[#3550] Port CloudAdapter & MSI to TypeScript samples - ARM preexisting RG templates

Fixes# 3550
Description
This PR updates the TS samples' ARM template-with-preexisting-rg.json files to include the parameters and resources required to support MSI.
Detailed Changes

Added appType, tenantId, existingUserAssignedMSIName, and existingUserAssignedMSIResourceGroupName parameters.
Added the connection with the provided identity in case MSI app type is selected.

Testing
These images show some of the bots working after being deployed to Azure using the ARM templates.

Promoted to MS in PR# 3578


[mimalloc] update to 2.1.1 to build failure

Fixes #29839
Update mimalloc to the latest version 2.1.1 to fix upstream bug.

[x] Changes comply with the maintainer guide
[x] SHA512s are updated for each updated download
[ ] The "supports" clause reflects platforms that may be fixed by this new version
[ ] Any fixed CI baseline entries are removed from that file.
[ ] Any patches that are no longer applied are deleted from the port's directory.
[x] The version database is fixed by rerunning ./vcpkg x-add-version --all and committing the result.
[x] Only one version is added to each modified port's versions file.

All features are tested successfully in the following triplet:

x86-windows
x64-windows
x64-windows-static

It doesn't seem to require  vcpkg_copy_pdbs(). The pdbs files has been deketed? @JonLiu1993
CMake Warning at scripts/cmake/vcpkg_copy_pdbs.cmake:44 (message):
  Could not find a matching pdb file for:

      F:/test/vcpkg/test/mimalloc_x86-windows/bin/mimalloc-redirect32.dll
      F:/test/vcpkg/test/mimalloc_x86-windows/debug/bin/mimalloc-redirect32.dll

It doesn't seem to require vcpkg_copy_pdbs(). The pdbs files has been deketed? @JonLiu1993
CMake Warning at scripts/cmake/vcpkg_copy_pdbs.cmake:44 (message):
  Could not find a matching pdb file for:

      F:/test/vcpkg/test/mimalloc_x86-windows/bin/mimalloc-redirect32.dll
      F:/test/vcpkg/test/mimalloc_x86-windows/debug/bin/mimalloc-redirect32.dll

vcpkg_copy_pdbs() should be kept because there are other PDB files：


joint node position, help me please

I am writing a tool for a program that can load gltf models, this program doesn't use "end" "start" points for the bones, it just use one pivot point.
The problem is that the joints with the gltf exported positions (translation) as pivot point are incorrect for the program because its the head position of the bone not the tail. My question is how can I modify the addon to export the tail positions as the translation position of the joint node ? I didn't even found the line in the addon script where the position is exported or prepared.

Sorry guys, the problem was I didn't know that those position in the node are realtive to the joint's parent node position, so I just need to subtract from it to get the correct pivot :D


fixed CI

Looks like there isn't a babel-preset-es2015@7 yet.

Codecov Report

Merging #42 into master will not change coverage.
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  Misses         59       59

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  The details of the data do not matter a great deal, but do need the volume to ensure triggers, flow, and similar, have appropriate filters.
As a developer I want to generate data sets large enough to use most, or all, of the storage in a partial or full sandbox to QA build with large volumes.

This functionality may be handled, at least in part, with Paul's internal code for LDV generation. Our primary use case for the data generation tool is a midsize dataset generation--large enough that the dataset can cover interesting permutations, but not true LDV.

Snowfakery, and some other tools, can do this well right now, but lack examples and documentation to make it easy even for an expert to generate the data.
Likely this issue can be split into multiple issues one for each tool that we end up creating docs/samples for.


Delete getHedgewareDistribution.ts

Moved over to hedgeware repo... I'd propose to delete this file to reduce the chances of someone getting confused between the two versions. Also, this doesn't work (for me) right now

Ok, we'll fix it nonetheless and change names to generic distribution.

Also it still works you just need to run with the proper config:
yarn script scripts/edgeware/getHedgewareDistribution.ts

Ok cool, maybe rename to something generic then. I'm having a hard time finding stuff between the different repos or figuring out how to run stuff - probably just because full docs haven't been finished yet

Yep it’s renamed now to getQuadraticDistribution
On Fri, Jun 18, 2021 at 9:02 PM Raymond Zhong @.***>
wrote:

Ok cool, maybe rename to something generic then. I'm having a hard time
finding stuff between the different repos or figuring out how to run stuff

probably just because full docs haven't been finished yet

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fix(elixir): compiler diagnostics have iodata

Current dependencies on/for this PR:

main

PR #18   👈

This comment was auto-generated by Graphite.


Inline Loader inside of Modal is inverted

Expected Result
Non-inverted loader
Actual Result
Inverted loader
Version
0.71.2
Testcase
https://codepen.io/andv/pen/vJNdxy

This is ultimately a CSS issue and this repo only contains React components. Please direct further CSS issues to the main repo https://github.com/Semantic-Org/Semantic-UI.
BTW, issue about this problem has already exists Semantic-Org/Semantic-UI#4014. Also, see #879 for a workaround.


update to docs button and docs URL changes.

I am still learning Git and branches and forks and stuff like that. I know the initial commit on this branch was a while ago, and I am not sure if I made the changes correctly. As far as rebasing and all that goes. I'm trying to use VSC as much as I can, for everything.

I'm concerned that this branch has diverged too far from the main branch, which would make merging harder. It might be better to rebranch from main and replace the links again?

Yeah probably best. I'll do that.

Yeah probably best. I'll do that.

I'm reconsidering this now, perhaps I should just try to rebase the branch.


Added restricted file: .pgpass

Added .pgpass in the list of restricted files into rules/restricted-files.data

Hi @gwen001,
thanks for (your first!) contribution, and welcome :).
Could you add the commit from #2533 to this PR, and close that one? As I see, both modifications extend same file, so I think those can be in same request. If you're done, please close that one.
Thanks!

@gwen001 My apologies, but our switch back to the v4.0/dev branch closed all the PRs and it turns out it is impossible to reopen them. Could you send this PR again, but based on the v4.0/dev branch? My apologies! Learning new things about GitHub every day...

Done: https://github.com/coreruleset/coreruleset/pull/2560


Make session_start_time required

fix #354
Motivation
For a long time, we've had session_start_time be an optional value throughout our pipelines, with a default value of datetime(1970,1,1). However, it's becoming clear that as a best practice, this has potential to be completely missed by users which can result in an incorrect NWBFile uploaded to DANDI.
How to test the behavior?
To-Do:

[x] Need to add explicit tests and catches for the assertions for these changes.
[x] Need to fix everything this breaks by default (i.e., tests for interfaces that don't auto-fill this)

Checklist

[X] Have you checked our Contributing document?
[X] Have you ensured the PR description clearly describes the problem and solutions?
[X] Have you checked to ensure that there aren't other open or previously closed Pull Requests for the same change?
[X] If this PR fixes an issue, is the first line of the PR description fix #XXX where XXX is the issue number?

Finished debugging and adding tests; actual code changes are +11/-22, the extra code in the changelog is from all the places in the tests that now require session_start_time to be specified in place of relying on the default.


BatchDatabase::load_cargo fails on Windows

Currently BatchDatabase::load_cargo fails on Windows due to cargo not handling extended-length paths well, when either the current directory or manifest-path is an extended-length path (paths starting with \\?\).
See rust-lang/cargo#6198 for more details.
This causes both test_loading_rust_analyzer and cargo run --package ra_cli analysis-stats to fail.
The transform to an extended-length path on Windows is caused by canonicalize in:
https://github.com/rust-analyzer/rust-analyzer/blob/65266c644a31e6b321e5afb3c5a2ee75be76cb0c/crates/ra_batch/src/lib.rs#L98
The actual fail happens in:
https://github.com/rust-analyzer/rust-analyzer/blob/65266c644a31e6b321e5afb3c5a2ee75be76cb0c/crates/ra_project_model/src/cargo_workspace.rs#L124
Output from running test_loading_rust_analyzer:

$ cargo test -p ra_batch --lib
    Finished dev [unoptimized + debuginfo] target(s) in 1m 25s
     Running target\debug\deps\ra_batch-0f1c014d7cd7f7f9.exe

running 1 test
test tests::test_loading_rust_analyzer ... FAILED

failures:

---- tests::test_loading_rust_analyzer stdout ----
thread 'tests::test_loading_rust_analyzer' panicked at 'called
`Result::unwrap()` on an `Err` value: ErrorMessage { msg: "cargo metadata
failed: error during execution of `cargo metadata`: error: failed to read
`\\\\?\\C:\\Stuff\\rust-analyzer\\crates/*\\Cargo.toml`\n\nCaused by:\n The
filename, directory name, or volume label syntax is incorrect. (os error 123)\n"
}', src\libcore\result.rs:1009:5
note: Run with `RUST_BACKTRACE=1` for a backtrace.

failures:
    tests::test_loading_rust_analyzer

test result: FAILED. 0 passed; 1 failed; 0 ignored; 0 measured; 0 filtered out

error: test failed, to rerun pass '-p ra_batch --lib'

might be useful: https://gitlab.com/kornelski/dunce

might be useful: https://gitlab.com/kornelski/dunce

That indeed looks something that could fix this, at least temporarily until std gets a method for getting the absolute path without the extended-length path format.

actually, @flodiebold, why do we call canonicalize there in the first place? It usually is better to just use paths as is: the fewer modifications to the path we do, the higher is the change that paths will work correctly. Original path might not be canonical, but it usually "works".

The
        let local_roots = roots
            .into_iter()
            .filter(|r| vfs.root2path(*r).starts_with(&root))
            .map(vfs_root_to_id)
            .collect();

to find the local roots didn't work otherwise. There's probably a better way to do that, though...

I need to check this, but I think what Cargo does is that it absolutize the path (using canonicalized path to cargo.exe) and doesn't do canonicalization

yep, Cargo doesn't do canonicalization, it does only normalization: https://github.com/rust-lang/cargo/blob/2e4cfc2b7d43328b207879228a2ca7d427d188bb/src/cargo/util/paths.rs#L65

My suggested fix is to, first, try use the normalize function from Cargo (by just copy-pasting it), and then fallback to dunce.

Currently, normalize does not work in the cargo run -p ra_cli analysis-stats case, since the path there is given using ".". We could consider using similar method as in test_loading_rust_analyzer e.g.
https://github.com/rust-analyzer/rust-analyzer/blob/ebfa26658e9f65491e79f6853bb7c77030f5b0fe/crates/ra_batch/src/lib.rs#L136-L140

Could we join std::env::current_dir() to the path to make it absolute?

cargo run -p ra_cli analysis-stats does work with
let path = std::env::current_dir()?;
let (db, roots) = BatchDatabase::load_cargo(path)?;

In this case the call to normalize does nothing, both the test_loading_rust_analyzer and the cli invocation seem to pass without needing canonicalization, at least on windows.

So that means that inside load_cargo we can do std::env::current_dir()?.join(path)?

That does seem to work. In both cases.

