well, I don’t know, because the code is not affected. Only DocBlock

He means issue with IDE, autocomplete, maybe static code analyzers, etc.


Add .emac.d to dotfiles

Default to spacemacs
defaults:
autonomy_user_dotfiles_emacs_repo: spacemacs
autonomy_user_dotfiles_emacs_repo_branch: develop
when:

autonomy_enable_emacs
autonomy_user_dotfiles_emacsd

Theoretically in place.  The git module is not behaving as expected.
It's stating that it completed successfully, but it's ending up in a detached head state rather than on the correct branch.

Never mind, I had the 'version' set to the value used by 'remote'


Subscription support

Is there any short term plan for subscription support yet? If yes how soon else a good direction to start off in implementing the support would be awesome. I am not strong in dart yet, but I can take a plunge. Thank you guys. Graphql support for flutter was a big plus for me making my decision to use flutter

+1 on this. I am interested in creating a front-end client for my real-time application in Flutter, however, I need the ability to manage GraphQL subscriptions.
Have any timelines been discussed for implementing this feature? If there is a known path forward I would also be interesting in helping out.

It's not a very high priority item for our team, but feel free to open a PR. We will start implementing subscriptions once most of the core features are more mature.

Cool

@juicycleff There's a basic Subscription Widget available to test out now! :)


Grafana does not support Druid Moving Average Query with error: "unsupported type query"

I use next Grafana version:

druid plugin version equals 1.2.0 and with this setup I receive error below:

Prompt please, what druid plugin and grafana versions support moving-average query?

@jbguerraz Hello!

If you're willing to contribute to the project to bring that new query type I'll be happy to help you doing so!

No, I'm not planing to contribute.
Did I correct understand that you're not going to add this functional in the near future?

I, personally, won't until we face the need for it.

Ok)


toggle.diff.editorMode needs a description and should be accessible through the command palette

While investigating #54967
The editor menu for the diff editor has the "Toggle Inline View" action.

This is not available through the command palette.
The command id is toggle.diff.editorMode but there is no description shown in the keybindings editor.

@kieferrm I do not think we support to show a description for commands in the keybindings editor, e.g. check this:

I think we should have a general issue for fixing that situation so that every command comes up with a proper description, can you file that? I will rename this issue.

Verifier: you can invoke "Compare: Toggle Inline View" from the command palette, only if the active editor is a diff editor.


Added backers and sponsors from OpenCollective

now backers/sponsors of koel from opencollective will appear on your readme!

Thanks Pia!


Need to support POST method for Search template

Is your feature request related to a problem? Please describe.
We used to use GET method + search template to get the result and we find in elasticsearch 7.13 it supports POST + search template. We had tested that if using this method the connection will keep alive not close. https://www.elastic.co/guide/en/elasticsearch/reference/7.13/search-template-api.html#search-template-api
Describe the solution you'd like
Support this api in opensearch client
Describe alternatives you've considered
No
Additional context
No

Hi @AlanYuXuan !  Can you give us more information about the use case you're trying to solve?  Is keeping the connection alive critical to your use case?
Thanks!

@AlanYuXuan I am personally not against supporting both GET and POST for the same queries, but you should be able to keep-alive on GETs too. Are you seeing different headers in one vs. the other? Is the client library you're using configured not to keep-alive on GET?

Closing this issue, due to lack of information. @AlanYuXuan please feel free reopen once you have more details and answers for the existing questions.


Type definitions are missing for most modules

Module graphql-transformer-core has typescript definitions - but in order to use it standalone, you need a bunch of other modules that do not. E.g. graphql-dynamodb-transformer. Given the source code is Typescript, there doesn't seem to be any reason why you wouldn't also generate type definitions for all of the modules (excepting a few modules where allowJs is set: amplify-cli, amplify-graphql-explorer, amplify-graphql-types-generator, amplify-ui-tests).
When you alter the tsconfig.json to generate type definitions, a few minor type errors emerge in the amplify-cli build (PR to follow).
Steps to reproduce the behavior:
import { DynamoDBModelTransformer } from 'graphql-dynamodb-transformer'

results in an Could not find a declaration file for module error on compilation.
Expected behavior
Published NPM packages to include type definitions, except where allowJs setting makes that impossible.

