Ability to configure access to the context from the entity interface

The generated entity interface implements the context interface to provide access to GraphQL query building functionality. For example the entity interface Film generated by tutorial schema looks like following:
public interface Film : CinemaContext {
  public val id: Long
  public val title: String
  public val actors: List<Actor>

  public override suspend fun query(__projection: QueryProjection.() -> Unit): Query
  public override suspend fun mutation(__projection: MutationProjection.() -> Unit): Mutation
  public override fun subscription(__projection: SubscriptionProjection.() -> Unit):
    CinemaSubscriber<Subscription>
}

As you can see, the entity interface Film extends the context interface CinemaContext, which makes the entity an entry point for executing GraphQL queries. We can use Kotlin extension functions to create a smart API that looks like rich domain model.
But the Kotlin extension functions defined in the context interface scope are available in the entity interface scope because the entity interface extends the context interface. Thus, context inheritance often leads to confusion in the customized API. To avoid such problems, we need to abandon context inheritance in the entity interface in favor of an accessor for the context.
Our entity interface might look like this:
public interface Film {
  public val id: Long
  public val title: String
  public val actors: List<Actor>

  public fun __context(): CinemaContext
}

But such a change would break the backward compatibility of the entity interfaces. Let's add the following generation settings to maintain backward compatibility:
plugins {
    kotlin("jvm")
    id("io.github.ermadmi78.kobby")
}

kobby {
    kotlin {
        entity {
            // Inherit context interface in entity interface
            contextInheritanceEnabled = true // true by default

            // Generate context access function
            contextFunEnabled = false // false by default

            // Context access function name
            contextFunName = "__context"
        }
    }
}

Available since version 1.4.0
To switch from context inheritance to context function:
Gradle
plugins {
    id("io.github.ermadmi78.kobby") version "1.4.0"
}

kobby {
    kotlin {
        entity {
            contextInheritanceEnabled = false
            contextFunEnabled = true
        }
    }
}

Maven
    <build>
        <plugins>
            <plugin>
                <groupId>io.github.ermadmi78</groupId>
                <artifactId>kobby-maven-plugin</artifactId>
                <version>1.4.0</version>
                <executions>
                    <execution>
                        <phase>generate-sources</phase>
                        <goals>
                            <goal>generate-kotlin</goal>
                        </goals>
                        <configuration>
                            <kotlin>
                                <entity>
                                    <contextInheritanceEnabled>false</contextInheritanceEnabled>
                                    <contextFunEnabled>true</contextFunEnabled>
                                </entity>
                            </kotlin>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>


some fixes

updated strings to f-strings
removed redundant use of f-string
replace type() with isinstance()
replaced x == [] with not x

Codecov Report

Merging #553 (7a9ee3e) into main (435228d) will decrease coverage by 0.05%.
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@@            Coverage Diff             @@
##             main     #553      +/-   ##
==========================================
- Coverage   94.15%   94.10%   -0.05%
==========================================
  Files          95       95
  Lines       12795    12844      +49
  Branches       24       24
==========================================
+ Hits        12047    12087      +40
- Misses        742      751       +9
  Partials        6        6

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Most of these are either manifestly wrong, or arguable and  styalistic.
I assume these changes were generated using some code tool - I suggest if you're creating more PRs like this, it would  be good to note what tool you're using.


Add CRC functionality from ESP ROM

[x] The code compiles without errors or warnings. Pre-existing warnings from unrelated code
[x] All examples work.
[x] cargo fmt was run.
[x] Your changes were added to the CHANGELOG.md in the proper section.
[x] You updated existing examples or added examples (if applicable).
[ ] Added examples are checked in CI (awaiting approval)

Nice to have

[x] You add a description of your work to this PR.
[x] You added proper docs for your newly added features and code.

ESP ROM includes hard-coded, referentially transparent CRC functions via simple table lookups. Kilobytes of program space can be saved using them. A compile-time #[cfg] attribute is internally used to automatically link against the ROM function or to a fallback implementation if not available.
Currently, all chips implement all functions except ESP32-S2 which omits the *_be() variants. On this chip those functions will be compiled instead.
I tested the example code on a real ESP32-C3, and simulated the ESP32-S2 on Wokwi to test the fallback implementation. All examples compile.

Fixed merge conflict in CHANGELOG.md


Paper QA Settings for AWS Bedrock

Hi, do you have any guidance or examples for the Settings parameter in order to use paper QA with entirely AWS Bedrock models. I'm able to access by Bedrock's LLM and embedding models using boto3 and litellm, but I can't figure out how to make it work in Paper QA Python API, and my errors are very generic and non-specific.