Closed the old PR and created new one with the join fix #825


Fetch argument None has invalid type <class 'NoneType'>

Hi. I'm having a lot of trouble trying to get your function to work. I'm getting an error now that looks like it is coming from your deconvolution functions. Could you please let me know what you think the solution is?
Code I used:
is_loaded=tf_cnnvis.deconv_visualization(graph_or_path=tf.get_default_graph(),
                                         value_feed_dict={x_pl:x_batch, y_gt:targets_batch, is_training:False, valid_eval_accs:1000.0, valid_xent:1000.0},
                                         layers='r',
                                         path_logdir="c:/p17/logs/tf_cnnvis/flowers/1/logs",
                                         path_outdir="c:/p17/logs/tf_cnnvis/flowers/1/out")

Thanks.
INFO:tensorflow:Restoring parameters from model\tmp-model
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-13-41761d870143> in <module>()
      3                                          layers='r',
      4                                          path_logdir="c:/p17/logs/tf_cnnvis/flowers/1/logs",
----> 5                                          path_outdir="c:/p17/logs/tf_cnnvis/flowers/1/out")

C:\Users\username\Anaconda3\envs\tfGPU\lib\site-packages\tf_cnnvis-1.0.0-py3.5.egg\tf_cnnvis\tf_cnnvis.py in deconv_visualization(graph_or_path, value_feed_dict, input_tensor, layers, path_logdir, path_outdir)
    381 def deconv_visualization(graph_or_path, value_feed_dict, input_tensor = None, layers = 'r', path_logdir = './Log', path_outdir = "./Output"):
    382 	is_success = _get_visualization(graph_or_path, value_feed_dict, input_tensor = input_tensor, layers = layers, method = "deconv",
--> 383 		path_logdir = path_logdir, path_outdir = path_outdir)
    384         return is_success
    385 def deepdream_visualization(graph_or_path, value_feed_dict, layer, classes, input_tensor = None, path_logdir = './Log', path_outdir = "./Output"):

C:\Users\username\Anaconda3\envs\tfGPU\lib\site-packages\tf_cnnvis-1.0.0-py3.5.egg\tf_cnnvis\tf_cnnvis.py in _get_visualization(graph_or_path, value_feed_dict, input_tensor, layers, path_logdir, path_outdir, method)
    149                         elif layers != None and layers.lower() in dict_layer.keys():
    150                                 layer_type = dict_layer[layers.lower()]
--> 151                                 is_success = _visualization_by_layer_type(g, value_feed_dict, input_tensor, layer_type, method, path_logdir, path_outdir)
    152                         else:
    153                                 is_success = False

C:\Users\username\Anaconda3\envs\tfGPU\lib\site-packages\tf_cnnvis-1.0.0-py3.5.egg\tf_cnnvis\tf_cnnvis.py in _visualization_by_layer_type(graph, value_feed_dict, input_tensor, layer_type, method, path_logdir, path_outdir)
    202
    203         for layer in layers:
--> 204                 is_success = _visualization_by_layer_name(graph, value_feed_dict, input_tensor, layer, method, path_logdir, path_outdir)
    205         return is_success
    206

C:\Users\username\Anaconda3\envs\tfGPU\lib\site-packages\tf_cnnvis-1.0.0-py3.5.egg\tf_cnnvis\tf_cnnvis.py in _visualization_by_layer_name(graph, value_feed_dict, input_tensor, layer_name, method, path_logdir, path_outdir)
    263                         elif method == "deconv":
    264                                 # deconvolution
--> 265                                 results = _deconvolution(graph, sess, op_tensor, X, feed_dict)
    266                         elif method == "deepdream":
    267                                 # deepdream

C:\Users\username\Anaconda3\envs\tfGPU\lib\site-packages\tf_cnnvis-1.0.0-py3.5.egg\tf_cnnvis\tf_cnnvis.py in _deconvolution(graph, sess, op_tensor, X, feed_dict)
    310                                                 c += 1
    311                                 if c > 0:
--> 312                                         out.extend(sess.run(reconstruct[:c], feed_dict = feed_dict))
    313         return out
    314 def _deepdream(graph, sess, op_tensor, X, feed_dict, layer, path_outdir, path_logdir):

C:\Users\username\Anaconda3\envs\tfGPU\lib\site-packages\tensorflow\python\client\session.py in run(self, fetches, feed_dict, options, run_metadata)
    787     try:
    788       result = self._run(None, fetches, feed_dict, options_ptr,
--> 789                          run_metadata_ptr)
    790       if run_metadata:
    791         proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

C:\Users\username\Anaconda3\envs\tfGPU\lib\site-packages\tensorflow\python\client\session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
    982     # Create a fetch handler to take care of the structure of fetches.
    983     fetch_handler = _FetchHandler(
--> 984         self._graph, fetches, feed_dict_string, feed_handles=feed_handles)
    985
    986     # Run request and get response.

C:\Users\username\Anaconda3\envs\tfGPU\lib\site-packages\tensorflow\python\client\session.py in __init__(self, graph, fetches, feeds, feed_handles)
    408     """
    409     with graph.as_default():
--> 410       self._fetch_mapper = _FetchMapper.for_fetch(fetches)
    411     self._fetches = []
    412     self._targets = []

C:\Users\username\Anaconda3\envs\tfGPU\lib\site-packages\tensorflow\python\client\session.py in for_fetch(fetch)
    228     elif isinstance(fetch, (list, tuple)):
    229       # NOTE(touts): This is also the code path for namedtuples.
--> 230       return _ListFetchMapper(fetch)
    231     elif isinstance(fetch, dict):
    232       return _DictFetchMapper(fetch)

C:\Users\username\Anaconda3\envs\tfGPU\lib\site-packages\tensorflow\python\client\session.py in __init__(self, fetches)
    335     """
    336     self._fetch_type = type(fetches)
--> 337     self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
    338     self._unique_fetches, self._value_indices = _uniquify_fetches(self._mappers)
    339

C:\Users\username\Anaconda3\envs\tfGPU\lib\site-packages\tensorflow\python\client\session.py in <listcomp>(.0)
    335     """
    336     self._fetch_type = type(fetches)
--> 337     self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
    338     self._unique_fetches, self._value_indices = _uniquify_fetches(self._mappers)
    339

C:\Users\username\Anaconda3\envs\tfGPU\lib\site-packages\tensorflow\python\client\session.py in for_fetch(fetch)
    225     if fetch is None:
    226       raise TypeError('Fetch argument %r has invalid type %r' %
--> 227                       (fetch, type(fetch)))
    228     elif isinstance(fetch, (list, tuple)):
    229       # NOTE(touts): This is also the code path for namedtuples.

TypeError: Fetch argument None has invalid type <class 'NoneType'>

From the error message it seems that the feed_dict going in has some element of type "None". Could you please recheck the value_feed_dict that you're passing in?

I've had a check and that feed_dict value is exactly what is going into one of my previous .run() statements. In fact it's just copied over from my testing code block which functions fine. I've even tried putting the values into the feed_dict manually and it seems to fail. Anything that tf_cnnvis does behind the scenes that means that even though it works perfectly fine within plain Tensorflow, when tf_cnnvis has a go it would fail?

There's a transfer of feed_dict elements happening inside tf_cnnvis. We cannot think of any case in which this would lead to the change of dictionary element which you  to None, but still yours could be a corner case we overlooked. Could you add a print statement in tf_cnnvis/utils.py (In the function parse_tensors_dict  could you add 2 lines print(X_in) and print(feed_dict[X_in]) at line 84 of that function and post the results- it would help us debug what is causing the error.
Or, a simpler solution, if you don't mind- just send us the graph/ feed_dict and we'll check things at our end.

Hmm. So it prints out my image data like so:
Tensor("ImageIn:0", shape=(?, 224, 224, 3), dtype=float32)
[[[[ 0.          0.          0.        ]
   [ 0.          0.          0.        ]
   [ 0.          0.          0.        ]
   ...,
   [ 0.          0.          0.        ]
   [ 0.          0.          0.        ]
   [ 0.          0.          0.        ]]

  [[ 0.          0.          0.        ]
   [ 0.          0.          0.        ]
   [ 0.          0.          0.        ]
   ...,
   [ 0.          0.          0.        ]
   [ 0.          0.          0.        ]
   [ 0.          0.          0.        ]]

  [[ 0.          0.          0.        ]
   [ 0.          0.          0.        ]
   [ 0.          0.          0.        ]
   ...,
   [ 0.          0.          0.        ]
   [ 0.          0.          0.        ]
   [ 0.          0.          0.        ]]

  ...,
  [[ 0.          0.          0.        ]
   [ 0.          0.          0.        ]
   [ 0.          0.          0.        ]
   ...,
   [ 0.09019608  0.14117648  0.09019608]
   [ 0.07450981  0.12156863  0.08235294]
   [ 0.07843138  0.1254902   0.08627451]]

  [[ 0.          0.          0.        ]
   [ 0.          0.          0.        ]
   [ 0.          0.          0.        ]
   ...,
   [ 0.08627451  0.1254902   0.09019608]
   [ 0.07058824  0.11764706  0.08235294]
   [ 0.07450981  0.11764706  0.08627451]]

  [[ 0.          0.          0.        ]
   [ 0.          0.          0.        ]
   [ 0.          0.          0.        ]
   ...,
   [ 0.08235294  0.12156863  0.09019608]
   [ 0.07058824  0.11372549  0.08235294]
   [ 0.07450981  0.11764706  0.08627451]]]]

And then returns the error from my original problem.  But I know that stuff is being sent to it.

No, actually it's printing out both - X_in is the Tensor("ImageIn:0", shape=(?, 224, 224, 3), dtype=float32) and feed_dict[X_in] is the array shown below. So, we can be sure that there are no issues there- and that is the only element we're modifying a bit.
So, then one of the other feed_dict elements must be becoming None.
Since you're not getting any issues running your test code, all the inputs at your end must be fine. I'm still scratching my head on how this error is popping up then. BTW, does the activation visualization case work for you?

@falaktheoptimist I can reproduce the issue and have the same problem.
I have implemented a standard CNN classifying MNIST numbers. I call tf_cnnvis in the following function:
def visualize_layers(feed_dict):
    # activation visualization
    layers = ['r', 'p', 'c']    # r = ReLU, p = pooling, c = convolutional layers

    start = time.time()
    is_success = activation_visualization(graph_or_path=tf.get_default_graph(), value_feed_dict=feed_dict,
                                          layers=layers, path_logdir="C:/Users/bucpau/PycharmProjects/Academy/Logs/", path_outdir="C:/Users/bucpau/PycharmProjects/Academy/Visualization/")
    start = time.time() - start
    print("Total time for neuron activation visualization : {} Success: {}".format(start, is_success))
    # deconv visualization
    layers = ['r', 'p', 'c']

    start = time.time()
    is_success = deconv_visualization(graph_or_path=tf.get_default_graph(), value_feed_dict=feed_dict,
                                      layers=layers, path_logdir="C:/Users/bucpau/PycharmProjects/Academy/Logs/", path_outdir="C:/Users/bucpau/PycharmProjects/Academy/Visualization/")
    start = time.time() - start
    print("Total time for deconvolution visualization: {} Success: {}".format(start, is_success))
    return None

The feed_dict provided as an input is the same I use for testing, but with only one image, see here:
    random_representation = mnist_data.test.next_batch(1)
    ran_x = preprocess_batch(random_representation[0], mean, std)
    ran_y = random_representation[1]
    test_dict = {x: ran_x, y: ran_y, is_training: False, keep_probability: 1}
    visualize_layers(test_dict)

The exact error reads:
Traceback (most recent call last):
  File "C:/Users/bucpau/PycharmProjects/Academy/CNN_MNIST.py", line 139, in <module>
    main()
  File "C:/Users/bucpau/PycharmProjects/Academy/CNN_MNIST.py", line 136, in main
    visualize_layers(test_dict)
  File "C:/Users/bucpau/PycharmProjects/Academy/CNN_MNIST.py", line 68, in visualize_layers
    layers=layers, path_logdir="C:/Users/bucpau/PycharmProjects/Academy/Logs/", path_outdir="C:/Users/bucpau/PycharmProjects/Academy/Visualization/")
  File "C:\ProgramData\Anaconda3\lib\site-packages\tf_cnnvis-1.0.0-py3.6.egg\tf_cnnvis\tf_cnnvis.py", line 379, in deconv_visualization
  File "C:\ProgramData\Anaconda3\lib\site-packages\tf_cnnvis-1.0.0-py3.6.egg\tf_cnnvis\tf_cnnvis.py", line 141, in _get_visualization
  File "C:\ProgramData\Anaconda3\lib\site-packages\tf_cnnvis-1.0.0-py3.6.egg\tf_cnnvis\tf_cnnvis.py", line 200, in _visualization_by_layer_type
  File "C:\ProgramData\Anaconda3\lib\site-packages\tf_cnnvis-1.0.0-py3.6.egg\tf_cnnvis\tf_cnnvis.py", line 261, in _visualization_by_layer_name
  File "C:\ProgramData\Anaconda3\lib\site-packages\tf_cnnvis-1.0.0-py3.6.egg\tf_cnnvis\tf_cnnvis.py", line 308, in _deconvolution
  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 895, in run
    run_metadata_ptr)
  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 1109, in _run
    self._graph, fetches, feed_dict_tensor, feed_handles=feed_handles)
  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 413, in __init__
    self._fetch_mapper = _FetchMapper.for_fetch(fetches)
  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 233, in for_fetch
    return _ListFetchMapper(fetch)
  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 340, in __init__
    self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 340, in <listcomp>
    self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
  File "C:\ProgramData\Anaconda3\lib\site-packages\tensorflow\python\client\session.py", line 230, in for_fetch
    (fetch, type(fetch)))
TypeError: Fetch argument None has invalid type <class 'NoneType'>

What wonders me is that the activation visualization works perfectly, with the same arguments. Any ideas on what is happening here?

