Default algorithms for client_secret_jwt auth

@maennchen I did have one question about this. My read of the spec was that RS256 should be allowed but not HS256, but there's a specific test case which relies on HS256 being allowed. This was introduced to fix https://github.com/erlef/oidcc/issues/287 but I don't totally understand the original issue.

@paulswartz Looking into it I'm not sure if this is correct the other way around.
The idea of client_secret_jwt is to infer the signature from the client_secret. This therefore operates on a shared secret & a MAC. With RS256 we need a public & private key.
Therefore I'm questioning if RS256 should be here and not HS256.
What part of the specification are you referring to?

Originally posted by @paulswartz in https://github.com/erlef/oidcc/pull/300#discussion_r1421225761

Some spec references:
OAuth 2.0 Authorization Server Metadata
https://datatracker.ietf.org/doc/html/rfc8414

token_endpoint_auth_signing_alg_values_supported [...] No default
algorithms are implied if this entry is omitted.  Servers SHOULD
support "RS256".  The value "none" MUST NOT be used.

JWT Profile
https://datatracker.ietf.org/doc/html/rfc7523#section-5

The "RS256" algorithm, from [JWA], is a mandatory-to-implement JSON
Web Signature algorithm for this profile.

Given that, I think there shouldn't be a default for client_secret_jwt or private_key_jwt. The SHOULD is applying to whether there's implementation support, not that it's a default value if the configuration is undefined.

I think there shouldn't be a default for client_secret_jwt or private_key_jwt.

@paulswartz I tend to agree. I think I remember some certification issue though without a default.
I’ll rerun the certification profiles without the default to be sure. (Or you can as well if you want.)
If we can pass everything without a default, we should assume no supported algorithms and fall back to the next auth method.

I added a patch for that to #300. I should have some time in the next few days to check it against the profiles if you don't get to it first.

Looking at the conformance suite, it's hard to tell what's expected here. The client_secret_jwt tests don't include any HS* algorithms in token_endpoint_auth_signing_alg_values_supported:
  "token_endpoint_auth_methods_supported": [
    "client_secret_jwt"
  ],
  "token_endpoint_auth_signing_alg_values_supported": [
    "RS256",
    "RS384",
    "RS512",
    "PS256",
    "PS384",
    "PS512",
    "ES256",
    "ES256K",
    "ES384",
    "ES512",
    "EdDSA"
  ],

@paulswartz Are we able to sign the token using the algoithms provided? On a first glance, they all look like they are asymmetric…
I’ll have a look myself in the next few days when I have some time available.

@paulswartz I see the reason why it was in:
Certification Setup

Test Command:
mix run_certification  \
  --profile test \
  --alias test \
  --version v3.2.0 \
  --auto-open \
  --auto-screenshot \
  --token-endpoint-auth-method client_secret_jwt

Test Name: oidcc-client-test
Logged Metadata:
19:18:39.334 [info] Loaded Provider Configuration: %Oidcc.ProviderConfiguration{
  issuer: "https://www.certification.openid.net/test/a/test/",
  authorization_endpoint: "https://www.certification.openid.net/test/a/test/authorize",
  token_endpoint: "https://www.certification.openid.net/test/a/test/token",
  userinfo_endpoint: "https://www.certification.openid.net/test/a/test/userinfo",
  jwks_uri: "https://www.certification.openid.net/test/a/test/jwks",
  registration_endpoint: "https://www.certification.openid.net/test/a/test/register",
  scopes_supported: ["openid", "phone", "profile", "email", "address",
   "offline_access"],
  response_types_supported: ["code", "id_token code", "token code id_token",
   "id_token", "token id_token", "token code", "token"],
  response_modes_supported: ["query", "fragment", "form_post"],
  grant_types_supported: ["authorization_code", "implicit"],
  acr_values_supported: ["PASSWORD"],
  subject_types_supported: [:public, :pairwise],
  id_token_signing_alg_values_supported: ["none", "RS256", "RS384", "RS512",
   "PS256", "PS384", "PS512", "ES256", "ES256K", "ES384", "ES512", "EdDSA"],
  id_token_encryption_alg_values_supported: ["RSA1_5", "RSA-OAEP",
   "RSA-OAEP-256", "RSA-OAEP-384", "RSA-OAEP-512", "ECDH-ES", "ECDH-ES+A128KW",
   "ECDH-ES+A192KW", "ECDH-ES+A256KW", "A128KW", "A192KW", "A256KW",
   "A128GCMKW", "A192GCMKW", "A256GCMKW", "dir"],
  id_token_encryption_enc_values_supported: ["A128CBC-HS256", "A192CBC-HS384",
   "A256CBC-HS512", "A128GCM", "A192GCM", "A256GCM"],
  userinfo_signing_alg_values_supported: ["RS256", "RS384", "RS512", "PS256",
   "PS384", "PS512", "ES256", "ES256K", "ES384", "ES512", "EdDSA"],
  userinfo_encryption_alg_values_supported: ["RSA1_5", "RSA-OAEP",
   "RSA-OAEP-256", "RSA-OAEP-384", "RSA-OAEP-512", "ECDH-ES", "ECDH-ES+A128KW",
   "ECDH-ES+A192KW", "ECDH-ES+A256KW", "A128KW", "A192KW", "A256KW",
   "A128GCMKW", "A192GCMKW", "A256GCMKW", "dir"],
  userinfo_encryption_enc_values_supported: ["A128CBC-HS256", "A192CBC-HS384",
   "A256CBC-HS512", "A128GCM", "A192GCM", "A256GCM"],
  request_object_signing_alg_values_supported: ["none", "RS256", "RS384",
   "RS512", "PS256", "PS384", "PS512", "ES256", "ES256K", "ES384", "ES512",
   "EdDSA"],
  request_object_encryption_alg_values_supported: ["RSA1_5", "RSA-OAEP",
   "RSA-OAEP-256", "RSA-OAEP-384", "RSA-OAEP-512", "ECDH-ES", "ECDH-ES+A128KW",
   "ECDH-ES+A192KW", "ECDH-ES+A256KW", "A128KW", "A192KW", "A256KW",
   "A128GCMKW", "A192GCMKW", "A256GCMKW", "dir"],
  request_object_encryption_enc_values_supported: ["A128CBC-HS256",
   "A192CBC-HS384", "A256CBC-HS512", "A128GCM", "A192GCM", "A256GCM"],
  token_endpoint_auth_methods_supported: ["client_secret_jwt"],
  token_endpoint_auth_signing_alg_values_supported: ["RS256", "RS384", "RS512",
   "PS256", "PS384", "PS512", "ES256", "ES256K", "ES384", "ES512", "EdDSA"],
  display_values_supported: :undefined,
  claim_types_supported: [:normal, :aggregated, :distributed],
  claims_supported: ["sub", "name", "given_name", "family_name", "middle_name",
   "nickname", "preferred_username", "gender", "birthdate", "address",
   "zoneinfo", "locale", "phone_number", "phone_number_verified", "email",
   "email_verified", "website", "profile", "updated_at", "txn"],
  service_documentation: :undefined,
  claims_locales_supported: :undefined,
  ui_locales_supported: :undefined,
  claims_parameter_supported: true,
  request_parameter_supported: false,
  request_uri_parameter_supported: true,
  require_request_uri_registration: false,
  op_policy_uri: :undefined,
  op_tos_uri: :undefined,
  revocation_endpoint: :undefined,
  revocation_endpoint_auth_methods_supported: ["client_secret_basic"],
  revocation_endpoint_auth_signing_alg_values_supported: :undefined,
  introspection_endpoint: :undefined,
  introspection_endpoint_auth_methods_supported: ["client_secret_basic"],
  introspection_endpoint_auth_signing_alg_values_supported: :undefined,
  code_challenge_methods_supported: :undefined,
  end_session_endpoint: :undefined,
  extra_fields: %{}
}

The test passes with the latest published oidcc release but fails with the current main.
However: It seems like this is a bug in the certification tooling. If I specify client_secret_jwt_alg HS256 in the config, I would expect that to show up in request_object_signing_alg_values_supported.
Since you were already in contact with the certification team: Would you mind to open an issue to see if this is intentional?


Refactor JsonSchema-to-Redsfhit transformation using new schema flattener

Original ticket

This has been addressed https://github.com/snowplow/iglu/issues/213


Error after installation

Hi,
First at all, thank you for your work!. I am obtaining a problem installing your library, o better say when I do "import exmol", I obtaing one error:"No module named 'dataclasses'".
I have installed as: pip install exmol...
Thanks!

Oh sorry is another package, I thought that It was an package of exmol sorry

Hi @PARODBE  thanks for pointing this out. This is actually an issue - maybe it should be a dependency. Can you tell me what version of Python you have? I believe dataclasses should be part of the standard library.

Hi,
Finally, I can work with te library. I have some questions. The first would be, when you show the counterfactual, you obtain your selected molecule and 3 more molecules from the same set of molecules that your selected ? In other words, in my case the selected molecule is part of test set, therefore the other 3 molecules with which the similarity is extracted are part of the test set or training set.
Another question is, are there any possibility in order to show the name of the molecule in the image?
And the last question, how would it be possible to pass a list of molecules?
My code is:
model=RandomForestClassifier(random_state=46)
model.fit(X_train_fp,tr.Activity)
def model_eval(smi_list, _=None):
mols = [Chem.MolFromSmiles(smi) for smi in smi_list]
feats = np.array([AllChem.GetMorganFingerprintAsBitVect(mol,radius=2,nBits=2048,useFeatures=True) for mol in mols])
labels = model.predict(feats).astype('int')
return labels
i=9
space = exmol.sample_space(Chem.MolToSmiles(mol_test[i]), model_eval)
mol_test[i]
exps = exmol.cf_explain(space)
fkw = {'figsize': (8,6)}
plt.rc('axes', titlesize=12)
exmol.plot_cf(exps, figure_kwargs=fkw, mol_size=(450,400), nrows=1)
plt.savefig('rf-simple.png', dpi=180)
svg = exmol.insert_svg(exps, mol_fontsize=14)
import skunk
font = {'family' : 'normal',
'weight' : 'normal',
'size'   : 22}
exmol.plot_space(space, exps, figure_kwargs=fkw, mol_size=(300,200), offset=0, cartoon=True, rasterized=True)
plt.scatter([], [], label='Counterfactual', s=150, color=plt.get_cmap('viridis')(1.0))
plt.scatter([], [], label='Same Class', s=150, color=plt.get_cmap('viridis')(0.0))
plt.legend(fontsize=22)
plt.tight_layout()
svg = exmol.insert_svg(exps, mol_fontsize=14)

This is what I think..
Question 1: In other words, in my case the selected molecule is part of test set, therefore the other 3 molecules with which the similarity is extracted are part of the test set or training set?
Not necessarily. First, we use the STONED algorithm to generate a “new set” of molecules. Then we use Tanimoto similarity to find similar molecules. Therefore, similar molecules can then be a part of your initial dataset or it can be a completely new molecule.
Question 2: are there any possibility in order to show the name of the molecule in the image?
We have not added the function to print the name/SMILES string of the molecule (or any other label) along with similarity when generating the images. But yes it is possible to add the SMILES string or a different label by editing the code locally.
Take a look at this code: https://github.com/ur-whitelab/exmol/blob/fe373d921d84fe0436d8b6554c113f8fe7cf8b03/exmol/exmol.py#L409
Question 3: And the last question, how would it be possible to pass a list of molecules?
Not sure what you mean by the list of molecules. You mean a list of base molecules?

Thanks for your answers! Regarding to third question/answer, yes, I want to say a lis of base molecules. By the way, the stoned algorithm is defined in your paper, no? For understand better, I don't know if it is a GAN for example or a VAE or another generative model.

Right now you cannot pass a list of base molecules unless you enumerate through it. Also, this is the reference to the STONED algorithm


Container stats show up on host page but not detailed container page (for RancherOS hosts)

RancherOS v0.4.0
Rancher v0.431
On the host page, it appears that the container stats are being read.