Closing as the related PR is merged.


can't use viewpager in the header part instead of textview?

Hi, it is a awesome project, I just met a problem, I wanna replace the textview control of the header with a viewpager, but it seems being failed, and the listview move to the header, how to fix it?

Hi, I used ViewPager as a header in one of my projects, and it worked just fine. Maybe you could provide me with more details?

I using the LinearLayout replace the textview in the header, then I add a viewpager into the LinearLayout, and the whole layout is within a fragment, however it is not working. can you show me your codes?
<ru.noties.scrollable.ScrollableLayout
xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:app="http://schemas.android.com/apk/res-auto"
android:layout_width="match_parent"
android:layout_height="match_parent"
android:id="@+id/scrollable_layout"
app:scrollable_maxScroll="@dimen/header_height"
app:scrollable_considerIdleMillis="125"
app:scrollable_friction="0.075"
app:scrollable_closeUpAnimationMillis="250"
app:scrollable_defaultCloseUp="true"
app:scrollable_scrollerFlywheel="false"
app:scrollable_closeUpAnimatorInterpolator="@android:anim/accelerate_decelerate_interpolator">
<LinearLayout
    android:layout_width="match_parent"
    android:layout_height="wrap_content"
    android:orientation="vertical">

    <LinearLayout
        android:layout_width="match_parent"
        android:layout_height="@dimen/header_height"
        android:background="@color/header_background"
        android:id="@+id/header"
        android:gravity="center">

       <android.support.v4.view.ViewPager
            android:layout_width="match_parent"
           android:layout_height="match_parent"
           android:id="@+id/banner_pager" />

    </LinearLayout>

    <ru.noties.scrollable.sample.TabsLayout
        android:layout_width="match_parent"
        android:layout_height="@dimen/tabs_height"
        android:background="@color/tabs_background"
        android:id="@+id/tabs" />

</LinearLayout>

<android.support.v4.view.ViewPager
    android:layout_width="match_parent"
    android:layout_height="match_parent"
    android:layout_marginTop="@dimen/tabs_height"
    android:id="@+id/view_pager" />

</ru.noties.scrollable.ScrollableLayout>

The code looks just fine (although I would eliminate ViewPager's LinearLayout in header). Maybe you could be more specific about the listview move to the header I don't quite get it.

I've added a ScrollableFragment to the sample application to show how ScrollableLayout could be used inside a Fragment with a ViewPager as a header


[Workers] Add Strict Compression compatibility flag

Add the new strictCompressioncompatibility flag, implemented in https://github.com/cloudflare/workerd/commit/4460d51f5fa9c5b4d73833508d3b91f4cd1b5eb4#diff-c7fa8ca455dd055174097625258de733986b4df090ed5e56b260ff2ed9b71b9a
It might be useful to get https://github.com/cloudflare/cloudflare-docs/pull/9244 in first as the flag added in that PR was added first and a comment refers to it. I am working on some more compat flag changes, but wanted to get this one in first.

Added another commit for the soon-to-be-merged strict crypto flag.

Linking to this from https://github.com/cloudflare/cloudflare-docs/pull/9312/files#diff-8619da0887af5a02cc96d0335c7a4583d284b2d0dc59f19def45f58c58ec9e92R16 would be ideal


alphabetize cli options

Summary
CLI options in documentation are not alphabetized properly. This PR reorders them and otherwise does not modify content.
Test plan
Ran yarn build but failed at 'Building TypeScript definition files' which would appear to be unrelated.

The CLA fails to submit. I've emailed<EMAIL_ADDRESS>

Was able to submit CLA fine this morning with same inputs. 🤷

@bdefore ping 🙂

@SimenB sorry for the delay on this. i've merged in master and made the analogous changes to older CLI docs. to my eye this looks good, but re-requesting your review since it touches more files.


Move files into user directories

@CrawFishFiesta  @dpalbrecht  I'm going to move your files into the /users sub-directories. Hopefully, Your local repos are up-to-date. If not, it may be easier to re-clone the repo and move your changed files over.