We were finally able to replicate when this was happening. It occurs for the flattened input case. Passing in the input_tensor argument as the reshaped (or unflattned) tensor should resolve this.

I've been so busy i've not had time to look at this since filing the issue! Do you mean feed the image as a rectangle instead of a flattened tensor? Because i'm pretty sure that's what I was doing!

@falaktheoptimist I've tried it out. Here for the deconvolution it works!

@jubjamie Then we have yet to figure out your particular corner case. I see from your output above that it was an RGB image and not a flattened one.

@falaktheoptimist  how to reshaped the input_tensor?

Use tf.reshape

@falaktheoptimist  below is my code, i use  InceptionResnetV2 model . the activation visualization works perfectly,but deconv_visualization don''t work (Fetch argument None has invalid type <class 'NoneType'>). Can you help me ?
`import os
import sys
import tensorflow as tf
import urllib2
from datasets import imagenet
from nets import vgg
from nets import inception_v4
from nets import inception_resnet_v2
from preprocessing import vgg_preprocessing
from preprocessing import inception_preprocessing
import time
import copy
import h5py
import numpy as np
from scipy.misc import imread, imresize
from tf_cnnvis import *
def get_img(src, img_size=False):
img = imread(src, mode='RGB') # misc.imresize(, (256, 256, 3))
if not (len(img.shape) == 3 and img.shape[2] == 3):
img = np.dstack((img,img,img))
if img_size != False:
img = imresize(img, img_size)
return img
checkpoints_dir = '/home/tfs/workspace/train_inception_resnet'
checkpoint = 'model.ckpt-326664'
slim = tf.contrib.slim
#image_size=vgg.vgg_16.default_image_size
image_size=inception_v4.inception_v4.default_image_size
with tf.Graph().as_default():
url=("http://img6.cache.netease.com/photo/0001/2017-05-12/CK8G865719BR0001.jpg")
if (len(sys.argv) > 1):
url = (sys.argv[1])
print(url)
image_string=urllib2.urlopen(url).read()
image = tf.image.decode_jpeg(image_string,channels=3)
processed_image=inception_preprocessing.preprocess_image(image,image_size,image_size,is_training=False)
processed_images=tf.expand_dims(processed_image,0)
with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope()):
logits,_=inception_resnet_v2.inception_resnet_v2(processed_images,num_classes=6,is_training=False)
probabilities = tf.nn.softmax(logits)

init_fn = slim.assign_from_checkpoint_fn(os.path.join(checkpoints_dir,checkpoint),slim.get_model_variables('InceptionResnetV2'))

with tf.Session() as sess:
  init_fn(sess)

  X = tf.placeholder(tf.float32, shape = [None, image_size, image_size, 3]) # placeholder for input images

  im = get_img(os.path.join("./sample_images", "images.jpg"), (image_size,image_size,3)).astype(np.float32)
  im = np.expand_dims(im, axis = 0)

  # deconv visualization
  layers = ['r', 'p', 'c']

  start = time.time()
  is_success = deconv_visualization(graph_or_path = tf.get_default_graph(), value_feed_dict = {X : im},
                              layers=layers, path_logdir="./Log/AlexNet", path_outdir="./Output/AlexNet")
  start = time.time() - start
  print("Total Time = %f" % (start))

res=slim.get_model_variables()`

Same error:
with tf.Session() as sess:
    X = tf.placeholder(tf.float32, shape = [None, 400, 600, 3])
    input_tensor = {X: np.array([image_data])}
    vis.deconv_visualization(sess.graph, input_tensor, sess.graph.get_tensor_by_name('sab/DecodeJpeg:0'), layers=layers)

I use the inception_v3 network, after retraining. Activation Visualization works,  though it shows a whole lot of pandas, no idea if retraining just failed or if the code is broken. Deconv fails.

Hello @sconeyard,
Can you please change your code as below and post the error here?
with tf.Session() as sess:
    X = tf.placeholder(tf.float32, shape = [None, 400, 600, 3])
    input_tensor = {X: np.array([image_data])}
    vis.deconv_visualization(sess.graph, input_tensor, X, layers=layers)

Hiii @fqfqiumou,
Can you make change as following and re-run your experiment?
Replace this
is_success = deconv_visualization(graph_or_path = tf.get_default_graph(), value_feed_dict = {X : im}, layers=layers, path_logdir="./Log/AlexNet", path_outdir="./Output/AlexNet")

with this
is_success = deconv_visualization(graph_or_path = tf.get_default_graph(), value_feed_dict = {X : im}, input_tensor=processed_images, layers=layers, path_logdir="./Log/AlexNet", path_outdir="./Output/AlexNet")

Thank You

@BhagyeshVikani ,
Thank you very much , it works.     because i use slim , so don't need to feed value_feed_dict,   just pass the image to input_tensor.

Hello,
Clarification regarding the input_tensor argument in all three visualization function:
input_tensor (tf.tensor object (Default = None)) – tf.tensor (input tensor to the model - where images enter into the models) Note: This is not a standalone tensor/placeholder separate from the model
Thank You

Closing this now, as the issue seems to have been resolved. Reopen if needed.

@BhagyeshVikani  I have this trouble. I followed all of the answers here and still could not get it working.
Here is my scenario.
I have a class that opens a model created from estimator API. It is not a frozen graph. It is from .meta, .index, checkpoint, .data-xx files. The CNN input shape is [864,480]
I load it in the class constructor.
def init(self,frozen_model_filename):
tf.reset_default_graph()
self.session = tf.Session()
self.input_placeholder = tf.placeholder(tf.float32, shape=[ 1,864,480,3])
self.model = self.load_model_from_meta(vad_model_file_fullpath)
def load_model_from_meta(self, vad_model_file_name):
    saver = tf.train.import_meta_graph(vad_model_file_name)
    saver.restore(self.session,tf.train.latest_checkpoint(vad_model_dir))
    print("restore successful")

def visualize(self):
im = np.expand_dims(imresize(imresize(imread(os.path.join("sample_images", "flicker1.jpg")), (256,256)) - mean, (864, 480)), axis = 0)
   layers = ['r', 'p', 'c']
    start = time.time()
    print("before session")
    self.session.run(tf.global_variables_initializer())
    is_success = activation_visualization(sess_graph_path = self.session, value_feed_dict = {self.input_placeholder : im},
                                      layers=layers, path_logdir=os.path.join("Log","MyModelfromMeta"),
                                      path_outdir=os.path.join("Output","MyModelfromMeta"))

    layers = ['r', 'p', 'c']
    is_success = deconv_visualization(sess_graph_path = self.session, value_feed_dict = {self.input_placeholder : im},
            #                          input_tensor="softmax_tensor", #Tried this with and without input_tensor
                                  layers=layers, path_logdir=os.path.join("Log","MyModelfromMeta"),
                                  path_outdir=os.path.join("Output","MyModelfromMeta"))

#main code
mean = np.load("./img_mean.npy").transpose((1, 2, 0)) # load mean image of imagenet dataset
Myclassifier = MyModel(unfrozen_model_Metafile_name)
#print(model.predict(images))
Myclassifier.visualize()
Like with others, it works for activation_visualization API, but fails with deconv_visualization(). It gives the same error - TypeError: Fetch argument None has invalid type <class 'NoneType'>
Please suggest the way out.

@ishwara-bhat The above mentioned solution worled for me too. Could you specify what exactly is the "softmax tensor"? Secondly, you might want to check your reconstruction tensor as well.

        for epoch in range(training_epochs):
            avg_cost = 0.
            total_batch = int(mnist.train.num_examples/batch_size)

            for i in range(total_batch):

                minibatch_x, minibatch_y = mnist.train.next_batch(batch_size)
                sess.run(train_op, feed_dict={x: minibatch_x, y: minibatch_y})
                                    # Compute average loss

                avg_cost += sess.run(cost, feed_dict={x: minibatch_x, y: minibatch_y})/total_batch
                # Display Logs per epOch step

            if  epoch % display_step == 0:
                print("Epoch:", '%04d' % (epoch+1), "cost =", "{:.9f}".format(avg_cost))
                accuracy = sess.run(eval_op, feed_dict={x: mnist.validation.images, y: mnist.validation.labels})
                print("Validation Error:", (1 - accuracy))

                summary_str = sess.run(summary_op, feed_dict={x: minibatch_x, y: minibatch_y})
                summary_writer.add_summary(summary_str, sess.run(global_step))

                saver.save(sess, "mlp_logs/model-checkpoint", global_step=global_step)
                print("Optimization Finished!")
                accuracy = sess.run(eval_op, feed_dict={x: mnist.test.images, y: mnist.test.labels})
                print("Test Accuracy: ", accuracy)

Here also the same error is coming can someone help
(fetch, type(fetch)))
TypeError: Fetch argument None has invalid type <class 'NoneType'>


Update adopters contact for IBM

Updating the IBM adopters main contact to @njhill

@yuzisun -- should I use this opportunity to sort the list by company name?

@yuzisun -- should I use this opportunity to sort the list by company name?

We can create another PR to fix the ordering.

/lgtm
/approve


mam/v2: sponge - replace state with flex_trits

Replace s field with state field which is of flex_trit_t* type rather than trit_t*

W'll be using trit_t for state


BUG || [BLOCKER] Update Case API failing

DPG-DEV
While Filing Case Update API failing at the last.


ability to use search on AMP

The “Search” functionality is something that I would love to get.
https://wordpress.org/support/topic/which-one-feature-will-make-this-plugin-perfect-for-you/
reported by @preetji

#285


Fixing the container vulnerability by updating maven dependency

What is the purpose of the change
This Pull request updates the SnakeYaml Maven dependency from 1.33 to 2.0 to fix the container vulnerability CVE-2022-1471
Brief change log

Updating the SnakeYaml Maven dependency in the pom.xml
The current version of the SnakeYaml: 1.33 and the Updated version: 2.0

Verifying this change
This change is a just maven dependency update doesn't make impact
Does this pull request potentially affect one of the following parts:

Dependencies (does it add or upgrade a dependency): (yes)
The public API, i.e., is any changes to the CustomResourceDescriptors: (no)
Core observer or reconciler logic that is regularly executed: (no)

Documentation

Does this pull request introduce a new feature? (no)

There seem to be some failing tests, also https://github.com/apache/flink-kubernetes-operator/blob/main/flink-kubernetes-operator/src/main/resources/META-INF/NOTICE should be updated.

Updated the NOTICE file as well to make test pass

Sure, will do it in local

Hi @kingsathurthi , sorry to bother you! If you are no longer following up, please close this issue and I will take over the management of it.

Hi Yang, I'm still active on this is issue, please allow me some time to close this.

There are still more compatibility-related issues:

@yangjf2019 shall we connect and discuss, Please share your slack id or find mine @satz

And, please refer to the contribution specification , please open a work order on jira and associate the work order to this issue.

@yangjf2019 shall we connect and discuss, Please share your slack id or find mine @satz

Or we can discuss it by email.

Sure will follow the contribution specifications. Where do I find your mail id

Yes, I'll take it over. If you can, please close this issue.


[BUG] Open data window when is not requested

Describe the bug
When i need to open the edit window for a data item, and close it without save, app open another window for copy/edit/send action
To Reproduce
Steps to reproduce the behavior:

Go to Process data settings
Click on edit icon any data record
Click on Close button
See closing wiindow and then opening new window (for copy/edit/send action )

/cib

Branch 101-_BUG_Open_data_window_when_is_not_requested created!