But on the detailed container page, the stats are stuck in "Connecting" .

Same issue here. Host stats working, container starts not. RancherOS v0.4.1, Rancher v0.47.0.

Also have same issue:
RancherOS v0.4.1
RancherServer v0.47.0
rancher/agent-instance:v0.6.0
rancher/agent:v0.8.2
Next errors from console:
[Report Only] Refused to load the script 'http://ip.ip.ip.ip:49152/livereload.js?snipver=1' because it violates the following Content Security Policy directive:
[Report Only] Refused to execute inline script because it violates the following Content Security Policy directive:

Verified with v0.49.0-rc1

@deniseschannon
this issue just appeared to us after we upgraded from rancher v1.0.2 to v1.1.0.
we are on CentOS7 hosts (3.10.0-327.13.1.el7.x86_64) with docker engine 1.10.3 (not RancherOS).


Excessive memory usage when minimax search depth > ~7

Describe the bug
Game with search-depth > 7 crashes, apparently due to high system memory usage.
To Reproduce
Steps to reproduce the behavior:

Install using procedure at  https://github.com/duanegoodner/xiangqigame.
From command line, run play_xiangqi -rst 8 -bst 8
Eventually (usually after ~30 moves) program crashes, with terminal message similar to:

[1]    1839133 killed

Expected behavior
Game runs to completion (either RED WINS, BLACK WINS, or DRAW), and program terminates without crashing.
Desktop (please complete the following information):

OS: Debian 12 Docker container on Debian 12 host with 64 GB system memory
Terminal type: Gnome

Additional context
Based on memory usage output by running top in another terminal while game is running, appears OS is killing process because of high memory usage. Likely due to TranspositionTable becoming very large.
Potential Solution
Implement TranspositionTable pruning to remove old / un-useful entries.

Prototype solutions and some of the infrastructure needed for a background pruning thread done in PRs: [#80]


add support for am processor, and option to use arm performance library

Arm based processors has been gaining popularity in HPC because its power efficiency. There are also substantial work to improve the performance like NEON and SVE technology and arm performance library (armpl).
This PR added configuration to allow the user to compile and run HPCCG on latest arm processor with auto-vectorization. Another improvement is to enable the user to use sparse matrix operations from armpl.
The recommended compilers (for arm) are gcc 10.2 or arm compiler for linux (acfl).  Acfl can be obtained from https://developer.arm.com/downloads/-/arm-compiler-for-linux.
example compile commands:

gcc compiler (make COMPILER=gnu LIBRARY=none)
acfl compiler (make COMPILER=arm LIBRARY=none)
gcc compiler with armpl (make COMPILER=gnu LIBRARY=armpl)
acfl with armpl (make COMPILER=arm LIBRARY=armpl)

Please feel free to contact me for more information.
Regards,
Jun

I will remove the flag for manual vectorization and create another PR.


Move omnibar to top

Move the omnibar to the top of the screen and try to unify with the UI elements displayed at the top.
Acceptance Criteria

Definition of Done

[ ]  Tested against acceptance criteria
[ ] Tested against the assumptions of the user story
[ ] The project builds without errors
[ ] Unit tests are written and passing
[ ] Tests on devices/browsers listed in the issue have passed
[ ] QA performed & issues resolved
[ ] Refactoring completed
[ ] Any configuration or build changes documented
[ ] Documentation updated
[ ] Peer Code Review performed

Done.


Remove metadata reading in the github actions

This step of the workflow reads a variable, to be used by the upload action.
https://github.com/Caaz/rimworld-creepers/blob/main/.github/workflows/Build.yml#L17-L21
We should remove that step, and modify the following line
https://github.com/Caaz/rimworld-creepers/blob/main/.github/workflows/Build.yml#L27
By uploading the action simply as "Creepers"

Would something like
env:
  mod_name: Creepers

# ...

    - uses: actions/upload-artifact@v2
      with:
        name: ${{ env.mod_name }}

be preferable?

That's probably smarter than hardcoding it actually, sure.


net: ip: Fix IPv6 RS message doesn't comply RFC4291

Fixes #26814
Signed-off-by: Eug Krashtan<EMAIL_ADDRESS>

@Delsian: Thanks for the patch, but can you please provide more detailed commit message, which explains how it didn't comply with RFC4291 before. https://github.com/zephyrproject-rtos/zephyr/issues/26814 as submited by you effectively has it all:

Current implementation of net_ipv6_send_rs() uses net_ipv6_addr_create_ll_allnodes_mcast() for creating RS packet. Next we send RS packet to all-nodes destination [FF02::1], but RFC42 requires all-routers destination for RS packet.

That paragraph should just go into the commit message. (Except note typo in the RFC number, made bold above.)

@pfalcon: Thanks for comment, done.


fixups for pr #875

bpfman-operator fixups to pr #875
Address comments from astoycos plus a few additional cleanups.
This doesn't contain any functional changes from pr #875, but rather optimizations, better organization, and general cleanup.

@astoycos, Thanks for your detailed review!  I think I addressed your comments one way or another in this pr.  In cases where I didn't, or where I thought my change was questionable, I added your comment to this pr and responded to it rather than trying to respond in the old pr.

This should be ready.


Bitwise XOR missing as a lax.reduce operation

The lax.reduce function includes bitwise AND and OR as possible reduction operations, but does not include XOR. Likewise, there is no specialized XOR-reduction function in JAX.

No -- I don't know how to add that. Are there bitwise AND and OR reductions
already? Or are there only those reductions for logical operations?
-- Jeremiah Willcock
On Wed, Jun 8, 2022 at 10:57 AM Jake Vanderplas @.***>
wrote:

Thanks for the report – I think it would be reasonable to add a reduce_xor
primitive, modeled after reduce_or and reduce_and. Are you interested in
contributing?
—
Reply to this email directly, view it on GitHub
https://github.com/google/jax/issues/11005#issuecomment-1150223086, or
unsubscribe
https://github.com/notifications/unsubscribe-auth/AAX34KHHQ2MDWYQJWWKYVSLVODNHXANCNFSM5YBQ2K4Q
.
You are receiving this because you authored the thread.Message ID:
@.***>

I was looking at _get_monoid_reducer in
https://jax.readthedocs.io/en/latest/_modules/jax/_src/lax/lax.html#reduce;
is that relevant to what reduction ops are available?
-- Jeremiah Willcock
On Wed, Jun 8, 2022 at 11:18 AM Jake Vanderplas @.***>
wrote:

Yes, there are bitwise AND and OR reductions currently. I may be confused,
because I thought you had referred to the existing bitwise AND and OR
reductions in your initial comment. Is there something I'm not
understanding about your request?
—
Reply to this email directly, view it on GitHub
https://github.com/google/jax/issues/11005#issuecomment-1150242523, or
unsubscribe
https://github.com/notifications/unsubscribe-auth/AAX34KH6PIZSBMAYWB5FO5TVODPZBANCNFSM5YBQ2K4Q
.
You are receiving this because you authored the thread.Message ID:
@.***>

Yes, that's relevant here. And you'll see the list of currently available reducers here: https://github.com/google/jax/blob/879a94a05405a84d677d0ab049cfefe0169dc22b/jax/_src/lax/lax.py#L970-L990

Note that even without the specialization, you can currently compute an xor reduction manually:
from jax import lax
import jax.numpy as jnp
result = lax.reduce(jnp.arange(6, dtype='uint8'), jnp.uint8(0), lax.bitwise_xor, (0,))
print(result)
# 1


Support for the CV32E40X

Repository at https://github.com/litex-hub/pythondata-cpu-cv32e40x
Uploaded to PyPi @ https://pypi.org/project/pythondata-cpu-cv32e40x/ (and test.pypi.org at https://test.pypi.org/project/pythondata-cpu-cv32e40x/).


Version 2.3.179 creates VB syntax error in ThisAssembly class

The new version 2.3.179 creates invalid code for Visual Basic projects for the ThisAssembly class.
The produced code is the following:
<Assembly: System.Reflection.AssemblyVersionAttribute("…")>
<Assembly: System.Reflection.AssemblyFileVersionAttribute("…")>
<Assembly: System.Reflection.AssemblyInformationalVersionAttribute("…")>
#If NETSTANDARD Or NETFRAMEWORK Or NETCOREAPP Then
<System.CodeDom.Compiler.GeneratedCode("Nerdbank.GitVersioning.Tasks","<IP_ADDRESS>651")>
#End If
Partial Friend NotInheritable Class ThisAssembly
    …
End Class

This is not valid in Visual Basic, because it's illegal for anything but more attributes to be written between the declaration of a type (or a member for that matter) and the attribute that will be applied to it. This includes the #End If instruction.
The only way to build what you did here as valid code is to include the declaration of the class in the header. Like so:
<Assembly: System.Reflection.AssemblyVersionAttribute("…")>
<Assembly: System.Reflection.AssemblyFileVersionAttribute("…")>
<Assembly: System.Reflection.AssemblyInformationalVersionAttribute("…")>
#If NETSTANDARD Or NETFRAMEWORK Or NETCOREAPP Then
<System.CodeDom.Compiler.GeneratedCode("Nerdbank.GitVersioning.Tasks","<IP_ADDRESS>651")>
Partial Friend NotInheritable Class ThisAssembly
#Else
Partial Friend NotInheritable Class ThisAssembly
#End If
    …
End Class

This is a vb compiler or .NET SDK issue. It only repros for .NET SDK style projects.
I filed https://github.com/dotnet/roslyn/issues/36365

Thanks for the report, @mkaring!


Handle merging different base paths

Overview
Currently, merging expects the same base path / root server URL, however, sometimes the specs to be merged can have different base paths.
Requirements
Description: enhance merging to handle merging different base paths.

Create absolute paths using OpenAPI3 server urls. Optionally error out if base path is different aside from host name.
Build all paths with absolute path. Optionally scan all paths an extract either hard coded root, or implicit base path on similarity.
The priority is for OpenAPI3 but will look to make the code generic so it can be added to OpenAPI2 if needed.

This is now accomplished by SpecPathsModify and SpecMoreModifyMulti:

SpecPathsModify: https://github.com/grokify/swaggman/blob/master/openapi3/modify/paths.go
SpecMoreModifyMulti: https://github.com/grokify/swaggman/blob/master/openapi3/modify/multi.go


RuntimeError: CUDA error: no kernel image is available for execution on the device

is a p5200 enough for this?
]
Traceback (most recent call last):
File "/home/user/mamba/simplermambassm.py", line 259, in
losses = estimate_loss()
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/home/user/mamba/simplermambassm.py", line 94, in estimate_loss
logits, loss = model(X,Y)
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/home/user/mamba/simplermambassm.py", line 210, in forward
x = self.blocks(x) # (B,T,C_e)
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/nn/modules/container.py", line 215, in forward
input = module(input)
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/home/user/mamba/simplermambassm.py", line 188, in forward
x = x + self.sa_head(self.ln1(x))
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
return forward_call(*args, **kwargs)
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/mamba_ssm/modules/mamba_simple.py", line 149, in forward
out = mamba_inner_fn(
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/mamba_ssm/ops/selective_scan_interface.py", line 306, in mamba_inner_fn
return MambaInnerFn.apply(xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight,
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/autograd/function.py", line 539, in apply
return super().apply(*args, **kwargs)  # type: ignore[misc]
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/cuda/amp/autocast_mode.py", line 113, in decorate_fwd
return fwd(*args, **kwargs)
File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/mamba_ssm/ops/selective_scan_interface.py", line 181, in forward
conv1d_out = causal_conv1d_cuda.causal_conv1d_fwd(x, conv1d_weight, conv1d_bias, True)
RuntimeError: CUDA error: no kernel image is available for execution on the device
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.