Ok, @CrawFishFiesta @dpalbrecht , I moved things around. If any of your files are in the wrong directory, you can move them and re-commit. If they are missing, let me know and get them back.

Ok thanks.
On Tue, Feb 14, 2017 at 1:58 PM, Eric Busboom<EMAIL_ADDRESS>wrote:

Ok, @CrawFishFiesta https://github.com/CrawFishFiesta @dpalbrecht
https://github.com/dpalbrecht , I moved things around. If any of your
files are in the wrong directory, you can move them and re-commit. If they
are missing, let me know and get them back.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/sandiegodata/age-friendly-communities/issues/3#issuecomment-279849371,
or mute the thread
https://github.com/notifications/unsubscribe-auth/ARPVnnx6jreYVFlMyat9wyGIi7J1mUJPks5rciOTgaJpZM4MBB7u
.

Looks good to me. Thanks, Eric.


Add missing props documentation in FlatList

based on issue https://github.com/facebook/react-native/issues/16501 about missing documentation in some RN components, in this PR I added back the props documentation of FlatList component. All the documentation and description were taken from https://facebook.github.io/react-native/docs/0.48/flatlist.html with some adjustment to match the current doc style.
Before:

With this PR:

Landed in 90ea73524bcb5b3db242075607705551914d76d4

@hramos Thanks! Just to confirm, is this the correct way to update the docs? If yes, I'm going to submit another PR for other missing props documentation. Would appreciate any feedback from you.

Yes, it is correct! I had to merge it manually as I just recently added Prettier support. You can now make sure your docs are formatted properly by running 'yarn prettier' in the website/ folder before submitting your PR.

@hramos Alright! Thank you for the confirmation. Will submit another PR soon.


Prevent failures by pinning autodelete to master

What
Autodelete will often fail if the environment was deployed from a branch that has been merged, meaning the remote branch no longer exists. This pins the autodelete branch to master to avoid this.
90% of the time the branch is now part of master and the operations carried out are quite simple, so this should cause no problems.
How to review
Code review.
Who can review
Anyone!

LGTM. Reviewed code only.

90% of the time the branch is now part of master

That's an interesting statistic, do you mind sharing how you arrived at it? :trollface:

@alext Sure, here's a direct quote:

<@alext> I reckon that’s true 80-90% of the time.


Create integration with docker-compose up as-is

Description
Please provide a meaningful description of what this change will do, or is for. Bonus points for including links to related issues, other PRs, or technical references.
Note that by not including a description, you are asking reviewers to do extra work to understand the context of this change, which may lead to your PR taking much longer to review, or result in it not being reviewed at all.
Type of Change

[ ] Bug Fix
[ ] New Feature
[ ] Breaking Change
[ ] Refactor
[ ] Documentation
[ ] Other (please describe)

Checklist

[ ] I have read the contributing guidelines
[ ] Existing issues have been referenced (where applicable)
[ ] I have verified this change is not present in other open pull requests
[ ] Functionality is documented
[ ] All code style checks pass
[ ] New code contribution is covered by automated tests
[ ] All new and existing tests pass

Codecov Report

:exclamation: No coverage uploaded for pull request base (main@e71bec9). Click here to learn what that means.
The diff coverage is n/a.

@@           Coverage Diff           @@
##             main       #1   +/-   ##
=======================================
  Coverage        ?   44.78%
=======================================
  Files           ?       44
  Lines           ?     1389
  Branches        ?       60
=======================================
  Hits            ?      622
  Misses          ?      755
  Partials        ?       12

Continue to review full report at Codecov.

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[layout] - centered the canvas and adjusted side panel position

centered the canvas which was initially on the right side.

@zaihl Which issue does this PR belongs to?

@zaihl Which issue does this PR belongs to?

https://github.com/singodiyashubham87/Draw-it-out/issues/246#issue-2328940391

@zaihl You must follow the guidelines which clearly says that you have to create a PR only if you are assigned an issue.
You are not assigned issue #246 & still, you raised a PR, why?
This is not acceptable at all.
Closing the PR. You should wait until the issue gets assigned to you.