Flash Player

Prerequisites

[ ] Can you reproduce the problem with Debug -> Reload Without Extensions?
[ X] Did you perform a cursory search to see if your bug or enhancement is already reported?
[X ] Did you read the Troubleshooting guide?

For more information on how to write a good bug report read here
For more information on how to contribute read here
Description
Pixlr extension gives me "Adobe Flash Player Not Installed!"
I have flash player installed and up to date.
Steps to Reproduce

Install Brackets
Install Pixlr
Run Pixlr

Expected behavior: Pixlr runs.
Actual behavior: Error message "Adobe Flash Player Not Installed!"
Versions
Windows 7 Ultimate, 64 bit service pack 1
Bracket 1.8 build 1/8/0-17108
Flash Player 24,0,0,221 installed
Please include the OS and what version of the OS you're running.
Please include the version of Brackets. You can find it under Help -> About Brackets (Windows and Linux) or Brackets -> About Brackets (macOS)

You should file an issue https://github.com/dnbard/brackets-pixlr
Closing as an extension problem


Adds waitForAnimate checks

This prevents further slide transitions whilst there is an animation. It fixes draggable breaking on mobile for me.
cc @bettytran @flipactual @robcolburn

:+1: Tested this and it seems to fix the issue!


Twap: api testing

Background
We need to think and add specific cases for twap api testing, namely GetArithmeticTwap and GetArithmeticTwapToNow
Suggested Design & Acceptance Criteria

[ ] Finish tests in api_test.go with different twap scenarios in a table driven test

closing this issue seeing that the twap wip branch has updated test cases!
A note that we still need tests for GetArithmeticTwapToNow


Navigation between nav graphs

Hi, #helpneeded
just want to ask you if it is possible to navigate between nav graphs. In tivi project you have root, discover, following, watched and search nav graph. Then bottom navigation item has it own nav graph.
Is it possible to navigate from discover nav graph (or Discover tab) to watched nav graph (or Watched tab).
Like in Discover tab, there is a button and onClick you will be redirected to Watched tab.  Maybe deeplinks? Or is there any other way?
Thanks,
Vlado

Hi 👋
I’m not 100% sure that I got the question but if I did then the answer is yes. You can freely navigate to different nav graphs with no hurdle.


🪲 1.5.1 breaking change (maybe)

Describe the bug
I get this error when using version 1.5.1
{
    "status": "Failed",
    "error": {
        "code": "DeploymentFailed",
        "target": "/providers/Microsoft.Management/managementGroups/xenia-corp/providers/Microsoft.Resources/deployments/shared-services",
        "message": "At least one resource deployment operation failed. Please list deployment operations for details. Please see https://aka.ms/arm-deployment-operations for usage details.",
        "details": [
            {
                "code": "ResourceDeploymentFailure",
                "target": "/providers/Microsoft.Management/managementGroups/xenia-corp/providers/Microsoft.Resources/deployments/lz-vend-sub-res-create-shared-services-i7ug2kbzlb7vc",
                "message": "The resource write operation failed to complete successfully, because it reached terminal provisioning state 'Failed'.",
                "details": [
                    {
                        "code": "DeploymentOutputEvaluationFailed",
                        "target": "/providers/Microsoft.Management/managementGroups/xenia-corp/providers/Microsoft.Resources/deployments/lz-vend-sub-res-create-shared-services-i7ug2kbzlb7vc",
                        "message": "Unable to evaluate template outputs: 'failedFeatures'. Please see error details and deployment operations. Please see https://aka.ms/arm-common-errors for usage details.",
                        "details": [
                            {
                                "code": "DeploymentOutputEvaluationFailed",
                                "target": "failedFeatures",
                                "message": "The template output 'failedFeatures' is not valid: The language expression property 'failedFeaturesRegistrations' doesn't exist, available properties are 'failedProvidersRegistrations'.."
                            }
                        ]
                    }
                ]
            }
        ]
    }
}

To Reproduce
Steps to reproduce the behaviour:
targetScope = 'managementGroup'

module subscriptionmod 'br/public:lz/sub-vending:1.5.1' = {
  name: '<my-name>'
  params: {
    subscriptionAliasEnabled: true
    subscriptionBillingScope: '<my billing scope>'
    subscriptionAliasName: '<my-name>'
    subscriptionDisplayName: '<my-name>'
    subscriptionWorkload: 'Production'
    subscriptionManagementGroupAssociationEnabled: true
    subscriptionManagementGroupId: '<my-mg>'
  }
}

results in above error
module subscriptionmod 'br/public:lz/sub-vending:1.4.1' = {
  name: '<my-name>'
  params: {
    subscriptionAliasEnabled: true
    subscriptionBillingScope: '<my billing scope>'
    subscriptionAliasName: '<my-name>'
    subscriptionDisplayName: '<my-name>'
    subscriptionWorkload: 'Production'
    subscriptionManagementGroupAssociationEnabled: true
    subscriptionManagementGroupId: '<my-mg>'
  }
}

results in successful deployment
Additional context
I'm happy to try and look into this one myself

Thanks for raising @tssdavey.
Can you confirm what bicep version you have installed? Are you on the latest?
az bicep version
bicep --version

Note it can be installed side-by-side so you need to check and ensure both are up-to-date

0.24.24

@tssdavey Thanks for submitting this issue, it should be fixed in the assigned PR.

I am also seeing this issue.

I am having the same issue. The sooner this PR can fix the better.

We are working to merge this asap. Not to get you blocked, you can just replace Invoke-RegisterSubscriptionResourceProviders.ps1 with the version in the PR to get this resolved.

For what it's worth I just reverted to using v1.4.1 of the module and it works a-ok.

Still having the issue, is this module still maintained since the move to AVM ?

This is indeed maintained. We are so close to getting the AVM module out with this fix in.
Cc: @sebassem

Any ETA when the branch will be merged?

ETA is mid-ext week, we are pending final reviews from the AVM team.

we have finally published the AVM module for sub-vending, thanks for your patience. Please review the announcement here on how to move forward.


Type consts/statics

We currently don't handle consts/statics in type inference at all. This has two parts: We need to use the declared type of the const when referring to it from another location, and we need to run type inference on the bodies of consts.
The first part involves roughly the following steps, I think:

add a ConstSignature analogous to FnSignature that contains the name of the const and the ra_hir::TypeRef for the type, and add a query which constructs this (and a method on Const which calls it, similar to Function::signature). The reason for this is that we don't want to directly refer to the AST during type inference, as the AST changes every time the file is edited in any way. Having this intermediate query means we only need to redo type inference when the type itself is changed.
add a resolver method on Const similar to Function::resolver (but without handling generic params)
in ty/lower.rs, add Const to TypableDef, and implement type_for_def for consts by getting the const signature and turning the TypeRef into a Ty using Ty::from_hir (for the values namespace; consts don't exist in the types namespace)
add tests for this in ty/tests.rs

I think statics will work the same way (and can reuse ConstSignature), so it makes sense to do them at the same time.
After that, we'll want to run type inference on const bodies; I think this will involve introducing a DefWithBody enum containing Function, Const and Static, and changing a bunch of places that currently use Function to use that instead (i.e. the body_syntax_mapping, body_hir, expr_scopes and infer queries). But first things first :smile:

I'll try this, I guess now we can add associated consts as well ?

Yes, I think that will just be a matter of a few lines here.

What does const bodies mean in this context? Something like?
const GLOBAL_CONST: u32 = {
    const IS: u32 = 35;
    const IC: u32 = 63;
    IS + IC + 14
};

Yes, or even just
const X: u32 = {
    let x = 35;
    let y = 63;
    x + y + 14
};

. In general, the value of a constant can be any expression grammar-wise; it's just that rust disallows a lot of operations currently in a const context, but we don't really need to deal with that for now. (The above code becomes legal with Rust 1.33, I think.)
Someone might for example write
const X: u32 = 123u32.count_ones();

, which compiles in current beta, and reasonably expect code completion to work there.
This isn't really high-priority though, since const bodies are usually pretty small :smile:

What about statics ? They are somewhat similar to consts when it comes to inference, right?
I guess inferring things in their bodies is fine, even though if it may not be allowed to compile ?

I think statics are basically the same when it comes to types.
Someday we'll maybe want to do const checking, and e.g. only complete const methods in const context, but the rules about what's allowed in consts are changing a lot at the moment so it doesn't make sense to put a lot of work into that, I think. In any case, type inference isn't different in const bodies vs. other expressions as far as I understand; const checking is just another step on top.


Fix 4 security vulnerabilities

As discussed with @mistercrunch privately, here are patches for 4 different security vulnerabilities. The fixed vulnerabilities are:

Code execution via yaml.load (fixed in 7e949ee)
Clickjacking to SQL execution in SQLLab (fixed in f113d2b)
XSS via chart descriptions (fixed in b6fcc22)
XSS via javascript link handler in markown (fixed in b6fcc22)

hi, would you consider requesting a CVE for those vulnerabilities to inform users ?


404 errors with all "Install the app" download links on web UI

Version: 3.0.0
As the title says, all web UI links to app installers throw 404 errors.  I first noticed this while trying to download the latest .deb, but it looks like all installer links point to an unavailable 3.0.1 version.
I found that the .deb link worked after changing web_linux-amd64-3.0.1.deb to web_linux-amd64-3.0.0.deb at the end of the URL, so I'm sure the other links can be fixed in the same way.

Solved, thank you for noticing...


[ar]: Translated new keys: Log In, Log Out and Sign In

The machine translation will work tonight, and tomorrow I will release a new version.
شكراً

Пожалуйста


[API] Issue Search Add filter for MilestoneNames

close #9851

Codecov Report

Merging #16173 (540e2d9) into main (1ec9e90) will increase coverage by 0.01%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##             main   #16173      +/-   ##
==========================================
+ Coverage   44.57%   44.58%   +0.01%
==========================================
  Files         700      700
  Lines       82867    82877      +10
==========================================
+ Hits        36940    36954      +14
+ Misses      39936    39933       -3
+ Partials     5991     5990       -1

Impacted Files
Coverage Δ

models/issue.go
58.63% <100.00%> (+0.16%)
:arrow_up:

routers/api/v1/repo/issue.go
48.61% <100.00%> (+0.71%)
:arrow_up:

modules/charset/charset.go
75.75% <0.00%> (+4.04%)
:arrow_up:

Continue to review full report at Codecov.

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S330 Cameras not showing up in Homekit

What happened?
S330 Cameras are not showing up in Homekit and are not configurable, only ignorable.
Device Type
other (UnSupported)
Plugin Version
v2.0.x (Supported)
HomeBridge Version
v1.4.x (Supported)
NodeJS Version
v16 (Supported)
Operating System
Debian 10+ (Supported)
Relevant log output
I have no logging

config
"username"<EMAIL_ADDRESS>            "password": "xxxxxxxxxx",
            "country": "NL",
            "deviceName": "",
            "platform": "EufySecurity",
            "cameras": [
                {
                    "serialNumber": "T8160P2122451F27",
                    "rtsp": false,
                    "talkback": false
                },
                {
                    "serialNumber": "T8210P6422411FF8",
                    "rtsp": false,
                    "enableCamera": true,
                    "useCachedLocalLivestream": true,
                    "videoConfig": {
                        "audio": true,
                        "audioSampleRate": 0
                    },
                    "talkback": true,
                    "immediateRingNotificationWithoutSnapshot": false,
                    "delayCameraSnapshot": true,
                    "snapshotHandlingMethod": 2,
                    "refreshSnapshotIntervalMinutes": 30
                },
                {
                    "serialNumber": "T8160P21224521B5",
                    "rtsp": false,
                    "talkback": false
                },
                {
                    "serialNumber": "T8410P42224801EB",
                    "useCachedLocalLivestream": true,
                    "rtsp": false,
                    "videoConfig": {
                        "audio": true,
                        "audioSampleRate": 0
                    },
                    "talkback": true,
                    "snapshotHandlingMethod": 2,
                    "delayCameraSnapshot": true,
                    "refreshSnapshotIntervalMinutes": 30
                }
            ],
            "_bridge": {
                "username": "0E:09:BD:51:1B:29",
                "port": 58032
            },
            "ignoreStations": [],
            "ignoreDevices": [],
            "cleanCache": true
        }
    ]
}

Same issue:
MacOS 13.3.1
Homebridge 1.6.1
Node.js 18.16.0
Plugin 2.1.7

Same issue:
Ubuntu: 20.04.5
Homebridge: 1.6.0
Node.js: 18.13.0
Plugin: 2.1.7

For anyone still having issues with s330 compatibility - install the previous vs of Eufy Security Homebridge app. It's stable on the 2.2(beta) but not on the current one, for some reason. I'd run that, in the interim, until they fix whatever bug caused the upset in the new version.