@tridao (I am not sure if this is just a hack, but for us old guys with CCC < 7, can we do this?)
I see that the Quadro P5200 has Cuda Compute capability 6.1.  I saw the same error with my GeForce GTX 1070 (which is also Compute Capability 6.1)
I was able to fix it by compiling the causal-conv1d dependency from source, as follows:
git clone https://github.com/Dao-AILab/causal-conv1d.git
cd causal-conv
# edit setup.py to add the lines here:
    cc_flag.append("-gencode")
    cc_flag.append("arch=compute_60,code=sm_60")

Here is where you need to add those lines.
Then, compile it from source with:
CAUSAL_CONV1D_FORCE_BUILD=TRUE pip install .
You can use the following script to test whether it is working properly:
import torch
from causal_conv1d import causal_conv1d_fn

batch, dim, seq, width = 10, 5, 17, 4
x = torch.zeros((batch, dim, seq)).to('cuda')
weight = torch.zeros((dim, width)).to('cuda')
bias = torch.zeros((dim, )).to('cuda')

causal_conv1d_fn(x, weight, bias, None)

close
>>> y = model(x)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/mamba_ssm/modules/mamba_simple.py", line 149, in forward
    out = mamba_inner_fn(
  File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/mamba_ssm/ops/selective_scan_interface.py", line 306, in mamba_inner_fn
    return MambaInnerFn.apply(xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight,
  File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/autograd/function.py", line 539, in apply
    return super().apply(*args, **kwargs)  # type: ignore[misc]
  File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/cuda/amp/autocast_mode.py", line 113, in decorate_fwd
    return fwd(*args, **kwargs)
  File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/mamba_ssm/ops/selective_scan_interface.py", line 181, in forward
    conv1d_out = causal_conv1d_cuda.causal_conv1d_fwd(x, conv1d_weight, conv1d_bias, True)
TypeError: causal_conv1d_fwd(): incompatible function arguments. The following argument types are supported:
    1. (arg0: torch.Tensor, arg1: torch.Tensor, arg2: Optional[torch.Tensor], arg3: Optional[torch.Tensor], arg4: bool) -> torch.Tensor

Invoked with: tensor([[[-0.4806,  1.2685,  0.3929,  ...,  0.3327,  0.3938, -0.5350],
         [ 0.9421, -0.1715, -0.0481,  ..., -0.1955, -0.8604, -0.4096],
         [ 0.5454, -0.1034, -0.2881,  ...,  0.2157, -1.2089, -0.3394],
         ...,
         [ 0.3014,  0.2976, -0.3656,  ..., -0.4423, -0.8560, -0.3013],
         [-0.3690, -0.3119, -0.1994,  ..., -0.4742, -0.6223,  0.2423],
         [-0.7320,  1.4818,  0.6340,  ..., -0.4294,  0.2926, -0.0436]],

        [[ 0.4325, -0.4794,  0.4466,  ...,  0.1774,  0.8001, -0.0083],
         [-0.2831, -0.2780,  0.3027,  ...,  0.3467, -1.0696,  0.2190],
         [-0.7058,  0.7942, -0.5447,  ...,  0.5141, -0.9554, -0.0649],
         ...,
         [-0.7701,  0.9309, -0.6030,  ...,  0.2993, -0.0422, -0.1484],
         [ 0.5808,  0.4285, -0.5568,  ...,  1.3064, -1.0199, -0.3363],
         [ 0.0734,  0.0993,  0.6768,  ..., -0.1356,  0.9295, -0.1664]]],
       device='cuda:0', requires_grad=True), tensor([[-0.0555,  0.4169,  0.2594, -0.4943],
        [-0.0554,  0.0376,  0.1702,  0.4476],
        [-0.1875,  0.4470,  0.2299, -0.0788],
        [-0.2496,  0.4405, -0.0241,  0.0307],
        [ 0.2666, -0.2731, -0.1284, -0.3504],
        [ 0.2001,  0.1497,  0.2172,  0.1289],
        [ 0.3474,  0.3953,  0.2375,  0.0597],
        [ 0.0498,  0.1374, -0.0508, -0.1526],
        [-0.2388, -0.2890, -0.4515,  0.0008],
        [-0.2706, -0.4276, -0.4668,  0.4245],
        [ 0.0252,  0.0295, -0.4991,  0.2078],
        [ 0.2212,  0.3381, -0.3815,  0.1831],
        [-0.3029, -0.3729, -0.1333, -0.1371],
        [-0.3745,  0.0316, -0.1675,  0.0064],
        [ 0.4358,  0.4920, -0.4541, -0.0722],
        [ 0.2807, -0.1016, -0.4563, -0.3044],
        [ 0.1035,  0.0162,  0.4479,  0.3260],
        [-0.2877,  0.1106,  0.4981,  0.4084],
        [-0.3320, -0.3829, -0.1360,  0.3744],
        [-0.3771, -0.3639, -0.1163,  0.3709],
        [-0.2274, -0.4964, -0.0816,  0.4454],
        [ 0.1764, -0.0485,  0.3448, -0.4393],
        [-0.3905, -0.3605,  0.0623, -0.2038],
        [-0.2044, -0.1454, -0.1526, -0.4165],
        [-0.0414,  0.1940,  0.3441, -0.3418],
        [ 0.4200, -0.2309,  0.1998, -0.1196],
        [-0.4553,  0.1990,  0.4579,  0.1669],
        [-0.3292,  0.0408, -0.4167,  0.3332],
        [ 0.4237,  0.4848, -0.3006, -0.2292],
        [ 0.4939,  0.1801, -0.1294,  0.0011],
        [ 0.3516, -0.3912,  0.3251,  0.3016],
        [-0.0648, -0.0567, -0.3247,  0.4323]], device='cuda:0',
       requires_grad=True), Parameter containing:
tensor([-3.1444e-01,  4.3207e-02,  2.2112e-01, -3.4120e-01,  4.0195e-01,
        -1.4227e-01, -4.5976e-01, -3.6258e-04, -4.6205e-01,  1.7177e-01,
         4.6020e-01, -1.7618e-01,  2.0168e-01,  1.2738e-01,  2.8975e-01,
        -4.2130e-01, -2.3378e-01, -1.8998e-01, -9.5853e-02, -2.4321e-01,
        -1.0333e-02, -2.0879e-01,  1.2288e-01,  5.1831e-02, -4.9842e-02,
        -3.1233e-01,  1.4064e-01, -2.4546e-01,  3.0703e-01,  1.4846e-02,
         7.5587e-02, -3.6691e-01], device='cuda:0', requires_grad=True), True
>>>

oops, I did it out of order

btw, I had to do something similar to get ctransformers to work

oops, sorry but I forgot a crucial thing.  Mamba states that it requires causal_conv2d version <= 1.0.2.  I forgot to mention this.  So, you need to do a git checkout v1.0.2 before you do
you'll now have to do:
$ cd causal-conv1d
$ git checkout v1.0.2
# you've already edited the setup.py file I assume
$ pip uninstall causal-conv1d
$ CAUSAL_CONV1D_FORCE_BUILD=TRUE pip install .

At this point, it may work ;)  Since Mamba dynamically loads the causal-conv1d python module, no re-compilation of mamba is necessary.  But I am not positive of that.

Sorry I'm traveling this week but will have time to look into this next week.

Processing /home/user/mamba/causal-conv1d
  Installing build dependencies ... done
  Getting requirements to build wheel ... error
  error: subprocess-exited-with-error

  × Getting requirements to build wheel did not run successfully.
  │ exit code: 1
  ╰─> [17 lines of output]
      Traceback (most recent call last):
        File "/home/user/lit-gpt/env/lib/python3.10/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 353, in <module>
          main()
        File "/home/user/lit-gpt/env/lib/python3.10/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 335, in main
          json_out['return_val'] = hook(**hook_input['kwargs'])
        File "/home/user/lit-gpt/env/lib/python3.10/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 118, in get_requires_for_build_wheel
          return hook(config_settings)
        File "/tmp/pip-build-env-w4x0ekut/overlay/lib/python3.10/site-packages/setuptools/build_meta.py", line 325, in get_requires_for_build_wheel
          return self._get_build_requires(config_settings, requirements=['wheel'])
        File "/tmp/pip-build-env-w4x0ekut/overlay/lib/python3.10/site-packages/setuptools/build_meta.py", line 295, in _get_build_requires
          self.run_setup()
        File "/tmp/pip-build-env-w4x0ekut/overlay/lib/python3.10/site-packages/setuptools/build_meta.py", line 480, in run_setup
          super(_BuildMetaLegacyBackend, self).run_setup(setup_script=setup_script)
        File "/tmp/pip-build-env-w4x0ekut/overlay/lib/python3.10/site-packages/setuptools/build_meta.py", line 311, in run_setup
          exec(code, locals())
        File "<string>", line 9, in <module>
      ModuleNotFoundError: No module named 'packaging'
      [end of output]

  note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error

× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> See above for output.

note: This error originates from a subprocess, and is likely not a

despite installing python3-packaging and pip install packaging (and can confirm I can import packaging)

nm
pip install wheel
python setup.py

yay, that did it
back in the game
=D

close
>>> y = model(x)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/mamba_ssm/modules/mamba_simple.py", line 149, in forward
    out = mamba_inner_fn(
  File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/mamba_ssm/ops/selective_scan_interface.py", line 306, in mamba_inner_fn
    return MambaInnerFn.apply(xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight,
  File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/autograd/function.py", line 539, in apply
    return super().apply(*args, **kwargs)  # type: ignore[misc]
  File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/torch/cuda/amp/autocast_mode.py", line 113, in decorate_fwd
    return fwd(*args, **kwargs)
  File "/home/user/miniconda3/envs/textgen/lib/python3.10/site-packages/mamba_ssm/ops/selective_scan_interface.py", line 181, in forward
    conv1d_out = causal_conv1d_cuda.causal_conv1d_fwd(x, conv1d_weight, conv1d_bias, True)
TypeError: causal_conv1d_fwd(): incompatible function arguments. The following argument types are supported:
    1. (arg0: torch.Tensor, arg1: torch.Tensor, arg2: Optional[torch.Tensor], arg3: Optional[torch.Tensor], arg4: bool) -> torch.Tensor

Invoked with: tensor([[[-0.4806,  1.2685,  0.3929,  ...,  0.3327,  0.3938, -0.5350],
         [ 0.9421, -0.1715, -0.0481,  ..., -0.1955, -0.8604, -0.4096],
         [ 0.5454, -0.1034, -0.2881,  ...,  0.2157, -1.2089, -0.3394],
         ...,
         [ 0.3014,  0.2976, -0.3656,  ..., -0.4423, -0.8560, -0.3013],
         [-0.3690, -0.3119, -0.1994,  ..., -0.4742, -0.6223,  0.2423],
         [-0.7320,  1.4818,  0.6340,  ..., -0.4294,  0.2926, -0.0436]],