@zaihl You must follow the guidelines which clearly says that you have to create a PR only if you are assigned an issue.
You are not assigned issue #246 & still, you raised a PR, why?
This is not acceptable at all.
Closing the PR. You should wait until the issue gets assigned to you.

I am sorry. I didn't keep that in mind. Forgive me as I am new to this.

@zaihl It's okay for a beginner, do keep it in mind next time. I've assigned you the issue now & you can start working on it.


SoundEvent API descriptions seem reversed

Bug Description
These descriptions seem reversed

Thanks, good catch. I'll fix these later today along with the spi feedback.

Fixed in main branch. Redeploying to the beta now (~10 mins).
I'll update the API documentation in Crowdin at ~5pm.

`Thanks Matt!

Updated in Crowdin.


[regression] migration changing column started to fail

Elixir version
Elixir 1.16.2 (compiled with Erlang/OTP 26)
Database and Version
any
Ecto Versions
master
Database Adapter and Versions (postgrex, myxql, etc)
postgrex 0.19.2
Current behavior
this migrations  started to fail recently:
  def change do
    alter table(:employees) do
      modify(:email, :string, size: 65_535, from: {:string, size: 255})
    end
  end

with error
21:00:26.999 [info] alter table employees
** (Postgrex.Error) ERROR 42601 (syntax_error) syntax error at end of input

    query: ALTER TABLE "employees"
    (ecto_sql 3.12.1) lib/ecto/adapters/sql.ex:1096: Ecto.Adapters.SQL.raise_sql_call_error/1
    (elixir 1.16.2) lib/enum.ex:1700: Enum."-map/2-lists^map/1-1-"/2
    (ecto_sql 3.12.1) lib/ecto/adapters/sql.ex:1203: Ecto.Adapters.SQL.execute_ddl/4
    (ecto_sql 3.12.1) lib/ecto/migration/runner.ex:348: Ecto.Migration.Runner.log_and_execute_ddl/3
    (elixir 1.16.2) lib/enum.ex:1700: Enum."-map/2-lists^map/1-1-"/2
    (ecto_sql 3.12.1) lib/ecto/migration/runner.ex:311: Ecto.Migration.Runner.perform_operation/3
    (stdlib 5.2.3) timer.erl:270: :timer.tc/2
    (ecto_sql 3.12.1) lib/ecto/migration/runner.ex:25: Ecto.Migration.Runner.run/8

It fails into [this branch] of the if (https://github.com/elixir-ecto/ecto_sql/blame/75ddf591fbe56977b041836079c01be298384024/lib/ecto/adapters/postgres/connection.ex#L1588) but returns [[], [], []] I think it suppose to be be executed the else branch.
I'm assuming it was introduced in this pr because my master version from before 2 weeks was working fine
Expected behavior
the migration works on 3.12.1

Hrm, we may need to revert the PR after all and introduce a command explicitly to change everything but the type.

the type returned from column_type(type, opts) is identical on 167 and 173
Thats the opts to this function:
[size: 65535, from: {:string, [size: 255]}]
  167 +      column_type = column_type(type, opts)
  168 +      from_column_type = extract_column_type(opts[:from])
  169 +
  170 +      drop_reference_expr = drop_reference_expr(opts[:from], table, name)
  171 +      any_drop_ref? = drop_reference_expr != []
  172 +
  173 +      if column_type == column_type(from_column_type, opts) do

Oh, so it would work if we extracted it properly? 🤔 Can you please send a PR?


A/B Testing

Add A/B testing on top of existing mixpanel infrastructure

This is now implemented in master.


[CP] Pass the --enable-skparagraph=false flag if the EnableSkParagraph manifest metadata value is false

Use case

Proposal

ssue_link
https://github.com/flutter/flutter/issues/103846#issuecomment-1136334771
Commit Hash
1e28cfafa05fd3ea01e3b1d72b6eacf95b537d95
Target
stable
Impacted Users
Users affected by regressions in SkParagraph
Impact Description
Flutter 3 was the first stable release that used SkParagraph to render text.
Some users are affected by issues in SkParagraph that were not present in the old Libtxt text renderer. For these cases Flutter 3 provides a flag that reenables Libtxt.
This PR makes it easier to use that flag on Android. With this PR, Android users can opt into Libtxt by setting a metadata attribute in the app manifest and removes the requirement to also pass a flag in the app's FlutterActivity subclass.
Workaround
Override getFlutterShellArgs in the app's FlutterActivity subclass
Risk
low
Test Coverage
yes
Validation Steps
Run an app like the one in https://github.com/flutter/flutter/issues/103846 that is affected by an SkParagraph regression.
Verify that the text renders as expected when this tag is added to the app's AndroidManifest.xml:

<meta-data
    android:name="io.flutter.embedding.android.EnableSkParagraph"
    android:value="false" />

This seems to be duplicate of https://github.com/flutter/flutter/issues/104641 which has been already approved cherry-pick.
Closing. Please follow-up in linked issue for further updates.


HHH-13466 2

https://hibernate.atlassian.net/browse/HHH-13466
Supersedes: https://github.com/hibernate/hibernate-orm/pull/2956

@sebersole, thanks for reviewing. I agree with what you say; unfortunately, I don't see an alternative.


Implement Rope::append()

Rope::append() and Rope::with_append() actually both have doctests. Not sure if that's a sufficient amount of tests for us to mark this as done.
WDYT @cjm00?


Found Code injection by MOMO v4.4.1

rt

@HuskyDG I am sorry. I did not understand your last comment. What do I need to do ? Please explain so that I can fix the code.


Bug: accelerated query fails with error Expression not supported Decimal128(Some(5),38,2)"

Describe the bug
TPCH-6 query fails with error when using sqlite or duckdb acceleration (MySQL). Arrow works fine.
sql> select
    sum(l_extendedprice * l_discount) as revenue
from
    lineitem
where
        l_shipdate >= date '1994-01-01'
  and l_shipdate < date '1995-01-01'
  and l_discount between 0.06 - 0.01 and 0.06 + 0.01
  and l_quantity < 24;
Error An internal error occurred. Execute '.error' to show details.
sql> .error
Status { code: Internal, message: "External error: Execution error: Unable to generate SQL: Expression not supported Decimal128(Some(5),38,2)", source: None }

The following PR was sent to datafusion-table-providers repo to fix this
https://github.com/datafusion-contrib/datafusion-table-providers/pull/19

Fixed by https://github.com/spiceai/spiceai/pull/2122


Changed BaseUndoable and updated some recipes to it.

Have Jared see this before merging to make sure he aproves

Pull denied,
First of all, removal is ALWAYS a single output item unless the machine has 2 outputs.
Also, BaseListAddition works, I see no reasons to stop using it.
I will however ping @joshiejack and see what he thinks

Ok, but I'm confused about the first part of what you said. I didn't do anything that had 2 outputs?


Support MSVC on Windows

Support MSVC on Windows (add CMake)
Add CTEST_ADD_TESTS_MANUALLY, CTEST_NO_JMP from https://github.com/gaydov/ctest
Add an input arg to filter test name.

longjmp() is certainly available, see https://msdn.microsoft.com/en-us/library/3ye15wsy.aspx.
Optional setup and teardown could be implemented as well with a little bit of hackery around (undocumented) /alternatename:... linker flag.

Additionally,

time should be queried with QueryPerformanceCounter() and QueryPerformanceFrequency().
inline should only be defined if __cplusplus isn't
MSVC 2013 supports designated initializers; also, moving __Test_Section to the beginning of declaration allows to support both MSVC and non-MSVC witht the same code
MSVC 2015 provides C99 snprintf()
CTEST_SEGFAULT support is totally possible as well, by not using sys_siglist and replacing kill(getpid(), signum) with raise(signum)

You also admit yourself that you combined three unrelated changes in one pull request, which is not the way it should be :)

@mikedld , thank you for the suggestion.
Yes, longjmp is supported. The patch just provide CTEST_NO_JMP, not using it for MSVC.
According to http://stackoverflow.com/questions/24090739/possible-compiler-bug-in-msvc12-vs2013-with-designated-initializer , MSVC 2013 don't support designated initializers.
I just modified my repo. When I create the pull request, I found that it includes some unrelated commits.