@jamaalmck which beta version of 2.2 did you try?
I tried beta.19:
npm install<EMAIL_ADDRESS>
But no cameras for me with the following settings:
{
    "platform": "EufySecurity",
    "username": "x",
    "password": "x",
    "country": "NL",
    "cameras": [
        {
            "serialNumber": "x",
            "rtsp": false,
            "talkback": false
        }
    ],
    "_bridge": {
        "name": "Homebridge Eufy cameras",
        "username": "x",
        "port": 51945,
        "pin": "x",
        "advertiser": "ciao"
    }
}

@jamaalmck which beta version of 2.2 did you try?
I tried beta.19:
npm install<EMAIL_ADDRESS>
But no cameras for me with the following settings:
{
    "platform": "EufySecurity",
    "username": "x",
    "password": "x",
    "country": "NL",
    "cameras": [
        {
            "serialNumber": "x",
            "rtsp": false,
            "talkback": false
        }
    ],
    "_bridge": {
        "name": "Homebridge Eufy cameras",
        "username": "x",
        "port": 51945,
        "pin": "x",
        "advertiser": "ciao"
    }
}

I'm on Beta 19, as well. I currently have everything on a child bridge, and all Cameras are unbridged and added separately through HomeKit. Unbridging them should be at the bottom of each camera's settings, right above Snapshot Behavior.
Child Bridges are located on the plugin page, just hit the wrench icon, bridge settings, turn on child bridges and reset. Hopefully that works for you.

@jamaalmck thanks a lot for your reply and help. I did everything you and the docs describe without luck. I'm on Beta 19 with this config:
        {
            "platform": "EufySecurity",
            "username": "x",
            "password": "x",
            "country": "NL",
            "cameras": [
                {
                    "serialNumber": "x",
                    "unbridge": true,
                    "rtsp": false,
                    "talkback": false,
                    "enableCamera": "true"
                }
            ],
            "_bridge": {
                "name": "Homebridge Eufy cameras",
                "username": "x",
                "port": 51945,
                "pin": "x",
                "advertiser": "ciao"
            }
        }

Can you please share your config?

@jamaalmck thanks a lot for your reply and help. I did everything you and the docs describe without luck. I'm on Beta 19 with this config:
        {
            "platform": "EufySecurity",
            "username": "x",
            "password": "x",
            "country": "NL",
            "cameras": [
                {
                    "serialNumber": "x",
                    "unbridge": true,
                    "rtsp": false,
                    "talkback": false,
                    "enableCamera": "true"
                }
            ],
            "_bridge": {
                "name": "Homebridge Eufy cameras",
                "username": "x",
                "port": 51945,
                "pin": "x",
                "advertiser": "ciao"
            }
        }

Can you please share your config?

That's Odd. Is it showing up as Not Responding? Any errors in your log?
Config:
{
"bridge": {
"name": "Homebridge ABA3",
"username": "x",
"port": 51445,
"pin": "x",
"advertiser": "bonjour-hap"
},
"accessories": [],
"platforms": [
{
"name": "Config",
"port": 8581,
"platform": "config"
},
{
"username": "x",
"password": "x",
"country": "US",
"deviceName": "",
"platform": "EufySecurity",
"ignoreStations": [
"T8410P20204203E1",
"T8410P202042327E"
],
"cameras": [
{
"serialNumber": "x",
"hsv": true,
"hsvRecordingDuration": 90,
"hsvConfig": {},
"snapshotHandlingMethod": 3,
"enableCamera": "true",
"enableButton": false,
"motionButton": false,
"unbridge": true,
"videoConfig": {
"audio": false
},
"talkback": false,
"talkbackChannels": 1
},
{
"serialNumber": "x",
"hsv": true,
"hsvRecordingDuration": 90,
"hsvConfig": {},
"snapshotHandlingMethod": 3,
"enableCamera": "true",
"unbridge": true,
"enableButton": false,
"motionButton": false
},
{
"serialNumber": "x",
"hsv": true,
"hsvRecordingDuration": 90,
"hsvConfig": {},
"snapshotHandlingMethod": 3,
"enableCamera": "true",
"videoConfig": {
"audio": true
},
"talkback": true,
"talkbackChannels": 1
}
],
"ignoreDevices": [
"X",
"X"
],
"ignoreMultipleDevicesWarning": true,
"cleanCache": true,
"_bridge": {
"username": "x",
"port": 33879
}
}
]
}

I am getting similar error camara not responding.
[7/7/2023, 11:31:19 AM] [EufySecurity-2.1.7] INFO:  Country set: US
[7/7/2023, 11:31:19 AM] [EufySecurity-2.1.7] INFO:  Finished initializing!
[7/7/2023, 11:31:20 AM] [EufySecurity-2.1.7] ERROR: Not connected can't continue!


The character doesn't appear

here is a second error. I don't know if it has anything to do with it, but after exiting the tour mode and re-entering with another character (in this case, the blue one), this one doesn't appear, nor do the bubbles.
https://github.com/user-attachments/assets/735fcdb4-4490-49e4-95f0-c6a7a423e87f

Does this happen everytime?

Every time I've tried, yes.  And a friend told me that this happened to him after playing for a long time.


Whether have a batch inference file?

I wonder that whether there have a batch inference code?
It seems like I can only input one context for one time.
That would be nice if u can provided a file like batch_run.py or something else. Thx!

Hi, if you want to use the SpikeGPT for batch inferencing, you can use the RWKV_GPT (in model.py) as the backbone instead of using RWKV_RNN, which provides fully support to the batch inference. For RWKV_RNN, since we expect it can process the variant sequence length, we do not support batch inference.

Thx, I'll  give it a try

Hi, if you want to use the SpikeGPT for batch inferencing, you can use the RWKV_GPT (in model.py) as the backbone instead of using RWKV_RNN, which provides fully support to the batch inference. For RWKV_RNN, since we expect it can process the variant sequence length, we do not support batch inference.

When I running model.py, I got this exception:
Exception has occurred: ImportError
DLL load failed while importing wkv: 找不到指定的模块。
File "D:\GitHub\S_GPT\src\model.py", line 56, in
wkv_cuda = load(name="wkv", sources=["cuda/wkv_op.cpp", "cuda/wkv_cuda.cu"],
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ImportError: DLL load failed while importing wkv: 找不到指定的模块。
It seems that it can find the module wkv, but I don't know what's wkv?

Hi, this actually located in this github repo, you can check this link for what file you need, the 'cuda' folder should in the same path with your script.

Hi, this actually located in this github repo, you can check this link for what file you need, the 'cuda' folder should in the same path with your script.

Thx for ur reply! It's ok when I run it on ubuntu.


anti-featurerequest: don't add PGP/S-MIME signing

Since this is in the README

PGP or S/MIME

I just wanted to add my 2¢ and ask you not to do this. This absolutely defeats the purpose of signing outgoing mail and supports misbehaviours that some might be used to because of Exchange.
Why are messages signed? To guarantee to a degree not only that a person has access to a mailaccount, but that it is in fact the owner of a mailbox who's writing. Signing this on a server level obviously removes this and just adds a client-visible DKIM.

I agree this is best left to clients. The question came up before, see https://news.ycombinator.com/item?id=34909525. What I had in mind was a case where someone has a mox instance for themselves. Mox would be like an extended mail client.
Thanks for bringing this up!


MSRV

are you targeting a minimum compiler version?
looks like the current MSRV is 1.48.0

No.
Do you have any suggestions what the minimum supported version should be?

No.
Do you have any suggestions what the minimum supported version should be?

i don't think there's any need to restrict the MSRV in any way, especially while this is being actively developed. It's more a case of tracking the MSRV so that changes to the MSRV can be tracked as a semver jump.
There's no universally accepted convention, but one strategy is to treat a MSRV increase as a patch change pre-1.0.0 and as a minor change after 1.0.0 is release
i use cargo-msrv to determine the MSRV, and then a CI target to check that the project builds using that compiler. Then any time i add code or change dependencies which forces the MSRV to increase, the CI will catch that and I can increment the package version.
Maybe that's overkill for this repo, i guess that's up to you to decide.


Problem with transfer label on new line in sidebar-menu

@almasaeed2010 Hello.
After upgrade to version 2.2.0 we have a small problem with labels. You add:
.sidebar-menu,
.main-sidebar .user-panel,
.sidebar-menu > li.header {
  white-space: nowrap!important;
  overflow: hidden;
}

And, if label for list in side menu is too long, it didn't transfer on new line.
You can see screenshots https://github.com/sonata-project/SonataAdminBundle/pull/3142

Screenshots
Before update:

After update

Hello,
If you need to use long text like that you probably should manually override the code. The problem with us doing it here is that it would cause problems in transitions for the sidebar-mini. Add the following code to your custom css file and include it after loading AdminLTE.
.sidebar-menu,
.main-sidebar .user-panel,
.sidebar-menu > li.header {
  white-space: normal!important;
}

Thanks!

HI all,
I think the text should be in aligned correctly.


Peer dependencies issue in @walletconnect/heartbeat 1.2.0

Hello,
Installing @walletconnect/heartbeat: 1.2.0 generates the 2 following peerDependencies error with yarn 3:
➤ YN0002: │ @walletconnect/heartbeat@npm:1.2.0 doesn't provide @types/node (p37663), requested by ts-node
➤ YN0002: │ @walletconnect/heartbeat@npm:1.2.0 doesn't provide typescript (pd3167), requested by ts-node

It looks like there is a difference between the package.json dependencies on this repo  and the one in the published package
From repo:
  "dependencies": {
    "@walletconnect/events": "^1.0.1",
    "@walletconnect/time": "^1.0.2",
    "tslib": "1.14.1"
  },

From npm:
  "dependencies": {
    "@walletconnect/events": "^1.0.1",
    "@walletconnect/time": "^1.0.2",
    "chai": "^4.3.7",
    "mocha": "^10.2.0",
    "ts-node": "^10.9.1",
    "tslib": "1.14.1"
  }

Can you take a look at it please ?

having the same issue with this. this is a major blocker for me since it bloats vercel serverless function bundle which makes it impossible to deploy my app

having same issues here.. is it possible to override dependency's depdency list to force some package to be dev dependency?

Fixed in<EMAIL_ADDRESS>and 2.7.0 for the monorepo pkgs


Simplify which folders are used

This will let folder structure look like so:
- .gpt_eng
  - archive
  - memory
- prompt
- [all the generateed code files ("workspace") stored on the top level]

It will also stop archiving the workspace – think we should instead version control all the project code via git and let gpt-engineer make commits directly after it generates something.
Waiting to merge until we have a good way to "merge in" the generated code into the existing code: #650

Holding off to merge this until #650 .
After this, we should do #665

I believe this is obsolete now that #749 has been merged


Mistranslation bug

If you include a super high shift on ceasar cypher , everything turns into a letter of the alphabet. I am not sure if this is a what, but I am assuming it is a bug.

Super high shift indeed, I did not expect the user to input number bigger than 2^53 - 1. I'll change the implementation to use BigInt, if available. Also I don't like the scientific number formatting here. It's a low priority issue, though. Thank you for reporting this edge case!

Super high shift indeed, I did not expect the user to input number bigger than 2^53 - 1. I'll change the implementation to use BigInt, if available. Also I don't like the scientific number formatting here. It's a low priority issue, though. Thank you for reporting this edge case!