        [[ 0.4325, -0.4794,  0.4466,  ...,  0.1774,  0.8001, -0.0083],
         [-0.2831, -0.2780,  0.3027,  ...,  0.3467, -1.0696,  0.2190],
         [-0.7058,  0.7942, -0.5447,  ...,  0.5141, -0.9554, -0.0649],
         ...,
         [-0.7701,  0.9309, -0.6030,  ...,  0.2993, -0.0422, -0.1484],
         [ 0.5808,  0.4285, -0.5568,  ...,  1.3064, -1.0199, -0.3363],
         [ 0.0734,  0.0993,  0.6768,  ..., -0.1356,  0.9295, -0.1664]]],
       device='cuda:0', requires_grad=True), tensor([[-0.0555,  0.4169,  0.2594, -0.4943],
        [-0.0554,  0.0376,  0.1702,  0.4476],
        [-0.1875,  0.4470,  0.2299, -0.0788],
        [-0.2496,  0.4405, -0.0241,  0.0307],
        [ 0.2666, -0.2731, -0.1284, -0.3504],
        [ 0.2001,  0.1497,  0.2172,  0.1289],
        [ 0.3474,  0.3953,  0.2375,  0.0597],
        [ 0.0498,  0.1374, -0.0508, -0.1526],
        [-0.2388, -0.2890, -0.4515,  0.0008],
        [-0.2706, -0.4276, -0.4668,  0.4245],
        [ 0.0252,  0.0295, -0.4991,  0.2078],
        [ 0.2212,  0.3381, -0.3815,  0.1831],
        [-0.3029, -0.3729, -0.1333, -0.1371],
        [-0.3745,  0.0316, -0.1675,  0.0064],
        [ 0.4358,  0.4920, -0.4541, -0.0722],
        [ 0.2807, -0.1016, -0.4563, -0.3044],
        [ 0.1035,  0.0162,  0.4479,  0.3260],
        [-0.2877,  0.1106,  0.4981,  0.4084],
        [-0.3320, -0.3829, -0.1360,  0.3744],
        [-0.3771, -0.3639, -0.1163,  0.3709],
        [-0.2274, -0.4964, -0.0816,  0.4454],
        [ 0.1764, -0.0485,  0.3448, -0.4393],
        [-0.3905, -0.3605,  0.0623, -0.2038],
        [-0.2044, -0.1454, -0.1526, -0.4165],
        [-0.0414,  0.1940,  0.3441, -0.3418],
        [ 0.4200, -0.2309,  0.1998, -0.1196],
        [-0.4553,  0.1990,  0.4579,  0.1669],
        [-0.3292,  0.0408, -0.4167,  0.3332],
        [ 0.4237,  0.4848, -0.3006, -0.2292],
        [ 0.4939,  0.1801, -0.1294,  0.0011],
        [ 0.3516, -0.3912,  0.3251,  0.3016],
        [-0.0648, -0.0567, -0.3247,  0.4323]], device='cuda:0',
       requires_grad=True), Parameter containing:
tensor([-3.1444e-01,  4.3207e-02,  2.2112e-01, -3.4120e-01,  4.0195e-01,
        -1.4227e-01, -4.5976e-01, -3.6258e-04, -4.6205e-01,  1.7177e-01,
         4.6020e-01, -1.7618e-01,  2.0168e-01,  1.2738e-01,  2.8975e-01,
        -4.2130e-01, -2.3378e-01, -1.8998e-01, -9.5853e-02, -2.4321e-01,
        -1.0333e-02, -2.0879e-01,  1.2288e-01,  5.1831e-02, -4.9842e-02,
        -3.1233e-01,  1.4064e-01, -2.4546e-01,  3.0703e-01,  1.4846e-02,
         7.5587e-02, -3.6691e-01], device='cuda:0', requires_grad=True), True
>>>

How did you solve this?

@tridao (I am not sure if this is just a hack, but for us old guys with CCC < 7, can we do this?)
I see that the Quadro P5200 has Cuda Compute capability 6.1. I saw the same error with my GeForce GTX 1070 (which is also Compute Capability 6.1)
I was able to fix it by compiling the causal-conv1d dependency from source, as follows:
git clone https://github.com/Dao-AILab/causal-conv1d.git
# this is the latest version that Mamba supports:
git checkout v1.0.2
cd causal-conv
# edit setup.py to add the lines here:
    cc_flag.append("-gencode")
    cc_flag.append("arch=compute_60,code=sm_60")

Here is where you need to add those lines.
Then, compile it from source with:
CAUSAL_CONV1D_FORCE_BUILD=TRUE pip install .
You can use the following script to test whether it is working properly:
import torch
from causal_conv1d import causal_conv1d_fn

batch, dim, seq, width = 10, 5, 17, 4
x = torch.zeros((batch, dim, seq)).to('cuda')
weight = torch.zeros((dim, width)).to('cuda')
bias = torch.zeros((dim, )).to('cuda')

causal_conv1d_fn(x, weight, bias, None)

EDIT: Just realized the Mamba repo also assumes CCC >= 7. So, I did a similar edit to the mamba setup.py and compiled it with:
henry@henry-gs65:mamba$ MAMBA_FORCE_BUILD=TRUE pip install .
(This takes about 10 minutes to compile)
Once doing this, the top-level Mamba demo works:
import torch

from mamba_ssm import Mamba

batch, length, dim = 2, 64, 16
x = torch.randn(batch, length, dim).to("cuda")
model = Mamba(
    # This module uses roughly 3 * expand * d_model^2 parameters
    d_model=dim, # Model dimension d_model
    d_state=16,  # SSM state expansion factor
    d_conv=4,    # Local convolution width
    expand=2,    # Block expansion factor
).to("cuda")
y = model(x)
assert y.shape == x.shape

causal_conv1d_fn(x, weight, bias, None)
Traceback (most recent call last):
File "", line 1, in
File "/home/ubuntu/Desktop/openmmlab/causal-conv1d/causal_conv1d/causal_conv1d_interface.py", line 46, in causal_conv1d_fn
return CausalConv1dFn.apply(x, weight, bias, activation)
File "/home/ubuntu/miniconda3/envs/torch/lib/python3.10/site-packages/torch/autograd/function.py", line 506, in apply
return super().apply(*args, **kwargs)  # type: ignore[misc]
File "/home/ubuntu/Desktop/openmmlab/causal-conv1d/causal_conv1d/causal_conv1d_interface.py", line 20, in forward
out = causal_conv1d_cuda.causal_conv1d_fwd(x, weight, bias, ctx.activation)
RuntimeError: CUDA error: no kernel image is available for execution on the device
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.

I'm sad that I can't install causal-conv1d correctly

@tridao (I am not sure if this is just a hack, but for us old guys with CCC < 7, can we do this?)
I see that the Quadro P5200 has Cuda Compute capability 6.1. I saw the same error with my GeForce GTX 1070 (which is also Compute Capability 6.1)
I was able to fix it by compiling the causal-conv1d dependency from source, as follows:
git clone https://github.com/Dao-AILab/causal-conv1d.git
# this is the latest version that Mamba supports:
git checkout v1.0.2
cd causal-conv
# edit setup.py to add the lines here:
    cc_flag.append("-gencode")
    cc_flag.append("arch=compute_60,code=sm_60")

Here is where you need to add those lines.
Then, compile it from source with:
CAUSAL_CONV1D_FORCE_BUILD=TRUE pip install .
You can use the following script to test whether it is working properly:
import torch
from causal_conv1d import causal_conv1d_fn

batch, dim, seq, width = 10, 5, 17, 4
x = torch.zeros((batch, dim, seq)).to('cuda')
weight = torch.zeros((dim, width)).to('cuda')
bias = torch.zeros((dim, )).to('cuda')

causal_conv1d_fn(x, weight, bias, None)

EDIT: Just realized the Mamba repo also assumes CCC >= 7. So, I did a similar edit to the mamba setup.py and compiled it with:
henry@henry-gs65:mamba$ MAMBA_FORCE_BUILD=TRUE pip install .
(This takes about 10 minutes to compile)
Once doing this, the top-level Mamba demo works:
import torch

from mamba_ssm import Mamba

batch, length, dim = 2, 64, 16
x = torch.randn(batch, length, dim).to("cuda")
model = Mamba(
    # This module uses roughly 3 * expand * d_model^2 parameters
    d_model=dim, # Model dimension d_model
    d_state=16,  # SSM state expansion factor
    d_conv=4,    # Local convolution width
    expand=2,    # Block expansion factor
).to("cuda")
y = model(x)
assert y.shape == x.shape

Oh, God, I solve it! Love from P40 (CCC 6.1)!!!

import torch

from mamba_ssm import Mamba

batch, length, dim = 2, 64, 16
x = torch.randn(batch, length, dim).to("cuda")
model = Mamba(
    # This module uses roughly 3 * expand * d_model^2 parameters
    d_model=dim, # Model dimension d_model
    d_state=16,  # SSM state expansion factor
    d_conv=4,    # Local convolution width
    expand=2,    # Block expansion factor
).to("cuda")
y = model(x)
assert y.shape == x.shape

same needs to be done for setup.py in mamba repo itself.
wonder why this hasn't been patched in by now

this solution no longer works for the latest mamba build
(mamba-venv) [root@pve-m7330 mamba]# python
Python 3.10.9 (main, Mar  8 2023, 10:47:38) [GCC 11.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.

import torch
d_state=16,  # SSM state expansion factor
d_conv=4,    # Local convolution width
expand=2,    # Block expansion factor
).to("cuda")
y = model(x)
assert y.shape == x.shape
from mamba_ssm import Mamba
/home/user/mamba/mamba_ssm/ops/selective_scan_interface.py:164: FutureWarning: torch.cuda.amp.custom_fwd(args...) is deprecated. Please use torch.amp.custom_fwd(args..., device_type='cuda') instead.
def forward(ctx, xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight,
/home/user/mamba/mamba_ssm/ops/selective_scan_interface.py:240: FutureWarning: torch.cuda.amp.custom_bwd(args...) is deprecated. Please use torch.amp.custom_bwd(args..., device_type='cuda') instead.
def backward(ctx, dout):
/home/user/mamba/mamba_ssm/ops/triton/layer_norm.py:986: FutureWarning: torch.cuda.amp.custom_fwd(args...) is deprecated. Please use torch.amp.custom_fwd(args..., device_type='cuda') instead.
def forward(
/home/user/mamba/mamba_ssm/ops/triton/layer_norm.py:1045: FutureWarning: torch.cuda.amp.custom_bwd(args...) is deprecated. Please use torch.amp.custom_bwd(args..., device_type='cuda') instead.
def backward(ctx, dout, *args):
/home/user/mamba/mamba_ssm/distributed/tensor_parallel.py:26: FutureWarning: torch.cuda.amp.custom_fwd(args...) is deprecated. Please use torch.amp.custom_fwd(args..., device_type='cuda') instead.
def forward(ctx, x, weight, bias, process_group=None, sequence_parallel=True):
/home/user/mamba/mamba_ssm/distributed/tensor_parallel.py:62: FutureWarning: torch.cuda.amp.custom_bwd(args...) is deprecated. Please use torch.amp.custom_bwd(args..., device_type='cuda') instead.
def backward(ctx, grad_output):
/home/user/mamba/mamba_ssm/ops/triton/ssd_combined.py:758: FutureWarning: torch.cuda.amp.custom_fwd(args...) is deprecated. Please use torch.amp.custom_fwd(args..., device_type='cuda') instead.
def forward(ctx, zxbcdt, conv1d_weight, conv1d_bias, dt_bias, A, D, chunk_size, initial_states=None, seq_idx=None, dt_limit=(0.0, float("inf")), return_final_states=False, activation="silu",
/home/user/mamba/mamba_ssm/ops/triton/ssd_combined.py:836: FutureWarning: torch.cuda.amp.custom_bwd(args...) is deprecated. Please use torch.amp.custom_bwd(args..., device_type='cuda') instead.
def backward(ctx, dout, *args):
batch, length, dim = 2, 64, 16
x = torch.randn(batch, length, dim).to("cuda")
model = Mamba(
...     # This module uses roughly 3 * expand * d_model^2 parameters
...     d_model=dim, # Model dimension d_model
...     d_state=16,  # SSM state expansion factor
...     d_conv=4,    # Local convolution width
...     expand=2,    # Block expansion factor
... ).to("cuda")
y = model(x)
Traceback (most recent call last):
File "", line 1, in
File "/home/user/mamba-venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/home/user/mamba-venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "/home/user/mamba/mamba_ssm/modules/mamba_simple.py", line 146, in forward
out = mamba_inner_fn(
File "/home/user/mamba/mamba_ssm/ops/selective_scan_interface.py", line 317, in mamba_inner_fn
return MambaInnerFn.apply(xz, conv1d_weight, conv1d_bias, x_proj_weight, delta_proj_weight,
File "/home/user/mamba-venv/lib/python3.10/site-packages/torch/autograd/function.py", line 574, in apply
return super().apply(*args, **kwargs)  # type: ignore[misc]
File "/home/user/mamba-venv/lib/python3.10/site-packages/torch/amp/autocast_mode.py", line 455, in decorate_fwd
return fwd(*args, **kwargs)
File "/home/user/mamba/mamba_ssm/ops/selective_scan_interface.py", line 187, in forward
conv1d_out = causal_conv1d_cuda.causal_conv1d_fwd(
TypeError: causal_conv1d_fwd(): incompatible function arguments. The following argument types are supported:

1. (arg0: torch.Tensor, arg1: torch.Tensor, arg2: Optional[torch.Tensor], arg3: bool) -> torch.Tensor

Invoked with: tensor([[[ 0.0585,  0.7326,  1.0051,  ...,  0.5815,  0.5123,  0.3603],
[-1.1954, -0.1859, -0.5755,  ...,  0.1994, -0.8212, -0.0045],
[-0.8660, -0.0906,  0.4311,  ...,  0.3252, -1.0255,  0.2714],
...,
[-0.2807, -0.6320, -0.0296,  ...,  0.0924, -0.1205,  0.0907],
[ 0.7265, -0.5012,  0.8706,  ..., -0.2371,  0.5663,  0.0296],
[-0.7008,  0.5792, -0.0274,  ...,  0.0996, -0.2764, -0.2743]],
    [[-0.1144,  0.0690,  0.9025,  ..., -0.4769, -0.3929, -0.0055],
     [ 0.6885,  0.4210, -0.0834,  ..., -0.0957,  0.4545, -1.1244],
     [ 0.6075,  0.0818,  0.1200,  ..., -0.2688, -0.4929,  0.0289],
     ...,
     [-0.6323, -0.2017, -0.3172,  ..., -0.2214,  0.8582,  1.2913],
     [ 0.3027, -0.1702,  0.2258,  ..., -0.6353,  0.1729, -0.2953],
     [ 1.0242,  0.3739, -0.4389,  ...,  0.5205,  0.1748, -0.4015]]],
   device='cuda:0', requires_grad=True), tensor([[ 0.0987,  0.1059,  0.1087,  0.2217],
    [-0.3802, -0.0909, -0.4198, -0.1257],
    [ 0.1083, -0.0419,  0.4580,  0.4761],
    [ 0.1094, -0.0357, -0.0371,  0.0531],
    [-0.3875,  0.1226, -0.1692,  0.1329],
    [-0.1424,  0.2907, -0.4435, -0.4014],
    [-0.2112,  0.4587,  0.0418,  0.4757],
    [ 0.1140,  0.3250,  0.3956, -0.4221],
    [-0.1109,  0.4271,  0.1018,  0.1395],
    [ 0.4083,  0.1258, -0.3790,  0.0996],
    [-0.0062, -0.2871,  0.3098,  0.3148],
    [-0.0467, -0.2842,  0.3562, -0.0613],
    [ 0.1013, -0.2330,  0.2027,  0.2846],
    [ 0.0039, -0.2095, -0.4826,  0.2009],
    [-0.2955, -0.1617, -0.0491,  0.3483],
    [-0.4664,  0.0722,  0.1840,  0.3535],
    [-0.0564,  0.2365, -0.3335,  0.1983],
    [-0.1127,  0.0549, -0.1763,  0.1116],
    [ 0.2882,  0.4756,  0.3223,  0.2688],
    [-0.2654,  0.0236,  0.3968,  0.2946],
    [-0.0341,  0.0547,  0.1876,  0.0800],
    [-0.2642, -0.2790,  0.3583,  0.2026],
    [-0.4669,  0.3040, -0.1916, -0.4390],
    [ 0.3570, -0.4490, -0.3143, -0.3155],
    [ 0.2841, -0.4582, -0.1350, -0.4604],
    [ 0.0170, -0.0625,  0.0056, -0.1275],
    [-0.3403,  0.2159, -0.1715, -0.2652],
    [-0.2765, -0.3144, -0.2965,  0.1824],
    [-0.0823, -0.2959, -0.2007,  0.1748],
    [ 0.3444,  0.4872, -0.4085,  0.1206],
    [-0.1521,  0.4988, -0.1553,  0.3104],
    [ 0.2039,  0.4049,  0.2656,  0.3132]], device='cuda:0',
   requires_grad=True), Parameter containing:

tensor([-0.4841, -0.3013,  0.4225, -0.3196,  0.1289, -0.2113,  0.2934,  0.0558,
-0.3115,  0.4327,  0.1833, -0.3552, -0.3535, -0.3619, -0.2438,  0.3835,
-0.0902, -0.0893,  0.1190,  0.1235,  0.3639,  0.2415,  0.0895,  0.0057,
-0.1587,  0.4039, -0.0957, -0.0197, -0.4331, -0.4305, -0.3638, -0.2179],
device='cuda:0', requires_grad=True), None, None, None, True

assert y.shape == x.shape
Traceback (most recent call last):
File "", line 1, in
NameError: name 'y' is not defined

I'm going to try a newer causal-conv1d than 1.0.2, but these instructions used to work... why not include compute 6.0 in the source rather than have us patch it in?  Compute 5.3 is in there.

disregard.  Works when I compile with causal-conv1d v1.4.0 with compute_60 patched into setup.py
although, this makes a good argument why not include this by default?

unfortunately
A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.1.1 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

when trying to load these libraries together
import torch
import optuna
from transformers import AutoTokenizer, TrainingArguments, AutoConfig
from trl import SFTTrainer
from peft import LoraConfig, get_peft_model
#from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel
#from mamba_ssm.models.config_mamba import MambaConfig
from langchain.text_splitter import RecursiveCharacterTextSplitter
from nltk.corpus import brown
import os
import nltk
import random
I would think if this patch was better integrated, this wouldn't happen

nevermind, I fixed that by
pip install numpy==1.*


Not working when ranging beacon

Using the function Ranging beacon, I got the error like this:  java.lang.NoClassDefFoundError: Failed resolution of: Landroid/support/v4/content/LocalBroadcastManager. Caused by: java.lang.NoClassDefFoundError: Class not found using the boot class loader; no stack trace available. I dont know how to fix this issue. Is there anyone know how to solve this problem ?

Hi @1553011
What phone do you use for testing? Android version? I use Sams*ng S5 and S10+ and there are no error happen.

same problem happen with samsung s7 (lastest android os version)

I am also getting this exact error. It worked perfectly a month ago, but in the meantime I've updated dart to Dart version 2.3.0 (build 2.3.0-dev.0.5 a1668566e5). When booting up my project this morning I got the error. I'm on a Samsung Galaxy S6.

This is not working on Samsung phones. I dug in to check what's the issue, found that it uses this version of the library
api 'org.altbeacon:android-beacon-library:2.16.1'
but there is an update on the library available here and it mentions a fix on Samsung specific phone.
I have updated the version of the library and created a PR here.
@alann-maulana  Can you merge that?

I believe this is because the package is using an old appcompat library: upgrading it to AndroidX should solve the problem. @alann-maulana, is that something you're looking at doing?

Sorry dudes, having a lot of homework last month. Already merge it. I hope it will fix your issue. Close it now :)

I was having the same error, for hours trying to find a solution, and the solutions is simple add this to build.gradle
implementation 'androidx.localbroadcastmanager:localbroadcastmanager:1.0.0'

Great catch @isaiasmac
I'll consider adding those line to README within FAQ parts 😄


tf.volume.resize

System information

TensorFlow version (you are using): 1.14
Are you willing to contribute it (Yes/No): Yes

Describe the feature and the current behavior/state.
tf.image.resize works for 2D images, would be nice with tf.volume.resize for 3D volumes, instead of using tf.keras.layers.UpSampling3D that only performs nearest neighbor interpolation.
Will this change the current api? How?
I don't think so
Who will benefit with this feature?
Many working with 3D data
Any Other info.

Is there any update on this?
Just as (TF2.2.0) tf.image.resize offers an option for bilinear interpolation, it would be great to have a tf.volume.resize that offers trilinear interpolation.
The closest thing right now is (TF2.2.0) tf.keras.backend.resize_volumes but that repeats elements when you look into its source code.

+1 Please add this functionality similar to torch.nn.functional.interpolate

Hi There,
We are checking to see if you still need help on this, as you are using an older version of tensorflow which is officially considered end of life . We recommend that you upgrade to the latest 2.x version and let us know if the issue still persists in newer versions. Please open a new issue for any help you need against 2.x, and we will get you the right help.
This issue will be closed automatically 7 days from now. If you still need help with this issue, please provide us with more information.

Any update on this feature request? I would love to see it implemented as well.

Same question. Will you update this feature?

any updates?

It is submitted again in https://github.com/keras-team/keras/issues/16260
You could subscribe and comment there.


Add a --no-wait command line option

Issue Type: Feature Request
When running code from the commad line and processing stdin, the process always waits until the file has been closed.
This was previously referenced in #39682, and has been closed as as-designed.
I feel that the process should not wait by default.  I understand the reason given, but it does not apply in all cases.
Would be nice if there were a --no-wait option so that the current default behavior could be overridden.
VS Code version: Code 1.23.0 (7c7da59c2333a1306c41e6e7b68d7f0caa7b3d45, 2018-05-03T15:23:14.634Z)
OS version: Darwin x64 17.5.0

Ended up removing the --wait when reading from stdin: https://github.com/microsoft/vscode/issues/199035


Application does not filter by groupId

Description:
When we give groupId as a parameter in the application search rest API it drops in the process of the request.
Suggested Labels:

Suggested Assignees:

Affected Product Version:
OS, DB, other environment details and versions:
Steps to reproduce:
Related Issues:

Closing the issue as this is older than 3 years. Please reopen if the issue is valid still.


TFLite 2.2.0 delegate API not working with Edge TPU runtime

Tensorflow-lite API : Release r2.2.0
EdgeTPU runtime : latest version of 1st April 2020 https://dl.google.com/coral/edgetpu_api/edgetpu_runtime_20200331.zip
Hello,
I'm trying to run an inference using the release r2.2.0 of the tensorflow-lite API and especially the delegates API by adding an experimental delegate to the interpreter instantiation. For the EdgeTPU runtime I'm using the version mentioned above.
interpreter = tflite.Interpreter(self._model_file,                                              experimental_delegates=[tflite.load_delegate('libedgetpu.so.1')])
I'm getting this error RuntimeError: Internal: Unsupported data type in custom op handler: -1342003056Node number 1 (EdgeTpuDelegateForCustomOp) failed to prepare.

Duplication of this issue: https://github.com/google-coral/edgetpu/issues/44
@othmaneAZ
Unfortunately, tensorflow package will no longer work with libedgetpu.so at this time. Please use the tflite_runtime package instead of the full tensorflow package as documented here.

I realize this is closed - but the language of the referenced page certainly doesn't make the limitation clear. It specifically says "you can access from the full tensorflow package"
_Although you can access the TensorFlow Lite API from the full tensorflow Python package, we recommend you instead use the tflite_runtime package. This package includes only the Interpreter class and load_delegate() function, which is all that's required to run inference, saving you a lot of disk space.
Shouldn't this say "You must use the tflite_runtime package. The full Tensorflow lite API is not currently supported"?

@lawrence-jeff woa, thanks for the suggestion, that is indeed true. One way that should works in theory is if you build your own tensorflow package from source using the same commit as our shared library: https://github.com/google-coral/edgetpu/blob/master/WORKSPACE#L5
But that's totally not something that we'd want users to have to do


Removed trailing whitespace, made list of partitions sorted by intege…

…r value and marked file as executable

LGTM


Automated cherry pick of #8228: eip: construct a pointer type

Cherry pick of #8228 on release/3.4.
#8228: eip: construct a pointer type

Codecov Report

Merging #8229 into release/3.4 will decrease coverage by 2.52%.
The diff coverage is 0.00%.