I'm going to close this request, due to outstanding issues. Feel free to request a new, improved patch


SS_ListDecorator bad argument forwarding on wrapper methods

Currently $myDecoratedList->filter('Bla', 2) will call $internalList->filter(array('Bla', 2))
(Ignore commit 3a9ea42 and 82ef236. My first thought was that the problem is located at SSViewer_Scope :p)

@sminnee You're too trigger-happy with the merge button ;) The contributor even asked to ignore two of the three commits - now we have two fairly arbitrary changes in there, which also aren't clearly marked as a revert - confusing and unnecessary.

Oh, oops, I ran the tests and they passed and thought we were okay.  Should I revert 3a9 and 82e, then?

The third commit (82ef23687969384368a113a3c0d07d67b6211184) reverts the first commit, so all good - just confusing.

Sorry but I could not fork the file ... I always got an 404 error.
And I had no idear howto pish only one commit :(

Have a read through http://doc.silverstripe.org/sapphire/en/trunk/misc/collaboration-on-git :)


Secure BWA with Entra

Fixes #33779
Notes

I'll edit again Tuesday morning for the final spit-'n-polish ✨.
The sample app is at :point_right: https://github.com/dotnet/blazor-samples/tree/main/9.0/BlazorWebAppEntra
Fixing a broken Keycloak link on the side

Internal previews

📄 File
🔗 Preview link

aspnetcore/blazor/security/blazor-web-app-with-entra.md
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aspnetcore/blazor/security/blazor-web-app-with-oidc.md
aspnetcore/blazor/security/blazor-web-app-with-oidc

aspnetcore/security/identity-management-solutions.md
aspnetcore/security/identity-management-solutions

aspnetcore/toc.yml
aspnetcore/toc

The latest version of the remarks on security ...

Don't store app secrets, connection strings, credentials, passwords, personal identification numbers (PINs), private C#/.NET code, or private keys/tokens in client-side code, which is always insecure. In test/staging and production environments, server-side Blazor code and web APIs should use secure authentication flows that avoid maintaining credentials within project code or configuration files. Outside of local development testing, avoid the use of environment variables to store sensitive data, as environment variables aren't the most secure approach. For local development testing, the [Secret Manager tool](xref:security/app-secrets) is recommended for securing sensitive data.

I mirrored these updates on the Blazor ROPC PR, and I'm 👂 for further feedback on improving this guidance.
I was a bit strong on env vars by not using the word "recommend," favoring instead to just guide devs to avoid them. Too strong? I can use the word "recommend" similar to how @Rick-Anderson phrased it ...

Outside of local development testing, we recommend avoiding the use of environment variables to store sensitive data, as environment variables aren't the most secure approach.

@halter73 ... I think we're good to go here.
I'll review these articles again before GA.


Information about chats not being retained for on premises users is incorrect

The statement below is incorrect.

In a cloud-based organization, users who participate in conversations that are part of the chat list in Teams must have an Exchange Online mailbox in order to retain chat conversations when the mailbox is placed on an eDiscovery hold. That's because conversations that are part of the chat list are stored in the cloud-based mailboxes of the chat participants. If a chat participant doesn't have an Exchange Online mailbox, you won't be able to preserve those chat conversations. For example, in an Exchange hybrid deployment, users with an on-premises mailbox may be able to participate in conversations that are part of the chat list in Teams. But in this case, content from these conversation can't be preserved because these users don't have a cloud-based mailboxes that can be placed on hold.

Organisations with hybrid exchange have shadow Exchange online mailboxes created to hold copies of the chat compliance records. Historically you needed to ask Microsoft to enable this feature but now it is enabled by default.
The Add Office app content for on-premises users checkbox is added under the Locations in Content Search. This allows eDiscovery to search the chats for on premise users.
See Microsoft Doc article https://docs.microsoft.com/en-us/microsoft-365/compliance/search-cloud-based-mailboxes-for-on-premises-users?view=o365-worldwide.
Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 7263ba97-4e79-7144-a9d4-5138fe228fa2
Version Independent ID: 316e242e-7f37-592e-9c8f-e754b23a29ca
Content: Create eDiscovery holds in a Core eDiscovery case - Microsoft 365 Compliance
Content Source: microsoft-365/compliance/create-ediscovery-holds.md
Service: o365-seccomp
GitHub Login: @markjjo
Microsoft Alias: markjjo

@nikki-c Thank you for your feedback. We have made changes to the document via PR#https://github.com/MicrosoftDocs/microsoft-365-docs/pull/4535
Thanks
Sri

@nikki-c Closing this issue as dupe of https://github.com/MicrosoftDocs/microsoft-365-docs/issues/4576 that you recently opened. I will also review/update PR#4535. The changes in the PR aren't quite accurate.