Just a little developer tip, never assume anything. If for any reason you cannot make have a very large input, add some code if it is too high telling them it is too large and set it back to a reasonable number for them.

I added support for arbitrary large shift values to Caesar Cipher. Translations should now work as expected, even for large numbers. Changes are rolling out in the next few hours. If you still encounter problems, please reopen this issue. Thank's again for reporting!


已发出的多签交易，支持查看该笔交易的call data

Is your feature request related to a problem? Please describe.
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
Describe the solution you'd like
A clear and concise description of what you want to happen.
Describe alternatives you've considered
A clear and concise description of any alternative solutions or features you've considered.
Additional context
Add any other context or screenshots about the feature request here.


[improve][misc] Return failed future in async method instead of throw exception directly

Motivation
Throws an exception from a async method generally a bad practice, for example in ProducerBuilderImpl#createAsync, checkArgument may throws a runtime exception directly:
https://github.com/apache/pulsar/blob/50b9a93e42e412d9f17b1637287d1a4c7c7ab148/pulsar-client/src/main/java/org/apache/pulsar/client/impl/ProducerBuilderImpl.java#L94-L97
If user call it in a async context, it may cause some unexpected behavior, here is a case:
CompletableFuture<xxx> future = new CompletableFuture<>();
xxx.whenComplete((v, ex) -> {
    xxx.createAsync().whenComplete((v, ex) -> {
        if (ex != null) {
            future.completeExceptionally(ex);
        } else {
            future.complete(v);
        }
    });
})

If createAsync() throws an exception from inside, the future will never get completed. This kind of problem is very hard to troubleshooting cause it could haven’t any log.
So I wrote a PMD rule to examine the entire Pulsar codebase (current master branch), aiming to identify similar kinds of problems. Here it is:
//MethodDeclaration[matches(@Name, "Async$")]
  [.//PrimaryPrefix/Name[matches(@Image, "(requireNonNull|checkNotNull|checkState|checkArgument)$")]]

Note: this rule may not be able to scan all similar code issues.
Modifications
Return a failed future instead of throw exception directly, for the given case above, it's will be change to:
public CompletableFuture<Producer<T>> createAsync() {
    // config validation
    if (conf.isBatchingEnabled() && conf.isChunkingEnabled()) {
        return FutureUtil.failedFuture(
                new IllegalArgumentException("Batching and chunking of messages can't be enabled together"));
    }

Verifying this change

[x] Make sure that the change passes the CI checks.

Does this pull request potentially affect one of the following parts:

If the box was checked, please highlight the changes

[ ] Dependencies (add or upgrade a dependency)
[ ] The public API
[ ] The schema
[ ] The default values of configurations
[ ] The threading model
[ ] The binary protocol
[ ] The REST endpoints
[ ] The admin CLI options
[ ] The metrics
[ ] Anything that affects deployment

Documentation

[ ] doc
[ ] doc-required
[x] doc-not-needed
[ ] doc-complete

Matching PR in forked repository
PR in forked repository: https://github.com/Shawyeok/pulsar/pull/9

Thanks for the contribution.
This is a breaking change in the API since exceptions could also be considered as part of the API although they aren't explicitly stated in the Java interface files. We use the PIP process to track changes to public APIs in Pulsar.
The other benefit of a PIP is that there will be broader awareness of the change in our approach to handle method parameter validation in asynchronous methods. The PIP process will help ensure better consistently in the project in the future.
@Shawyeok please start a new PIP for this change.


merge docs apis into a single one using wasm hacks

https://github.com/different-ai/embedbase/blob/main/docs/pages/api/ask.ts

sdk works on edge and node seamlessly now - need to bump version


Project status, further development/roadmap?

Hey @raphaellepuschitz just stumbled upon this project, it's really nice, are you looking forward to continue developing it and maybe packaging it into an installable package on NPM?
Happy to help you if you're interested, let me know!

Hi,
thanks for your interest!
Yes, I will continue the developemnt, but only as a side-project. So please be patient, if there's some time between version releases.
Also, I plan to support Node.js and NPM, hopefully with some of the next versions. If I need help with it, I'll let you know - thank's for the offer!
You can find the current roadmap in the README: https://github.com/raphaellepuschitz/SVG-World-Map/blob/master/README.md#further-development--changelog


Two things about signature.

(TBD) The hash of block/transaction doesn't involve signature data.

The block hash is calculated from header data, but nothing at all about block body.  I think It is not safe enough and should be changed. See this.

header has tx merkle tree root, enough @shiwk

Right, this can be closed.


The peer hangs when added Audio Transceiver - UWP

When I make the offer the Video is working fine both ways. When adding Audio Transceiver the peer hangs.

private async void CreateOfferButton_Click(object sender, RoutedEventArgs e)
{
        //await AddMicrophoneSourc();
        //AddLocalAudioTrack();
        //AddAudioTransceiver();

        await AddWebCamSource();
        AddLocalVideoTrack();
        AddVideoTransceiver();

        _peerConnection.CreateOffer();

    }

Offer message

The Peer hangs
Peer connection initialized successfully.
The Signal Server is Connected! Current User is gia80
Microphone Source was created
LocalAudioTrack was added. The track is Enabled True.
AudioTransceiver was created
MlineIndex: (mid =#-1)
DesiredDirection: SendReceive
LocalAudioTrack microphone_track
PeerConnection IsConnected: False
Webcam Source was created
LocalVideoTrack was added. The track is Enabled True.
VideoTransceiver was created
MlineIndex: (mid =#-1)
DesiredDirection: SendReceive
LocalVideoTrack webcam_track
PeerConnection IsConnected: False
gia80 Has mede an offer to Admin
gia80 Sends Ice Candidate Ice to  Admin
Data: candidate:1995739850 1 udp<PHONE_NUMBER> <IP_ADDRESS> 53539 typ host generation 0 ufrag U4V5 network-id 1 network-cost 10|0|0
gia80 Sends Ice Candidate Ice to  Admin
Data: candidate:1995739850 1 udp<PHONE_NUMBER> <IP_ADDRESS> 53540 typ host generation 0 ufrag U4V5 network-id 1 network-cost 10|1|1
gia80 Sends Ice Candidate Ice to  Admin
Data: candidate:2162486046 1 udp<PHONE_NUMBER> <IP_ADDRESS> 53539 typ srflx raddr <IP_ADDRESS> rport 53539 generation 0 ufrag U4V5 network-id 1 network-cost 10|0|0
gia80 Sends Ice Candidate Ice to  Admin
Data: candidate:947351098 1 tcp<PHONE_NUMBER> <IP_ADDRESS> 58137 typ host tcptype passive generation 0 ufrag U4V5 network-id 1 network-cost 10|0|0
gia80 Sends Ice Candidate Ice to  Admin
Data: candidate:2162486046 1 udp<PHONE_NUMBER> <IP_ADDRESS> 53540 typ srflx raddr <IP_ADDRESS> rport 53540 generation 0 ufrag U4V5 network-id 1 network-cost 10|1|1
gia80 Sends Ice Candidate Ice to  Admin
Data: candidate:947351098 1 tcp<PHONE_NUMBER> <IP_ADDRESS> 58138 typ host tcptype passive generation 0 ufrag U4V5 network-id 1 network-cost 10|1|1
gia80 Sends Offer to  Admin
Data: v=0
o=-<PHONE_NUMBER>83772993 2 IN IP4 <IP_ADDRESS>
s=-
t=0 0
a=group:BUNDLE 0 1
a=msid-semantic: WMS
m=audio 9 UDP/TLS/RTP/SAVPF 111 103 104 9 102 0 8 106 105 13 110 112 113 126
c=IN IP4 <IP_ADDRESS>
a=rtcp:9 IN IP4 <IP_ADDRESS>
a=ice-ufrag:U4V5
a=ice-pwd:5GI1VGEBeoALnTAAesYziVfF
a=ice-options:trickle
a=fingerprint:sha-256 B9:4F:45:FC:32:B2:86:F9:06:71:10:AB:06:D2:A3:2A:96:C4:15:6C:B5:16:8C:D5:2B:05:A8:99:E0:DA:68:0A
a=setup:actpass
a=mid:0
a=extmap:1 urn:ietf:params:rtp-hdrext:ssrc-audio-level
a=extmap:9 urn:ietf:params:rtp-hdrext:sdes:mid
a=sendrecv
a=msid:- bb95083c-fa21-40ae-813d-0d0dbc055cf1
a=rtcp-mux
a=rtpmap:111 opus/48000/2
a=rtcp-fb:111 transport-cc
a=fmtp:111 minptime=10;useinbandfec=1
a=rtpmap:103 ISAC/16000
a=rtpmap:104 ISAC/32000
a=rtpmap:9 G722/8000
a=rtpmap:102 ILBC/8000
a=rtpmap:0 PCMU/8000
a=rtpmap:8 PCMA/8000
a=rtpmap:106 CN/32000
a=rtpmap:105 CN/16000
a=rtpmap:13 CN/8000
a=rtpmap:110 telephone-event/48000
a=rtpmap:112 telephone-event/32000
a=rtpmap:113 telephone-event/16000
a=rtpmap:126 telephone-event/8000
a=ssrc:3352556778 cname:xuMEriQ8ZrZcv+gq
a=ssrc:3352556778 msid: bb95083c-fa21-40ae-813d-0d0dbc055cf1
a=ssrc:3352556778 mslabel:
a=ssrc:3352556778 label:bb95083c-fa21-40ae-813d-0d0dbc055cf1
m=video 9 UDP/TLS/RTP/SAVPF 96 97 98 99 100 101 127 124 125
c=IN IP4 <IP_ADDRESS>
a=rtcp:9 IN IP4 <IP_ADDRESS>
a=ice-ufrag:U4V5
a=ice-pwd:5GI1VGEBeoALnTAAesYziVfF
a=ice-options:trickle
a=fingerprint:sha-256 B9:4F:45:FC:32:B2:86:F9:06:71:10:AB:06:D2:A3:2A:96:C4:15:6C:B5:16:8C:D5:2B:05:A8:99:E0:DA:68:0A
a=setup:actpass
a=mid:1
a=extmap:2 urn:ietf:params:rtp-hdrext:toffset
a=extmap:3 http://www.webrtc.org/experiments/rtp-hdrext/abs-send-time
a=extmap:4 urn:3gpp:video-orientation
a=extmap:5 http://www.ietf.org/id/draft-holmer-rmcat-transport-wide-cc-extensions-01
a=extmap:6 http://www.webrtc.org/experiments/rtp-hdrext/playout-delay
a=extmap:7 http://www.webrtc.org/experiments/rtp-hdrext/video-content-type
a=extmap:8 http://www.webrtc.org/experiments/rtp-hdrext/video-timing
a=extmap:10 http://tools.ietf.org/html/draft-ietf-avtext-framemarking-07
a=extmap:9 urn:ietf:params:rtp-hdrext:sdes:mid
a=sendrecv
a=msid:- 64a24f16-17c4-4126-99f8-b91dde060e4e
a=rtcp-mux
a=rtcp-rsize
a=rtpmap:96 VP8/90000
a=rtcp-fb:96 goog-remb
a=rtcp-fb:96 transport-cc
a=rtcp-fb:96 ccm fir
a=rtcp-fb:96 nack
a=rtcp-fb:96 nack pli
a=rtpmap:97 rtx/90000
a=fmtp:97 apt=96
a=rtpmap:98 VP9/90000
a=rtcp-fb:98 goog-remb
a=rtcp-fb:98 transport-cc
a=rtcp-fb:98 ccm fir
a=rtcp-fb:98 nack
a=rtcp-fb:98 nack pli
a=fmtp:98 x-google-profile-id=0
a=rtpmap:99 rtx/90000
a=fmtp:99 apt=98
a=rtpmap:100 H264/90000
a=rtcp-fb:100 goog-remb
a=rtcp-fb:100 transport-cc
a=rtcp-fb:100 ccm fir
a=rtcp-fb:100 nack
a=rtcp-fb:100 nack pli
a=fmtp:100 packetization-mode=1
a=rtpmap:101 rtx/90000
a=fmtp:101 apt=100
a=rtpmap:127 red/90000
a=rtpmap:124 rtx/90000
a=fmtp:124 apt=127
a=rtpmap:125 ulpfec/90000
a=ssrc-group:FID 335510026<PHONE_NUMBER>
a=ssrc:335510026 cname:xuMEriQ8ZrZcv+gq
a=ssrc:335510026 msid: 64a24f16-17c4-4126-99f8-b91dde060e4e
a=ssrc:335510026 mslabel:
a=ssrc:335510026 label:64a24f16-17c4-4126-99f8-b91dde060e4e
a=ssrc:2750956389 cname:xuMEriQ8ZrZcv+gq
a=ssrc:2750956389 msid: 64a24f16-17c4-4126-99f8-b91dde060e4e
a=ssrc:2750956389 mslabel:
a=ssrc:2750956389 label:64a24f16-17c4-4126-99f8-b91dde060e4e