Hi @istranic can you post your stack trace(s)?
Also, check these litellm docs on how to use Bedrock, we are just passing through to them: https://docs.litellm.ai/docs/providers/bedrock

Hi @jamesbraza I made progress and solved some of the issues. I can now manually add and query docs with all models hosted locally.
litellm_embedding_model = LiteLLMEmbeddingModel(
    name="openai/sentence-transformers/all-mpnet-base-v2",
    config=embedding_config,

## Add Docs
  await docs.aadd(doc,
                  settings=settings,
                  citation=doc,
                  docname=doc,
                  embedding_model=litellm_embedding_model)

## Query
answer = await docs.aquery(
    "What is the Nonlinearity Error and why does it matter?",
    settings=settings,
    embedding_model=litellm_embedding_model
)

However, when I add docs using the method above, it's not doing any of the advanced parsing, correct? In order to benefit from advanced parsing, I need to use something like:
answer = await agent_query(
    QueryRequest(
        query="What manufacturing challenges are unique to bispecific antibodies?",
        settings=settings,
        embedding=litellm_embedding_model
    )

However, this doesn't work because embedding, embedding_model, etc. are not recognized arguments. If I try to specify the embedding model in the settings, it doesn't work either, and I get errors related to missing Open AI keys. What's the correct approach for specifying the litellm_embedding_model in the agent_query API?

it's not doing any of the advanced parsing

So I can understand, what do you mean by "advanced parsing"?

However, this doesn't work because embedding, embedding_model, etc. are not recognized arguments

Yeah when using agent_query you need to specify a QueryRequest which contains settings: Settings, kind of like we do here: https://github.com/Future-House/paper-qa#ask-manually
And for setting the embedding_model in Settings, start by reading here: https://github.com/Future-House/paper-qa#changing-embedding-model
So you won't be directly instantiating a LiteLLMEmbeddingModel, it will be instantiated for you by the inner workings of paper-qa given the Settings. Does that make sense?

An issue I found in this line is that the LiteLLMEmbeddingModel class is only passing config["kwargs"] to litellm API, and it's ignoring other parameter in the config dict. Maybe this is the root-cause of the issues.
When I specify the embedding settings using the LiteLLMEmbeddingModel object instead of Settings, I hack by dumping the config arguments into the kwargs key, but I doubt this is how the API is intended, and I'm not sure that upstream code is going that either, without which you'll get errors.
embedding_config = dict(
        kwargs = dict(
        api_base="abc",
        api_key="abc",
        num_retries=3,
        timeout=120
        )
)

litellm_embedding_model = LiteLLMEmbeddingModel(
    name="openai/sentence-transformers/all-mpnet-base-v2",
    config=embedding_config,

)

Hi @jamesbraza just wondering if you've had a chance to consider the potential bug above.

To rephrase my first question, is there any logical difference between adding documents manually and running docs.query, vs using the agent_query API above?

To answer this one:

Docs.query is a wrapper on Docs.aquery
agent_query kicks off an agent loop, of which the agent has a gen_answer tool. The gen_answer tool will also call Docs.aquery

So they ultimately invoke the same method.
In other words, you can either:

Populate a Docs object yourself using Docs.aadd and then call docs.query
Let the agent do this for you in agent_query

The paper_search tool ultimately calls Docs.aadd
The gen_answer tool calls Docs.aquery

I can try to update the README wording a bit to clarify this.

An issue in this line is that the LiteLLMEmbeddingModel class only passes config["kwargs"] to litellm API, and it's ignoring other parameters in the config dict. Maybe this is the root-cause of the issues.

As far as kwargs thing, I think you're using it correctly:

You set your embedding model's name into Settings.embedding and any special configuration into Settings.embedding_config
Eventually inside the code, an EmbeddingModel variant is instantiated. If the Settings.embedding begins with litellm-, then a LiteLLMEmbeddingModel is instantiated and sets the Settings.embedding_config to be the LiteLLMEmbeddingModel.config
Then the LiteLLMEmbeddingModel.config's kwargs are ultimately passed to litellm.aembedding as keyword arguments

So I actually think you're using it as intended. Your example:

embedding_config = dict(
        kwargs = dict(
            api_base="abc",
            api_key="abc",
            num_retries=3,
            timeout=120
        )
)

litellm_embedding_model = LiteLLMEmbeddingModel(
    name="openai/sentence-transformers/all-mpnet-base-v2",
    config=embedding_config,

)

You have bypassed the Settings. What you should do is something like:
Settings(embedding_config=embedding_config)

Does that clear things up a bit?