Implement test fixtures

Fixtures allow Hardhat to not repeat the same operations and load the correct testing state rather than redoing all the setup on each test in the beforeEach loop. I implemented fixtures for a part of the testing codebase and here are the results:
Before fixtures
 -  92fe24b551c432e21c083b401222196433b8a9b8

Executed in  158.30 secs
usr time  148.80 secs    0.06 millis  148.80 secs
sys time    9.02 secs    1.19 millis    9.02 secs
After implementing fixtures for half the tests - c05b7c4b6c6390a5853f37feeccba460aca97e4c

Executed in   71.10 secs
usr time   68.83 secs    0.07 millis   68.83 secs
sys time    4.14 secs    1.21 millis    4.13 secs
I don't like how some of these code changes look, but let me know if you see how it can be improved.
I think the most useful fixtures are for the equippable stuff as there is a lot of setup for each contract before testing besides the deployments themselves.

Added more test fixtures and merged latest changes from /dev


[CLEANUP] These files are in need of cleanup and/or better comments

[x] MASTER-SCRIPT.ahk
[ ] MASTER-FUNCTIONS.ahk
[ ] MASTER-SETTINGS.ahk

Moving to Projects


A question about repo  community-operators-prod and certified-operators

Can I remove the operators from the repo community-operators-prod after I release them in the repo certified-operators?

@FushuWang this is possible by opening a PR.


How can I redirect back to the app

i'm able to verify the metamask address but I have to navigate back to my app manually. Is there a way that it can go back automaticaly?

i'm able to verify the metamask address but I have to navigate back to my app manually. Is there a way that it can go back automaticaly?

Sorry about that, last time I check, metamask mobile does not support that.

Metamask has automatically reopened the app after successful confirmation


ObjectFormer

您好，请问ObjectFormer的--init_weight_path object_former/processed_model_weights.pth 这个weight在哪里
谢谢回复

@Inkyl Xuekang is responsible for this part.

您好，请问ObjectFormer的--init_weight_path object_former/processed_model_weights.pth 这个weight在哪里 谢谢回复

Sorry, the specific weights are not currently available, but I can provide you with a script to process and extract the relevant weights.
import math
from typing import List, Optional
import torch
import timm
import torch.nn.functional as F

# Load a pre-trained Vision Transformer (ViT) model
model = timm.create_model('vit_base_patch16_224', pretrained=True)

def resample_abs_pos_embed(
        posemb,
        new_size: List[int],
        old_size: Optional[List[int]] = None,
        num_prefix_tokens: int = 1,
        interpolation: str = 'bicubic',
        antialias: bool = True,
        verbose: bool = False,
):
    # Determine the old and new sizes, assuming a square shape if old_size is not provided
    num_pos_tokens = posemb.shape[1]
    num_new_tokens = new_size[0] * new_size[1] + num_prefix_tokens
    if num_new_tokens == num_pos_tokens and new_size[0] == new_size[1]:
        return posemb

    if old_size is None:
        hw = int(math.sqrt(num_pos_tokens - num_prefix_tokens))
        old_size = hw, hw

    # Separate the prefix tokens if any exist
    if num_prefix_tokens:
        posemb_prefix, posemb = posemb[:, :num_prefix_tokens], posemb[:, num_prefix_tokens:]
    else:
        posemb_prefix, posemb = None, posemb