Answer message

Working Video but no Audio. Audio Transmiter omitted
Peer connection initialized successfully.
The Signal Server is Connected! Current User is gia80
Webcam Source was created
LocalVideoTrack was added. The track is Enabled True.
VideoTransceiver was created
MlineIndex: (mid =#-1)
DesiredDirection: SendReceive
LocalVideoTrack webcam_track
PeerConnection IsConnected: False
gia80 Has mede an offer to Admin
gia80 Sends Ice Candidate Ice to  Admin
Data: candidate:947351098 1 tcp<PHONE_NUMBER> <IP_ADDRESS> 58144 typ host tcptype passive generation 0 ufrag fH+U network-id 1 network-cost 10|0|0
gia80 Sends Offer to  Admin
Data: v=0
o=-<PHONE_NUMBER>844678066 2 IN IP4 <IP_ADDRESS>
s=-
t=0 0
a=group:BUNDLE 0
a=msid-semantic: WMS
m=video 9 UDP/TLS/RTP/SAVPF 96 97 98 99 100 101 127 124 125
c=IN IP4 <IP_ADDRESS>
a=rtcp:9 IN IP4 <IP_ADDRESS>
a=ice-ufrag:fH+U
a=ice-pwd:/HEm2f9hPzPq4vItnZFC1WZb
a=ice-options:trickle
a=fingerprint:sha-256 8E:88:DC:46:AC:11:98:06:EE:A0:11:F4:5B:57:D0:16:F7:98:EA:95:42:8E:19:DB:C3:62:1F:BB:78:AE:1F:E6
a=setup:actpass
a=mid:0
a=extmap:2 urn:ietf:params:rtp-hdrext:toffset
a=extmap:3 http://www.webrtc.org/experiments/rtp-hdrext/abs-send-time
a=extmap:4 urn:3gpp:video-orientation
a=extmap:5 http://www.ietf.org/id/draft-holmer-rmcat-transport-wide-cc-extensions-01
a=extmap:6 http://www.webrtc.org/experiments/rtp-hdrext/playout-delay
a=extmap:7 http://www.webrtc.org/experiments/rtp-hdrext/video-content-type
a=extmap:8 http://www.webrtc.org/experiments/rtp-hdrext/video-timing
a=extmap:10 http://tools.ietf.org/html/draft-ietf-avtext-framemarking-07
a=extmap:9 urn:ietf:params:rtp-hdrext:sdes:mid
a=sendrecv
a=msid:- 14efdb95-2cc1-47d1-87b2-054c0573c9d2
a=rtcp-mux
a=rtcp-rsize
a=rtpmap:96 VP8/90000
a=rtcp-fb:96 goog-remb
a=rtcp-fb:96 transport-cc
a=rtcp-fb:96 ccm fir
a=rtcp-fb:96 nack
a=rtcp-fb:96 nack pli
a=rtpmap:97 rtx/90000
a=fmtp:97 apt=96
a=rtpmap:98 VP9/90000
a=rtcp-fb:98 goog-remb
a=rtcp-fb:98 transport-cc
a=rtcp-fb:98 ccm fir
a=rtcp-fb:98 nack
a=rtcp-fb:98 nack pli
a=fmtp:98 x-google-profile-id=0
a=rtpmap:99 rtx/90000
a=fmtp:99 apt=98
a=rtpmap:100 H264/90000
a=rtcp-fb:100 goog-remb
a=rtcp-fb:100 transport-cc
a=rtcp-fb:100 ccm fir
a=rtcp-fb:100 nack
a=rtcp-fb:100 nack pli
a=fmtp:100 packetization-mode=1
a=rtpmap:101 rtx/90000
a=fmtp:101 apt=100
a=rtpmap:127 red/90000
a=rtpmap:124 rtx/90000
a=fmtp:124 apt=127
a=rtpmap:125 ulpfec/90000
a=ssrc-group:FID<PHONE_NUMBER><PHONE_NUMBER>
a=ssrc:2212701345 cname:SML65bLBI1s5gg54
a=ssrc:2212701345 msid: 14efdb95-2cc1-47d1-87b2-054c0573c9d2
a=ssrc:2212701345 mslabel:
a=ssrc:2212701345 label:14efdb95-2cc1-47d1-87b2-054c0573c9d2
a=ssrc:1652958511 cname:SML65bLBI1s5gg54
a=ssrc:1652958511 msid: 14efdb95-2cc1-47d1-87b2-054c0573c9d2
a=ssrc:1652958511 mslabel:
a=ssrc:1652958511 label:14efdb95-2cc1-47d1-87b2-054c0573c9d2
gia80 Sends Ice Candidate Ice to  Admin
Data: candidate:1995739850 1 udp<PHONE_NUMBER> <IP_ADDRESS> 64500 typ host generation 0 ufrag fH+U network-id 1 network-cost 10|0|0
gia80 Sends Ice Candidate Ice to  Admin
Data: candidate:2162486046 1 udp<PHONE_NUMBER> <IP_ADDRESS> 64500 typ srflx raddr <IP_ADDRESS> rport 64500 generation 0 ufrag fH+U network-id 1 network-cost 10|0|0
WireMessage
MessageType: Answer
Receiver: gia80
Data: v=0
o=-<PHONE_NUMBER>685438311 2 IN IP4 <IP_ADDRESS>
s=-
t=0 0
a=group:BUNDLE 0
a=msid-semantic: WMS
m=video 9 UDP/TLS/RTP/SAVPF 96 97 98 99 100 101 127 124 125
c=IN IP4 <IP_ADDRESS>
a=rtcp:9 IN IP4 <IP_ADDRESS>
a=ice-ufrag:lYtV
a=ice-pwd:DMOLNOAa3eQPHmfr+dTDQrwh
a=ice-options:trickle
a=fingerprint:sha-256 24:EB:E9:E2:CF:0A:0F:95:56:49:38:F8:2E:9E:2C:DA:8B:0E:5C:52:90:E8:09:6A:52:C5:BE:0B:BE:04:B5:44
a=setup:active
a=mid:0
a=extmap:2 urn:ietf:params:rtp-hdrext:toffset
a=extmap:3 http://www.webrtc.org/experiments/rtp-hdrext/abs-send-time
a=extmap:4 urn:3gpp:video-orientation
a=extmap:5 http://www.ietf.org/id/draft-holmer-rmcat-transport-wide-cc-extensions-01
a=extmap:6 http://www.webrtc.org/experiments/rtp-hdrext/playout-delay
a=extmap:7 http://www.webrtc.org/experiments/rtp-hdrext/video-content-type
a=extmap:8 http://www.webrtc.org/experiments/rtp-hdrext/video-timing
a=extmap:10 http://tools.ietf.org/html/draft-ietf-avtext-framemarking-07
a=extmap:9 urn:ietf:params:rtp-hdrext:sdes:mid
a=sendrecv
a=msid:- 17f13f0e-ee0a-4f7c-9c62-feb3132dc141
a=rtcp-mux
a=rtcp-rsize
a=rtpmap:96 VP8/90000
a=rtcp-fb:96 goog-remb
a=rtcp-fb:96 transport-cc
a=rtcp-fb:96 ccm fir
a=rtcp-fb:96 nack
a=rtcp-fb:96 nack pli
a=rtpmap:97 rtx/90000
a=fmtp:97 apt=96
a=rtpmap:98 VP9/90000
a=rtcp-fb:98 goog-remb
a=rtcp-fb:98 transport-cc
a=rtcp-fb:98 ccm fir
a=rtcp-fb:98 nack
a=rtcp-fb:98 nack pli
a=fmtp:98 x-google-profile-id=0
a=rtpmap:99 rtx/90000
a=fmtp:99 apt=98
a=rtpmap:100 H264/90000
a=rtcp-fb:100 goog-remb
a=rtcp-fb:100 transport-cc
a=rtcp-fb:100 ccm fir
a=rtcp-fb:100 nack
a=rtcp-fb:100 nack pli
a=fmtp:100 packetization-mode=1
a=rtpmap:101 rtx/90000
a=fmtp:101 apt=100
a=rtpmap:127 red/90000
a=rtpmap:124 rtx/90000
a=fmtp:124 apt=127
a=rtpmap:125 ulpfec/90000
a=ssrc-group:FID<PHONE_NUMBER><PHONE_NUMBER>
a=ssrc:2803839634 cname:FwDIM0dMBLj91eTF
a=ssrc:1803572327 cname:FwDIM0dMBLj91eTF
WireMessage
MessageType: Ice
Receiver: gia80
Data: candidate:1495340180 1 udp<PHONE_NUMBER> <IP_ADDRESS> 57966 typ host generation 0 ufrag lYtV network-id 1 network-cost 10|0|0
WireMessage
MessageType: Ice
Receiver: gia80
Data: candidate:2939252032 1 udp<PHONE_NUMBER> <IP_ADDRESS> 57966 typ srflx raddr <IP_ADDRESS> rport 57966 generation 0 ufrag lYtV network-id 1 network-cost 10|0|0
PeerConnection: connected.
ICE state: Checking
RemoteVideoTrack was added
WireMessage
MessageType: Ice
Receiver: gia80
Data: candidate:396552292 1 tcp<PHONE_NUMBER> <IP_ADDRESS> 53102 typ host tcptype passive generation 0 ufrag lYtV network-id 1 network-cost 10|0|0
ICE state: Connected
ICE state: Completed

Environment

MR-WebRTC version 2

Platform: UWP
Player (UWP)]

Architecture: x86, x64,

Target device: Windows Desktop

What do you mean by "the peer hangs"? Does the process stop inside one of the MR-WebRTC API calls?