@@              Coverage Diff               @@
##           release/3.4   #8229      +/-   ##
==============================================
- Coverage         6.22%   3.69%   -2.53%
==============================================
  Files              759     303     -456
  Lines           117145   61932   -55213
==============================================
- Hits              7289    2287    -5002
+ Misses          109197   59486   -49711
+ Partials           659     159     -500

Flag
Coverage Δ

#aFlag
3.69% <0.00%> (-2.53%)
:arrow_down:

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files
Coverage Δ

pkg/compute/models/buckets.go
1.10% <0.00%> (+0.03%)
:arrow_up:

pkg/compute/models/cachedimages.go
1.75% <0.00%> (-0.06%)
:arrow_down:

pkg/compute/models/cloudaccounts.go
5.13% <0.00%> (-0.01%)
:arrow_down:

pkg/compute/models/dns_recordsets.go
1.82% <0.00%> (-0.24%)
:arrow_down:

pkg/compute/models/elasticips.go
1.08% <0.00%> (-0.01%)
:arrow_down:

pkg/compute/models/networks.go
0.48% <0.00%> (-0.01%)
:arrow_down:

pkg/compute/models/schedtags.go
2.95% <0.00%> (-0.28%)
:arrow_down:

pkg/compute/models/secgroups.go
1.53% <0.00%> (-0.01%)
:arrow_down:

pkg/compute/models/wires.go
1.24% <0.00%> (-0.01%)
:arrow_down:

pkg/multicloud/esxi/ip.go
0.00% <0.00%> (ø)

... and 454 more

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fix(theme-default): Capitalise the 'P' in 'Next page'

Summary
In the previous/next page links at the bottom of docs pages, the text used to read "Previous Page" (title case) and "Next page" (sentence case). This changes the "Next Page" link to title case so that the two links match style.

Checklist

[x] Tests updated (or not required).
[x] Documentation updated (or not required).

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


Adds from prop to navigation state for better pop animations

It has conflicts as well. Probably I've merged your PRs in wrong ordering :)


Add merge workflow

closes #6
Here I am adding a module with the merge_sce workflow, ported from https://github.com/AlexsLemonade/scpca-nf.
As noted in the readme, I made a few changes along the way.
Most of these were simplify some assumptions and make the workflow work with the input of a list of project folders rather than from a sample sheet.
One part of this meant that I had to figure out whether the adt was present for each sample, as we use that to determine a few things... I ended up doing this by looking for _adt.h5ad files, which seemed the simplest way to make sure we had that information, but I really only use this variable in the AnnData conversion. I probably could have incorporated something to make it work by further modifying the export script, but I did not do that as yet. (I did modify the merge_sce.R script to look for ADTs directly, mostly because the script was already doing that in another context.)
Other than that, things do seem to run as expected. I tested it with SCPCP000012, and I am currently testing with SCPCP000007 to make sure the ADT component works as expected.
I am filing this as a draft partly because I want to make sure that the changes in progress to enable this workflow more automatically are not interrupted.

Since you last looked @allyhawkins, I added a function to get the files for a given project in a similar way to the download script. I expect to use this in some future updates, in particular when we might link up the simulation module to run through the rest of the workflow. This should remove some boilerplate in later modules in any case.
I also removed the has_adt value completely. The only real problem with this is that the stub workflow for moving AnnData doesn't know whether it has to make an _adt file. Which really should not be a big problem.
I'm still testing a bit, but the big hurdle was overcome, which was learning how Groovy handles named arguments. I can not emphasize how much they do not work the way I expected them to work.

Okay, I think this is ready to go in. The last thing I did was add some separate entrypoints to the main workflow, so we can run just the test or simulation portions separately if we want.


Node re-registration required to update the node labels

Added a note about the node labels.
/sig node
/kind cleanup

@sftim I added a reason why re-registration is needed. PTAL if you have time


Add a --recreate-lock-file workaround till fixed nix version is released

For some reason CI doesn't run on this commit lol

thanks for the fix


Getting "Rate limit exceeded" too often for a low-consumption use case

I'm getting rate-limited very often while testing a low-consumption use case. Doing this with https://github.com/openai/openai-realtime-console doesn't fire the rate limit. It's the same use case and volume (just one user). What could be happening under the hood? Maybe the LiveKit libraries do many extra calls?

what is your rate limit set to? we would keep a persistent websocket connection to OpenAI. it's not clear why it would consume more of your rate limit.

Hi @davidzhao! What do you mean by my rate limits? LiveKit side or OpenAI's rate limits?
After testing it more, I got a bit more information. I get those errors after interrupting the model several times. Maybe it's a LiveKit rate limit? I'm using the free plan for testing purposes.

@carloslfu is it solved?

I haven't tried again, but maybe it was. I see the @livekit/agents dependency was updated. I'll try again as soon as I have some free time.


Merge search to master

Summary of Changes
Include a summary of changes then remove this line
Tests
What tests did you add or modify and why? If no tests were added or modified, explain why. Remove this line
Documentation
What documentation did you add or modify and why? Add any relevant links then remove this line
CheckList
Make sure you have checked all steps below to ensure a timely review.

[ ] PR title addresses the issue accurately and concisely. Example: "Updates the version of Flask to v1.0.2"

In case you are adding a dependency, check if the license complies with the ASF 3rd Party License Policy.

[ ] PR includes a summary of changes, including screenshots of any UI changes.
[ ] PR adds unit tests, updates existing unit tests, OR documents why no test additions or modifications are needed.
[ ] In case of new functionality, my PR adds documentation that describes how to use it.

All the public python functions and the classes in the PR contain docstrings that explain what it does

[ ] PR passes all tests documented in the developer guide

Codecov Report

Merging #315 into master will increase coverage by 4.89%.
The diff coverage is n/a.

@@            Coverage Diff             @@
##           master     #315      +/-   ##
==========================================
+ Coverage    71.6%   76.49%   +4.89%
==========================================
  Files         143       26     -117
  Lines        3701     1187    -2514
  Branches      462      109     -353
==========================================
- Hits         2650      908    -1742
+ Misses       1018      250     -768
+ Partials       33       29       -4

Impacted Files
Coverage Δ

...ication/static/js/components/common/Tabs/index.tsx

...ndsen_application/static/js/ducks/search/api/v0.ts

...ion/static/js/components/PreferencesPage/index.tsx

...mponents/PreferencesPage/PreferenceGroup/index.tsx

...ication/static/js/components/common/Flag/index.tsx

...sen_application/static/js/ducks/allTags/reducer.ts

...tatic/js/components/common/ScrollTracker/index.tsx

...pplication/static/js/ducks/popularTables/api/v0.ts

...static/js/components/common/ResourceList/index.tsx

...tic/js/components/common/ResourceList/constants.ts

... and 93 more

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@danwom just made the branch for v.1.1.0 so we can make a new release from that one -- allowing us to start putting design changes into master. Will use this PR for review.

@ttannis should be ready for final review.


Allow for GT fields with phased or unphased ploidy separators

@mlathara, FYI - what will be displayed will be a string form for GT, previously it was a list of integers.


Add a tutorial on loading data from JSON

This is a common need, to know how to store data in JSON and use it in templates.
Secondarily, to get JSON remotely (say from an API) and use it in templates. The kit includes sync-request to do this.

closing and see if it comes up in research


Add more wrappers for opengl functions

Currently does there only exist a wrapper for a small subset of the available functions for OpenGL and WebGL.
Wrappers are needed for:

[ ] Textures
[ ] Blending
[ ] Constants
[ ] Other potential functions

Defered for now

@TheAzack9 has made progress on adding wrappers around rendering primitives.


Typo in ad service account cmdlets

Changed InInstall-ADServiceAccount to Uninstall-ADServiceAccount

@jayhaddad : Thanks for your contribution! The author(s) have been notified to review your proposed change.

#label:"aq-pr-triaged"

#sign-off

thanks, @jayhaddad !


Business metrics

As an outside observer trying to learn more about SimpleReport, I need up to date metrics on the numbers of tests, active facilities and so forth, so that I can tell if this product is succeeding.
Implementation Possibilities:

REST-ish resource controllers
actuator endpoints with HAL navigation
??

Likely questions to ask:

how many tests have been run total? Last 24 hours? Yesterday?Last 7 days? Last month?
how many facilities have been active total, etc?

solved with metabase


How to configure formatting

How can I configure the behaviour of vim.lsp.buf.format? Specifically I'd like to default to add an empty trailing newline at the end of each file, the current default seems to be to make sure the last line of the file isn't empty.

You can pass arguments using a lua table. You can find the list of arguments in Neovim's documentation, see :help vim.lsp.buf.format()
For your specific case you can try this.
vim.lsp.buf.format({
  formatting_options = {
    insertFinalNewline = true,
  }
})

Is worth mention this really depends on your language server. If it doesn't work then probably your LSP doesn't support that configuration.
If you want to force a new line, you can try using a blocking request to the server, then use lua to insert the line after the LSP is done.
-- Blocking formatting request
vim.lsp.buf.format({
  async = false,
  timeout_ms = 10000,
})

-- append new line
vim.fn.append(vim.fn.line("$"), '')


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Add makeUp func to client.Option for clean code

What this PR does / why we need it: Clean code
Which issue(s) this PR fixes:

Fixes #
Special notes for your reviewer:
Does this PR introduce a user-facing change?:

Additional documentation:

Checklist:

[x]  Added relevant tests or not required

[x] Didn't break anything

My system is not using port 2380. But I have the following problem. So I don't know that etcd relate tests pass.(passed other tests).

➜  yorkie git:(master) docker-compose -f docker/docker-compose-ci.yml up etcd --build -d
[+] Running 0/1
 ⠋ Container etcd  Starting                                                                                0.1s
Error response from daemon: driver failed programming external connectivity on endpoint etcd (9ef0938cf020b663d862ef8d22d23726be988216c1f7776529024eb55671e4b1): listen tcp4 <IP_ADDRESS>:2380: bind: address already in use
➜  yorkie git:(master) sudo lsof -PiTCP -sTCP:LISTEN | awk '{print $9}'
NAME
*:22
*:22
*:22
*:22
[fe80:4::aede:48ff:fe00:1122]:49154
[fe80:4::aede:48ff:fe00:1122]:49155
[fe80:4::aede:48ff:fe00:1122]:49156
[fe80:4::aede:48ff:fe00:1122]:49157
[fe80:4::aede:48ff:fe00:1122]:49158
*:29919
localhost:631
localhost:631
localhost:6443
*:27017
localhost:3350
localhost:57228
localhost:58863

@loganstone Hello Logan. Good to see you again.
The intent of this work seems to be to keep the options cleaner. I wish we could proceed with the direction of the client option cleanup work rather than a simple cleaning code.
It might be a good idea to look into how other libraries do it. I think etcd's way is fine too.
https://github.com/etcd-io/etcd/blob/69279532f46cb7491bb60a588be534100fdc058d/client/v3/client.go#L85-L118
What do you think?

@loganstone Hello Logan. Good to see you again.
The intent of this work seems to be to keep the options cleaner. I wish we could proceed with the direction of the client option cleanup work rather than a simple cleaning code.
It might be a good idea to look into how other libraries do it. I think etcd's way is fine too.
https://github.com/etcd-io/etcd/blob/69279532f46cb7491bb60a588be534100fdc058d/client/v3/client.go#L85-L118
What do you think?

I misunderstood that you were using variadic function trick.
Now I understand your intentions.


Fix SSL tests scripts with recent OpenSSL server with Diffie-Hellman

compat.sh and ssl-opt.sh don't work with a modern OpenSSL (e.g. the system one on Ubuntu 20.04) because they force the use of a small DH prime. We do this for compatibility with ancient versions of OpenSSL which we still use for legacy features. Only do it for those ancient versions, not for modern versions. This way you can run the SSL test scripts with the system version of OpenSSL on a modern system (barring other issues which I haven't checked).
This may turn out not to be needed in 3.0 if we remove everything that requires legacy OpenSSL for interoperability testing. If so we can either revert the patch or keep it for similarity with LTS branches.
Backports:

2.x: https://github.com/ARMmbed/mbedtls/pull/4289
2.16: https://github.com/ARMmbed/mbedtls/pull/4326

Is your “without this patch” the merge-base of this branch and development, or a different vintage of development?