    # Perform interpolation
    embed_dim = posemb.shape[-1]
    orig_dtype = posemb.dtype
    posemb = posemb.float()  # Convert to float32 for interpolation
    posemb = posemb.reshape(1, old_size[0], old_size[1], -1).permute(0, 3, 1, 2)
    posemb = F.interpolate(posemb, size=new_size, mode=interpolation, antialias=antialias)
    posemb = posemb.permute(0, 2, 3, 1).reshape(1, -1, embed_dim)
    posemb = posemb.to(orig_dtype)

    # Concatenate back the prefix tokens if they were separated earlier
    if posemb_prefix is not None:
        posemb = torch.cat([posemb_prefix, posemb], dim=1)

    return posemb

# Initialize a dictionary to store the processed weights
processed_state_dict = {}

# Extract and resample the positional embedding
pos_embed = model.state_dict()['pos_embed'][0][1::].unsqueeze(0)
pos_embed = resample_abs_pos_embed(pos_embed, [14, 28], num_prefix_tokens=0)
processed_state_dict['pos_embed'] = pos_embed

# Copy the patch embedding projection weights
processed_state_dict['patch_embed.proj.weight'] = model.state_dict()['patch_embed.proj.weight']
processed_state_dict['patch_embed.proj.bias'] = model.state_dict()['patch_embed.proj.bias']

# Process and extract weights from the first 8 transformer blocks
for i in range(8):  # Only process the first 8 blocks
    block_prefix = f'blocks.{i}.'

    # Extract norm1 weights and biases
    processed_state_dict[f'{block_prefix}norm1.weight'] = model.state_dict()[f'{block_prefix}norm1.weight']
    processed_state_dict[f'{block_prefix}norm1.bias'] = model.state_dict()[f'{block_prefix}norm1.bias']

    # Split and extract q, k, v weights and biases from qkv
    qkv_weight = model.state_dict()[f'{block_prefix}attn.qkv.weight']
    qkv_bias = model.state_dict()[f'{block_prefix}attn.qkv.bias']
    dim = qkv_weight.shape[0] // 3
    processed_state_dict[f'{block_prefix}attn.q.weight'] = qkv_weight[:dim]
    processed_state_dict[f'{block_prefix}attn.k.weight'] = qkv_weight[dim:2*dim]
    processed_state_dict[f'{block_prefix}attn.v.weight'] = qkv_weight[2*dim:]
    processed_state_dict[f'{block_prefix}attn.q.bias'] = qkv_bias[:dim]
    processed_state_dict[f'{block_prefix}attn.k.bias'] = qkv_bias[dim:2*dim]
    processed_state_dict[f'{block_prefix}attn.v.bias'] = qkv_bias[2*dim:]

    # Extract the attention projection weights and biases
    processed_state_dict[f'{block_prefix}attn.proj.weight'] = model.state_dict()[f'{block_prefix}attn.proj.weight']
    processed_state_dict[f'{block_prefix}attn.proj.bias'] = model.state_dict()[f'{block_prefix}attn.proj.bias']

    # Extract norm2 and MLP weights and biases
    processed_state_dict[f'{block_prefix}norm2.weight'] = model.state_dict()[f'{block_prefix}norm2.weight']
    processed_state_dict[f'{block_prefix}norm2.bias'] = model.state_dict()[f'{block_prefix}norm2.bias']
    processed_state_dict[f'{block_prefix}mlp.fc1.weight'] = model.state_dict()[f'{block_prefix}mlp.fc1.weight']
    processed_state_dict[f'{block_prefix}mlp.fc1.bias'] = model.state_dict()[f'{block_prefix}mlp.fc1.bias']
    processed_state_dict[f'{block_prefix}mlp.fc2.weight'] = model.state_dict()[f'{block_prefix}mlp.fc2.weight']
    processed_state_dict[f'{block_prefix}mlp.fc2.bias'] = model.state_dict()[f'{block_prefix}mlp.fc2.bias']

# Save the processed weights to a .pth file
torch.save(processed_state_dict, 'processed_model_weights.pth')

Hi, i find some parts of the ObjectFormer are missing.
https://github.com/scu-zjz/IMDLBenCo/blob/1807684d411ae770aabfebec82b33adcf1eb5b4e/IMDLBenCo/model_zoo/object_former/object_former.py#L319-L324
The implementation of label loss is missing. This method needs a global average pooling with a linear layer as the classifier