When I make an offer to the receiver (Including Audio Transmitter) the receiver hangs, no answer is coming back from the receiver. When the Audio Transmitter is omitted then the peer is connected an the video link is working.
On the receiver I Call the following.

     private async Task AddWebCamSource()
    {
        _webcamSource = await DeviceVideoTrackSource.CreateAsync();
        _webcamSource.I420AVideoFrameReady += LocalI420AFrameReady;
    }

    private void AddLocalVideoTrack()
    {
        var videoTrackConfig = new LocalVideoTrackInitConfig
        { trackName = "webcam_track"};
        _localVideoTrack = LocalVideoTrack.CreateFromSource(_webcamSource, videoTrackConfig);
    }

    private void AddVideoTransceiver()
    {
        _videoTransceiver = _peerConnection.AddTransceiver(MediaKind.Video, new TransceiverInitSettings()
        {InitialDesiredDirection = Transceiver.Direction.SendReceive});

        _videoTransceiver.LocalVideoTrack = _localVideoTrack;

    }

    private async Task AddMicrophoneSourc()
    {
        _microphoneSource = await DeviceAudioTrackSource.CreateAsync();

    }

    private void AddLocalAudioTrack()
    {
        var audioTrackConfig = new LocalAudioTrackInitConfig
        {trackName = "microphone_track"};
        _localAudioTrack = LocalAudioTrack.CreateFromSource(_microphoneSource, audioTrackConfig);
    }

    private void AddAudioTransceiver()
    {
            _audioTransceiver = _peerConnection.AddTransceiver(MediaKind.Audio, new TransceiverInitSettings()
            { InitialDesiredDirection = Transceiver.Direction.SendReceive});

            _audioTransceiver.LocalAudioTrack = _localAudioTrack;

    }

I catch the Transmitter
_peerConnection.TransceiverAdded += TransceiverAdded;

    private void TransceiverAdded(Transceiver transceiver)
    {
        switch (transceiver.MediaKind)
        {
            case MediaKind.Video:
                _videoTransceiver = transceiver;
                transceiver.DesiredDirection = Transceiver.Direction.SendReceive;
                transceiver.LocalVideoTrack = _localVideoTrack;
                break;
            case MediaKind.Audio:
                _audioTransceiver = transceiver;
                transceiver.DesiredDirection = Transceiver.Direction.SendReceive;
                transceiver.LocalAudioTrack = _localAudioTrack;
                break;
            default:
                throw new ArgumentOutOfRangeException("Invalid MediaKind");
        }
    }

I notice that when I attache Audio Transmitter to the peer in the sender and make a offer to the receiver the receiver freezes when the method ReceiveWireMessage(Message wireMessage) is called in the case WireMessageType.Ice:  _peerConnection.AddIceCandidate(msg)
The Candidate is when this happens is: "candidate:1495340180 1 udp<PHONE_NUMBER> <IP_ADDRESS> 61251 typ host generation 0 ufrag biBW network-id 1 network-cost 10"
The Peer_LocalSdpReadytoSend and Peer_IceCandidateReadytoSend is not called after.
When the Audio Transmitter is omitted from the peer in the sender the methods is called and Video work fine, but no sound.
Working candidate:
candidate:1495340180 1 udp<PHONE_NUMBER> <IP_ADDRESS> 59346 typ host generation 0 ufrag 9L+2 network-id 1 network-cost 10"

I am not 100% sure but I would expect that you need to relay the messages in the same order as they are generated (certainly you shouldn't reorder do things like delivering ICE candidates before the offer is received). Can you try enforcing that they are delivered to the peers in order?
FWIW I have never seen freezes in AddIceCandidate in normal use cases. It would help if you could provide a callstack of the point where it freezes and/or run a debug build of MR-WebRTC and check the logs for errors.

I can not build MR-WebRTC  i use Nuget MR-WebRTC .UWP and when I dubug AddIceCandidate I see nothing, no Exception, the method only freezes. I have no more ideas. I can do some work around, maybe slow down the transmission of the signal or make some ordered list for receiving messages, but i dont think it is right.
What I can see is
On the caller Side
0). Audio Transceiver Added
1). Video Transceiver Added
2). Send Sdp : Offer
3). Send IceCandidate
4). Send IceCandidate
5). Send IceCandidate
6). Send IceCandidate
7). Send IceCandidate
8). Send IceCandidate
When the App Freeze on the calliee
0). Received Sdp : Offer
1). Audio Transceiver Added
2). Video Transceiver Added
3). RemoteVideoTrack was added
4). Received IceCandidate
5). PeerConnection: connected.
     "FREEZE"

For the success when Audio Transmitter is not used
On the caller side
0). Video Transceiver Added
1). Send Sdp : Offer
2). Send IceCandidate
3). Send IceCandidate
4). Send IceCandidate
5). Received Sdp : Answer
6). PeerConnection: connected.
7). ICE state: Checking
8). RemoteVideoTrack was added
9). Received IceCandidate
10). Received IceCandidate
11). ICE state: Connected
12). ICE state: Completed
13). Received IceCandidate
On Callee side
0). Received Sdp : Offer
1). Video Transceiver Added
2). RemoteVideoTrack was added
3). PeerConnection: connected.
4). Send Sdp : Answer
5). Received IceCandidate
6). Send IceCandidate
7). Send IceCandidate
8). ICE state: Checking
9). Received IceCandidate
10). Received IceCandidate
11). Send IceCandidate
12). ICE state: Connected
13). ICE state: Disconnected

If there are possibility to debug MR-WebRtc Lib with my scenario I can provide my project https://github.com/Domonap/WebRtc-UWP.git

You can try pausing the Visual Studio debugger after the process has frozen in AddIceCandidate and looking at the Callstack/Parallel stack windows - you should be able to see if there are one or more deadlocked thread and where they are waiting. If the deadlock is in native code you will need the mrwebrtc pdbs that you can find at https://github.com/microsoft/MixedReality-WebRTC/releases/download/v2.0.0/mrwebrtc_uwp-2.0.0_pdbs.zip

Yes I have try to pause and load Pdbs, Put on Native debug and Include all Exception, but still I'm not getting any useable information. Obvious I'm doing something wrong with the debugger. I have tried everything that I can with the working environment that I have. To go to bottom with it I would need to compile the Mr-WebRct and Webrct Lib, but for now I don't have the possibility to do that.
Where strange that it work with node-dss but not with signalR, and it always freezes on the same point. After stopping the flow with the debugger it does work. Some synchronization needs to be done.
In the coming weeks I will be trying again to make it work.

Hi I Have found the problem. I was calling  AddIceCandidate and SetRemoteDescriptionAsync from another thread that comes from SignalR. The solution was to add
await Task.Run(() => _peerConnection.AddIceCandidate(candidate));  and
await Task.Run(() => _peerConnection.SetRemoteDescriptionAsync(message.Spd));


Defund Oakdale Police Department

TO<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>SUBJECT: [INSERT UNIQUE SUBJECT LINE HERE]
Dear Mayor McCarty and Oakdale City Council Members,
My name is [YOUR NAME] and I am a resident of Oakdale. I am writing to you about the city budget presented to the public on the city government website.
I am asking the Oakdale city government to commit to racial justice, to not be complicit in further promoting a system of policing that reinforces systemic oppression — that further reinforces the oppression of some of your constituents, who you were elected to serve and protect.
To do so, I demand future funding for the police department be redirected elsewhere. In 2019, this fiscal year, police services were allocated over 1 million dollars in funding. This means they received over thirty percent of the city’s total funds, leaving slivers to be distributed amongst the more essential services that power the heartbeat of our city. This subsidy, rather than dismantling, promotes and encourages a system based in racial and class inequality.
Research has shown, time and time again, that it is not police (or prisons) that aid communities so they are protected— it is affordable housing, educational opportunities, community violence intervention programs, and access to holistic health treatment that do so, because misdemeanors and lawbreaking are often caused by unsupportive environments that rather than alleviating financial or mental issues, only dramatize them further. Support funding departments that prioritize your constituents rather than aggressively funding a system far beyond repair.
Can I count on you to consider an alternative budget that, rather than emphasizing law enforcement, emphasizes social services with proven impact on not just actual crime but also the welfare of your constituents, such as housing and education?
Thank you,
[YOUR NAME]
[YOUR ADDRESS]
[YOUR EMAIL]
[YOUR PHONE NUMBER]

Thank you for submitting your city to defund12.org! Your request is now in the queue for review and publishing. We’ll reply here if we have any questions.

Editing notes: Edits made per #1406. I need help with confirming police budget numbers, as the original email said the police budget was $1 million (I think there was just a digit missing?). I found budget numbers here, but these charts made my head spin. https://drive.google.com/file/d/1B0zIx66CFVWMN6SJszGJ37X5JW181xLT/view
To<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>Subject: [UNIQUE SUBJECT LINE HERE]
Message: [Don't forget to replace the [x]'s with your information, and consider adding a couple of sentences in your own words to ensure this message gets past email filters.]
Dear Mayor McCarty and Oakdale City Council Members,
My name is [YOUR NAME] and I am a resident of Oakdale. I am writing to you about the city budget presented to the public on the city government website.
I am asking the Oakdale City Council to commit to racial justice and to not be complicit in further promoting a system of policing that reinforces systemic oppression.
To do so, I demand future funding for the police department be redirected elsewhere. In 2019, this fiscal year, police services were allocated over $1 million in funding. This means they received over 30% of the city’s total funds, leaving slivers to be distributed amongst the more essential services that power the heartbeat of our city. This subsidy, rather than dismantling, promotes and encourages a system based in racial and class inequality.
Research has shown, time and again, that it is not the police (or prisons) that promote community safety—it is affordable housing, educational opportunities, community violence intervention programs, and access to holistic health treatment that reduce crime. Misdemeanors and lawbreaking are often caused by unsupportive environments, and policing and incarceration only further contribute to financial and mental instability. Support funding departments that prioritize your constituents rather than aggressively funding a system far beyond repair.
Can I count on you to consider an alternative budget that, rather than emphasizing law enforcement, defunds it and reallocates funding to social services with a proven impact on the welfare of your constituents, such as housing and education?
Thank you,
[YOUR NAME]
[YOUR ADDRESS]
[YOUR EMAIL]
[YOUR PHONE NUMBER]

@aurendisson On page 6 of the document you shared I found these numbers for 2019-20 Adopted Final Budget:
Police Administration: $945,604
Police Dispatch: $677,262
Police Special Services: $344,216
Police Field Service: $3,258,897
This is not including Police Capital Facilities funding, Police Equipment Replacement funding, Police Equestrian Unit funding, and others throughout the document. It's a weirdly organized and not very user friendly page. But considering this information, the funding amounts to more than $4 million.
I couldn't find a source for the claim that it is 30% of the city's total funds.
@dumbneanderthal Maybe you could help us out with your sources?


E2e reporter test results

Create html report for test results
Add util functions to attach screenshots to the html report

https://emurgo.atlassian.net/browse/YOMO-684

will create a new PR as this was created based on an older branch


Support for generic annotations and meta for third-party libraries

Hi, we would need to send some annotations (or meta) data along with the data that is sent automatically as a segment or subsegment. Taking into account that this data is grabbed from the context, so this data changes at each request. A good example is the request_id that is progressed as a header between all of the services.
We are keen on the Aiohttp user case but might be worth it have a solution that fits all of the integrations.
A possible solution that wouldn't introduce much technical debt would be adding a new method of the Context that would be called each time that a segment is closed/sent to add all extra meta/annotations. By default will do nothing, but the developer would have the chance to override this method subclassing the Context class and implementing its own logic.
Thoughts, any other ideas?

Sorry for the late response. One thing I would like to clarify is that the X-Ray service aggregates metadata and annotations to trace level. In the aiohttp case all segments/subsegments generated for a single incoming request are already correlated with a trace id, so as long as you add the request_id to any segment/subsegment, you can then filer on that request id and see all segments/subsegments for that request. IMO a recorder level hook is not necessary as the recorder will blindly add the pre-defined metadata/annotations to each trace entity, which increases unnecessary payload and adds more overhead.
Having said that, I do think there should be a more convenient way to add metadata/annotations to X-Ray provided middleware or library patcher, to make user code less verbose. I propose some callback that you register on a per middleware/library basis. In the aiohttp case you will do something like
def my_cb(request, entity):
    id = request.headers['x-request-id']
    entity.put_annotation('id', id)
    metadata = generate_whatever_i_need()
    entity.put_metadata(metadata)

xray_recorder.register_callback('aiohttp', my_cb)

Then in the X-Ray aiohttp middleware it will do some extra work
your_cb = xray_recorder.get_cb('aiohttp')
your_cb(request, segment)

This way any aiohttp user can register any callback and extract whatever extra information they want and add adds annotations/metadata. We can do similar thing on third party library patcher to have the recorder to do some customized work to add additional info when doing capture work.
But some explicit contract is necessary so the recorder knows what parameter to pass to the user defined callback and the users know what parameters are expected to be provided when writing the callback.
Please let me know your thoughts.

Thanks for the clarification, good to know that is only needed to add the metadata in one place.
Regarding your proposal I like it, and more important solves the issue. What I don't like is having the callback configured at recorder level and being used explicitly only for the third integrations, it's kinda weird IMO.
So what I would propose is move the callback registration as close as we can to the third party package, this will allow us to implement the callback per integration, perhaps in the case of the Aiohttp middleware:
from aws_xray_sdk.ext.aiohttp.middleware import middleware, register_cb

def cb(request, entity):
    entity.put_annotation('id', request.headers['id'])

register_cb(cb)
app = web.Application(middlewares=[middleware])

Thoughts?