That was comparing this branch, vs. this branch with "git revert HEAD" applied, so should be like-for-like.

The travis error seems unrelated (failure to install some python requirements) and can be ignored since Jenkins passed.

The Travis error is unrelated (failure to install a python requirement on windows) and can be ignored since Jenkins passed. Still labelling "needs: ci" while we're waiting for the pr-merge job on Jenkins to complete.

Actually the pr-merge job on Jenkins passed but just failed to report, so this is all good.


Add lock to init_datastoreCache

init_datastoreCache and a couple of it's callers use global 'datastores' with no lock, which may cause race. This PR is to add lock to init_datastoreCache() to avoid the race.

LgTM

@govint Could you explain more what is the test you suggested? I am not quite clear.

Ok, modify init_datastoreCache() to always do a forced init - meaning datastores will be set to None and re-initialized on each call and run a workload of test-esx on say >=2 VMs on the host. Verify no exceptions/errors in the ESX service.

To add to the discussion above. The main gap is that datastores flip to new value is not transactional for anyone not acquiring the lock. It is easily healed by accumulating the.liat in a local object (local var) and flipping in one op at the end of init (datastores = tmp_datastores). This way one a traversal of stores started in a thread, init in another thread will not impact the object in use. Forcing init defeats the purpose of cache though it's good for testing.
If there is further discussion, let's take it to telegram

@msterin @govint I have addressed your comments and run the test that Govindan suggested(no exception is found in ESX). Please review it.


Update request: SWT 4.5→ SWT-4.26

Package name: swt
Latest released version: 4.26

Current version on the unstable channel: 4.5
Current version on the stable/release channel: 4.5

[x] Checked the nixpkgs pull requests

Notify maintainers

Note for maintainers: Please tag this issue in your PR.
The swt package (which dates from 2015) no longer works with Gtk2 shipped in NixPkgs as it 21 point releases behind being current:
***WARNING: GTK+ version too new (major mismatch)
***WARNING: SWT requires GTK 2.18.0
***WARNING: Detected: 2.24.33

cc @bb010g (maintainer of that package)

I hit this issue while trying to build  https://github.com/BiglySoftware/BiglyBT, so I took a crack at updating to the latest SWT version here.
This seems to work for my purposes (in that BiglyBT builds and the entire UI appears functional), however there is a a conflict with trying to merge that back in. A couple of packages are relying on a jdk8 build of SWT, however SWT has not supported jdk8 for a couple years; attempting to use jdk8 causes the build to fail:
     [exec] Building GTK3 bindings:
     [exec] gcc  -DSWT_VERSION=4958r2    -DLINUX -DGTK -I/nix/store/4agh2lx6ggpp5g3zl9a2w5a0h0a78nwy-openjdk-8u352-ga/lib/openjdk/include -I/nix/store/4agh2lx6ggpp5g3zl9>
     [exec] *** GTK3 Build failed, aborting further actions..
     [exec] swt.c: In function 'JNI_OnLoad':
     [exec] swt.c:24:16: error: 'JNI_VERSION_10' undeclared (first use in this function); did you mean 'JNI_VERSION_1_6'?
     [exec]    24 |         return JNI_VERSION_10;
     [exec]       |                ^~~~~~~~~~~~~~
     [exec]       |                JNI_VERSION_1_6
     [exec] swt.c:24:16: note: each undeclared identifier is reported only once for each function it appears in
     [exec] make: *** [make_linux.mak:142: swt.o] Error 1

The packages relying on swt_jdk8 appear to be:

tuxguitar
areca

I'll try to take a look at them to see if they can use newer jdk/SWT without much hassle.

@daniel-sampliner AFAIK BiglyBt relies on it's own jars of swt which are in their git repo for each architecture they support. It's from their ReadMe
Please correct me if i'm wrong, BiglyBt package is one i definitely want in NixOs

This issue is bothering me too, more details in https://github.com/NixOS/nixpkgs/issues/340048#issuecomment-2395558967 .

While trying to investigate why we don't update swt, I all of a sudden found this old attempt:

https://github.com/NixOS/nixpkgs/pull/75609

I managed to update it also today, and it wasn't so hard. However, I wasn't sure if the swt.jar file and the shared objects are actually functional after the update, so I wanted to see if a the dependent packages work with the new swt. Apparently, some packages that seem to depend on swt don't really use or need it, and some other packages don't actually launch with or without the update, so I marked them as broken, and even tuxguitar is now updated and works with the new swt :tada:. All changes are available here:

https://github.com/NixOS/nixpkgs/pull/347022


[Design] Add [HtmlAttributeName(..., DictionaryAttributePrefix="prefix")]

#89
support adding attributes (that aren't otherwise bound) to a tag helper dictionary
add ObjectCreationExpression, Prefix and more to TagHelperAttributeDescriptor
determine string-ness when creating TagHelperAttributeDescriptors
handle Prefix matches in TagHelperBlockRewriter
refactor CSharpTagHelperCodeRenderer to handle new indexer property assignments
calculate string-ness for a bound attribute using all TagHelperAttributeDescriptors
extend handling of invalid attribute names to include [HtmlAttributeName]

known issue: ObjectCreationExpression is a C# expression because otherwise
generation has insufficient information to determine correct constructor.

no VB or F# (for example) workarounds today

no new tests yet
nits:

add TagHelperHelper
separate TagHelperAttributeDescriptorComparer and TypeBasedTagHelperDescriptorComparer
encourages reuse and most are already used in multiple classes
use <inheritdoc/> in TagHelperDescriptorComparer
also give it an explicit constructor

/cc @pranavkm @NTaylorMullen

Closing [Design].  Let's do the real bits in pieces.

:+1: thanks @dougbu that'll be fantastic. Hard to consume it all as it is


Create Linux recipe for Cassandra

Documentation: https://docs.newrelic.com/docs/integrations/host-integrations/host-integrations-list/cassandra-monitoring-integration.
Necessary Configuration :

Username: (Prompt user to enter)
Password: (Prompt user to enter)
Hostname: (Allow user to confirm default of localhost, or enter new value)
Port: (Allow user to confirm default port of 9042, or enter new value).

Note: The default cassandra port is different in our docs and base config file, and this is not the default port that Cassandra runs with. We should use the 9042 default in our version of the config file.
For Logging: Ensure that common log file directories/patterns & process name for Cassandra is populated in the beginning of the recipe.
Aha! Link: https://newrelic.aha.io/features/VIRTUOSO-47

@sheilakrishnan and @rishimukhop1 I believe this work is completed, and this issue should be closed.

@sheilakrishnan and @rishimukhop1 I believe this work is completed, and this issue should be closed.


Can't output two different files from same tree

I'm trying to compile two different JS files from my scripts tree. A logical way to go about this seemed to be to run this filter on my tree twice, with two different configurations. However, doing so produces some really strange errors that, to me, resembles a race condition or something.
Here's the relevant part of my Brocfile
var rjsOptions = {
    baseUrl: "scripts",
    inlineText: true,
    optimize: "uglify",
    stubModules: ["text"],
    paths: {
        knockout: "empty:"
    }
};

var fooScript = requireJs(scripts, {
    requirejs: _.extend(rjsOptions, {
        include: ["foo"],
        insertRequire: ["main"],
        mainConfigFile: "scripts/main.js",
        name: "main",
        out: "scripts/main.js"
    })
});

var barScript = requireJs(scripts, {
    requirejs: _.extend(rjsOptions, {
        insertRequire: ["bar"],
        mainConfigFile: "scripts/main.js",
        name: "bar",
        out: "scripts/bar.js"
    })
});

module.exports = mergeTrees([
    fooScript,
    barScript
]);

And here's the error output I'm getting (the error is being thrown when merging the trees):
$ broccoli build build
Error: Merge error: file scripts/bar.js exists in /home/fredrik/app/tmp/require_js_filter-tmp_dest_dir-yMHQNi3F.tmp and /home/fredrik/app/tmp/require_js_filter-tmp_dest_dir-C8Wv970J.tmp
Pass option { overwrite: true } to mergeTrees in order to have the latter file win.
  at mergeRelativePath (/home/fredrik/app/node_modules/broccoli-merge-trees/index.js:98:21)
  at mergeRelativePath (/home/fredrik/app/node_modules/broccoli-merge-trees/index.js:122:17)
  at /home/fredrik/app/node_modules/broccoli-merge-trees/index.js:23:5
  at $$$internal$$tryCatch (/home/fredrik/app/node_modules/broccoli-merge-trees/node_modules/promise-map-series/node_modules/rsvp/dist/rsvp.js:490:16)
  at $$$internal$$invokeCallback (/home/fredrik/app/node_modules/broccoli-merge-trees/node_modules/promise-map-series/node_modules/rsvp/dist/rsvp.js:502:17)
  at $$$internal$$publish (/home/fredrik/app/node_modules/broccoli-merge-trees/node_modules/promise-map-series/node_modules/rsvp/dist/rsvp.js:473:11)
  at Object.$$rsvp$asap$$flush [as _onImmediate] (/home/fredrik/app/node_modules/broccoli-merge-trees/node_modules/promise-map-series/node_modules/rsvp/dist/rsvp.js:1581:9)
  at processImmediate [as _immediateCallback] (timers.js:345:15)

Build failed

If I do pass {overwrite: true} to the mergeTrees call, I get the output of the first requireJs call (ie. the scripts/main.js), but with the filename bar.js.
I asked a question on StackOverflow about this as well, but this seems to be more of a bug than a misunderstanding on my part, so I thought I'd report it.

Hi!
Did you try to export only fooScript? I think the filter run the optimizer tool but also keep the sources. Can you please confirm this?

I tested again, and it seems that even if I module.exports only the first or the second tree, I get only one file - scripts/bar.js with the content of scripts/main.js.

That's weird, it looks like calling the filter twice has a side effect. We are currently working on a new version with updated packages. I think I'll publish a version this weekend. Will you be available to try it again?

I know, super weird... I'm doing some debugging and it looks like the requirejs.optimize call in both callbacks is receiving the exact same result (content of the first call, options.out file name from the 2nd call), so it's definitely a problem with requirejs. For some reason requirejs.optimize, if called twice, overrides parts of the first config with the 2nd config.
And yeah, I can absolutely do more testing. If it's the #9 PR, I can go ahead and try it beforehand to let you know if that helps as well?

It would help a lot :+1:

Getting the same result even with the patch :/

I'll keep in my your problem and tell you asap when it's fixed

I found the problem! So silly...
It's my call to _.extend that overwrites the config object. If I simply change the order of the arguments I pass to _.extend, it works as expected.
var fooScript = requireJs(scripts, {
    requirejs: _.extend({
        include: ["foo"],
        insertRequire: ["main"],
        mainConfigFile: "scripts/main.js",
        name: "main",
        out: "scripts/main.js"
    }, rjsOptions)
});

Sorry, I should have noticed it earlier! _.extend mutates the first parameter. When you want another reference do like this _.extend({}, rjsOptions, {...})

Yeah, I should've caught that one... Anyway, thanks for taking the time to respond :)


migrations: Fix performance of migration 0436.

This was doing bulk_create in a loop for each realm, which is too slow on very large servers. Just do a single bulk_create with a reasonable batch_size at the end.
From my understanding this is already applied on Zulip Cloud, but might still be worth tweaking the code anyway, if only to  not leave a bad example of doing things inefficiently that someone might copy when writing a future migrations.
https://github.com/zulip/zulip/pull/25146#discussion_r1183171485

Merged, thanks @mateuszmandera!


Enter key on hardware android keyboard post message

Summary
Enter key on hardware android keyboard now post message
Ticket Link
This PR fixes  #8797
Device Information
This PR was tested on: [One Plus 3, Android 8.0]
@enahum Please review :)

Thanks @saketkumar95 for the pull request!
Per the CONTRIBUTING.md file displayed when you created this pull request, we need to add you to the list of approved contributors for the Mattermost project.
Please help complete the Mattermost contribution license agreement?
This is a standard procedure for many open source projects. Your form should be processed within 24 hours and reviewers for your pull request will be able to proceed.
Please let us know if you have any questions.
We are very happy to have you join our growing community! If you're not yet a member, please consider joining our Contributors community channel to meet other contributors and discuss new opportunities with the core team.

Hi @saketkumar95 thanks for this PR but the idea behind that ticket is that if you don't have an external keyboard connected it works the way it does now, but if an external keyboard is present pressing the enter key should send the message instead of creating a new line and the new line can be done by pressing shift+enter or it should follow the users preferences the way it is done in the webapp

Hi @saketkumar95 Do you still plan to work on this?

Hey @saketkumar95 - thanks for helping and taking the time to contribute this PR!
As noted by @enahum above, some of the functionality is missing, so I'll close the PR for now. I'll leave the branch open if you'd like to continue on it later.
If you have any questions, don't hesitate to let us know. We also have other introductory React tickets listed here if you're interested tackling any of those.
Thanks again! Hope to see you around :)


Move values closer to where they'll be needed

Hey :wave:
Thank you for this package, it sounds pretty useful and very well done.
This change makes it so that values are declared in the branches where they'll be used, and not prematurely where they might end up being unused.
I found this by running the elm-review rule NoPrematureLetComputation
If you haven't come across elm-review, it's a linter for Elm. I would highly recommend you try it out. A good starter set for this package would be to run the following commands:
npm install elm-review
elm-review init --template jfmengels/elm-review-config/package

(I might make a few other pull requests / issues, please let me know if my suggestions are bothering you. If not, I hope they'll be useful)
Have a lovely day :wave:

Thank you for checking and commenting! I have never touched your elm-review yet, but that sounds great for me!
The edited code looks good, so I will check it locally for benchmarking in a few days. ( seems to have no change to performance because the process never goes to the other branch.)

The edited code works well! Thank you. (Any other pull requests or issues are also welcome!)


fade effect for 0.3

Cant find how.

of course, i think, i can do this with jcarouselanimate/jcarouselanimateend (fadeIn before animation and fadeOut after) but would be perfect to fade without scrolling.

cycle doesnt have nice carousel. dont want load 2 different scripts only for fade. will do fade effect by myself. thx for answer.


fix bar beta-eta clash and alg comments

fixes #13 and #14

This looks good to me, thanks @miguelbiron .
@jhuggins @jnegrea can I just get one more set of eyes on this before merging? If you're happy with it you can go ahead and merge.

To your question, I found an old copy of tchdr.sty in Gary's repos. The old convention was \boldeta. Not sure what you think of it but to me it suggests a bold eta.
https://github.com/garyzhubc/tchdr/blob/7d7df3726f2a422eb52d8bfbc5690e5f39cdda36/tchdr.sty#L542

I'd be OK with two solutions:

for \eta, just implement the two explicitly: \bareta and \boldeta
some joint command like \bobeta to do \boldorbar

Ok I went with the first option because it seemed like the least disruptive.

The fix looks fine, and I like having both bold and bar defined separately. I was thinking for a moment that we should have a version that is \boldorbareta so that it changes to whatever convention \boldorbar is set to, but then I rethought that since it is maybe redundant as it doesn't save many characters relative to \boldorbar{\eta}

Thanks @miguelbiron . Merged!


Add min and max aliases

Fixes #9

Less code to drop the longer form, but maybe better UX to keep both


add yaml provider

Contributing a YAML provider to allow import and export of topics via .yaml files.

Hi @teharris1 thanks a bunch for your PR, this is awesome I was just thinking today that I needed a yaml importer (and oddly, had not seen your PR until today). I'll take a more careful look at the code in the next couple of weeks, but there are some API changes I would like before I can approve this. Should we discuss?


提供一个关闭自动验证漏洞的开关

Preflight Checklist

[X] I agree to follow the Code of Conduct that this project adheres to.
[X] I have searched the issue tracker for an issue that matches the one I want to file, without success.

Problem Description
目前洞态在发现漏洞后会自动用一些无害的payload对漏洞进行主动验证，虽然是无害的payload，但是在复杂的业务场景下可能也会对QA的测试结果造成影响
Proposed Solution
希望提供一个按项目 开关 自动验证漏洞的按钮
Alternatives Considered
No response
Additional Information
No response

https://github.com/HXSecurity/DongTai-webapi/issues/259

@exexute

fixed in v1.1.3


Bug Title
Responsiveness Issue
Description of BUG
Social icons are hiding in responsiveness.
Kindly assign me this issue.
Reporduction URL of bug
No response
Bug Issue Type
Frontend
Screenshot of the issue if in UI/ Document

Are you taking this issue fix
yes

Sure @RoshaanAli , please go agead, and also star the repo to make it cooler

@RoshaanAli  Check bug issue #255 , it is made just like that to hide in mobile and tab screen sizes...

@jsvigneshkanna If social icon is to be shown at <900px view port than assign it to me, as i know where to make changes and fixed issue... has work on it Bug #255

Issue Fixed....

@AmanJain18 , here the github and linkedin icons should only be visible in view port < 900px and have some horizontal padding between 2 icons

@AmanJain18 , here the github and linkedin icons should only be visible in view port < 900px and have some horizontal padding between 2 icons

When the screen size is less the <900px only github and linkedin icon should be visible and rest icon should be hidden right ?


FixAfter Approve, even comments should be considered as mergeable

Proposed changes
Issue Number: close #xxx

Further comments
If this is a relatively large or complex change, kick off the discussion at<EMAIL_ADDRESS>by explaining why you chose the solution you did and what alternatives you considered, etc...

run buildall

TEST


Reimplement hash library using non-licensed, MIT or BSD code

A good catch by @galexite shows that the sources from which the hash functions in the hash module derived  seems to use unattributed code from the PHP source.
This all needs to be purged and replaced with new implementations or MIT/BSD licensed code.

A good alternative is https://www.libtom.net/LibTomCrypt/

Thanks. I love it's license and I'll look through it for inspiration on replacing the current implementation. I'll also use this medium to solicit help from any body interested in helping with the transition of the hash methods. PRs for this issue are very welcomed.

As far as I know PHP License is not restrictive and is compatible with MIT

https://www.php.net/license/3_01.txt
https://www.php.net/license/index.php

It would be needed to include the license comment in the affected source code and put

"This product includes PHP software, freely available from
http://www.php.net/software/".

somewhere

I think it's counter productive for Blade MIT license. For those who plan on embedding Blade, this would mean they'll have to advertise The use of PHP in their product as well.
I believe the Unlicense and BSD license are very much more permissive and offer better interoperability with the MIT license.


[MRG, BUG] Fix appending Annotations with channel names

Reference issue
Fixes: #9779
What does this implement/fix?
Explain your changes.
Additional information
Any additional information you think is important.

thx @adam2392 !


Questions about TDC

I'm having trouble with the TDC module,the paper explains that it uses the TDC module proposed by AUTOHR.However, I don't think CDC_T code implements the formula for TDC in AUTOHR.Why use out_normal-06*out_diff, and out_diff is to first calculate the combination of the weights of the t0 fragment and the t2 fragment in the convolution kernel, and then use the 3D convolution to volume, I am very confused, can you explain,thank you

self.conv.weight[:, :, 0, :, :].sum(2).sum(2) + self.conv.weight[:, :, 2, :, :].sum(2).sum(2) is a processing of the convolution kernel. Since the size of the convolution kernel is 3x3x3 and the channel is IODHW, where the third channel is Depth which means time. Two sum (2) means accumulating on H and W channels, which equals to taking average in a 3x3 space. Then add up averages from first frame and third frame for each step of convolution operation, then calculate difference with second frame.
According to the original text, the output at the current spatio-temporal position in the feature map (that is, the central position of the convolution kernel) should subtract from adjacent outputs (i.e., previous and next frames). I think this writing should be consistent with the original text.
I reproduced this model and made a fair comparison with other models on public datasets. If interested, check out https://github.com/KegangWangCCNU/PhysBench

Thank you very much for your reply.


Adding another profile

Accepting and added Frank

OPS Build status updates of commit 421e73b:
:clock10: Preparing: average preparing time is 40 sec(s)

OPS Build status updates of commit 421e73b:
:clock10: Incremental building: average incremental building time is 36 sec(s)

OPS Build status updates of commit 421e73b:
:white_check_mark: Validation status: passed

File
Status
Preview URL
Details

advocates/frank-boucher.yml
:white_check_mark:Succeeded
View

advocates/index.html.yml
:white_check_mark:Succeeded
View

advocates/map.yml
:white_check_mark:Succeeded
View

advocates/media/profiles/frank-boucher.png
:white_check_mark:Succeeded
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advocates/toc.yml
:white_check_mark:Succeeded
View

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


Capture methodology and possible biases per survey / site visit

Related
#45
Requirement
The solution shall record methodology and aberrations from it on a survey (site visit) level to allow analysts to determine possible biases of data collection.
Use cases

Surveying hatchling fan angles under time pressure means that possibly only nests with fan angles are recorded. The survey metadata needs to note whether there was saturation sampling or a subset, or a biased subset.
Survey X aims to follow well-documented methodology Y, but encounters problem Z and therefore changes steps A of methodology to B. The observer has a way to record these changes as structured (choice fields with defined options in drop-down menus) as possible and as unstructured (comments, photos) as necessary.

Link to peer reviewed methodology, known / planned deviations, and unplanned / on the day deviations.


New Relic Logging API

Feature Description
Add the ability to send to the NR Logging API in addition to the Insights API.
Describe Alternatives
A separate Log client.

Resolved by #438


benchmark diva alone

git clone https://github.com/marrlab/DomainLab.git   domainlab_diva
cd domainlab_diva
git checkout fbopt
./run_benchmark_slurm examples/benchmark/pacs_diva_fbopt_alone_es5.yaml 10

Running successfully.

rst.txt

Results before bug in yaml:
rst_bug.txt

'str_diva_multiplier_type': 'gammad_recon'
we probably should not run this option at all.

Should we be concerned about this? This has never happened so far to my knowledge.

could you vim one of them and send a screen shot where the error message occur?

error_log.txt

tks, it is my prlbem, i printed the name "error",  will change the code so that "error" would not occur in loggin

so code is fine

@DanScarc , fixed in latest commit
https://github.com/marrlab/DomainLab/pull/462/commits/1d6345be904f3eed2b3d0afa4a2d1d8c56beba00

Should I rerun again, then?

no

post some results tomorrow morning here

Perfect, as suspected. I'll post results tomorrow!

rst.txt
Here you go.

so most jobs get stopped at iteration 8, i will fix this issue so we could run them long enough
https://github.com/marrlab/DomainLab/issues/572

fixed, could you pull and rerun the same yaml file?
you could delete all historical folders already.

wait, let me do a last edit to yaml file

Running since 10'.

Ah ok, then killing jobs now. You tell me when you're sure.

done, please run now

snake make should use 3 cores at the moment.

https://github.com/marrlab/DomainLab/pull/462/commits/4007dc8b52005d9773265d216d2bd27443373147,
use  at least this commit

do we have some intermediate results?

Yes.
rst.txt

Updated results below
rst.txt


fix: set HTTPS for all OpenAI URLs

This seems to have come from https://github.com/theodo-group/LLPhant/pull/233
When we use the latest version to embed a document, we get this error from the OpenAI API:
{
    "error": {
      "type": "invalid_request_error",
      "code": "http_unsupported",
      "message": "The OpenAI API is only accessible over HTTPS. Ensure the URL starts with 'https://' and not 'http://'.",
      "param": null
    }
  }

It looks like someone else tried to fix it in this commit but there were other cases to also update.
https://github.com/theodo-group/LLPhant/commit/a423f2f5c78e8cfeb3468b1a0292117bb15c8672#diff-3f621a2c1806f726b861dcf2eeaa1f96e4a5e5d929f15041d082107e8d87f520R44

Thanks @jshah4517 !