Thanks for the clarification @jamesbraza . I'm now able to run aadd and aquery with bedrock and local models just by updating the settings dictionary.
When I add data using aadd, it doesn't look like there's any advanced table parsing or summarization, is there? We run many queries on table data so it's important that the information in the tables be represented correctly, and I'm not getting good answers right now.
On another note, when I use agent_query instead of aadd+aquery, I get an error below related to tool choice. I tried to tweak the agent settings by setting tool_names to "auto" or None but couldn't get it to work. Do you have any suggestions?
litellm.exceptions.BadRequestError: litellm.BadRequestError: OpenAIException - ..........  Value error, tool_choicemust either be a named tool or "auto".tool_choice="none" is not supported.`

Hi @jamesbraza regarding our tables, they are within PDFs. Here's an example.
It seems like most RAG parsing tools (LlamaParse etc.) create summarize or tables, images, figures, etc. so that would be an awesome feature request for our use cases. I'll consider contributing in this regard as well.
Here's the code I'm running. It doesn't look like I'm tweaking the tool_choice, so I don't know how it ends up as None. Is there anything obviously wrong in my usage?
### LOCAL SETTINGS
local_llm_config = dict(
    model_list=[
        dict(
            model_name="llm",
            litellm_params=dict(
                model=model_local,
                api_base="",
                api_key="",
            ),
        )
    ]
)

local_summary_config = dict(
    model_list=[
        dict(
            model_name="summary",
            litellm_params=dict(
                model=model_local,
                api_base="",
                api_key="",
            ),
        )
    ]
)

embedding_config_local = dict(
        kwargs = dict(
            api_base="",
            api_key="",
            num_retries=3,
            timeout=120
        )
)

local_settings=Settings(
    llm=model_local,
    llm_config=local_llm_config,
    summary_llm= model_local,
    summary_llm_config=local_summary_config,
    embedding=model_embedding_local,
    embedding_config=embedding_config_local,
    agent=AgentSettings(
        agent_llm=model_local,
        agent_llm_config=local_llm_config,
    ),
    parsing=ParsingSettings(use_doc_details=False, chunk_size = 2000),
    paper_directory=source_folder,
)

### BEDROCK SETTINGS
bedrock_llm_config = dict(
    model_list=[
        dict(
            model_name='llm',
            litellm_params=dict(
                model = model,
                aws_access_key_id= os.environ["AWS_ACCESS_KEY_ID"],
                aws_secret_access_key= os.environ["AWS_SECRET_ACCESS_KEY"],
                aws_region_name= os.environ["AWS_REGION_NAME"],
            ),
        )
    ]
)

bedrock_embedding_config = dict(
    model_list=[
        dict(
            model_name='embedding_model',
            litellm_params=dict(
                model= model_embedding,
                aws_access_key_id= os.environ["AWS_ACCESS_KEY_ID"],
                aws_secret_access_key= os.environ["AWS_SECRET_ACCESS_KEY"],
                aws_region_name= os.environ["AWS_REGION_NAME"],
            ),
        )
    ]
)

bedrock_summary_config = dict(
    model_list=[
        dict(
            model_name='summary',
            litellm_params=dict(
                model = model,
                aws_access_key_id= os.environ["AWS_ACCESS_KEY_ID"],
                aws_secret_access_key= os.environ["AWS_SECRET_ACCESS_KEY"],
                aws_region_name= os.environ["AWS_REGION_NAME"],
            ),
        )
    ]
)

bedrock_settings=Settings(
    llm=model,
    llm_config=bedrock_llm_config,
    summary_llm= model,
    summary_llm_config=bedrock_summary_config,
    embedding=model_embedding,
    embedding_config=bedrock_embedding_config,
    agent=AgentSettings(
        agent_llm=model,
        agent_llm_config=bedrock_llm_config
    ),
    parsing=ParsingSettings(use_doc_details=False, chunk_size = 2000),
    paper_directory=source_folder,
)

## When I run the agent, I either specify the local_settings or bedrock_settings
answer = await agent_query(
    QueryRequest(
        query="Which device has the highest sensitivity?",
        settings=local_settings,
    )
)

It seems like most RAG parsing tools (LlamaParse etc.) create summarize or tables, images, figures, etc. so that would be an awesome feature request for our use cases. I'll consider contributing in this regard as well.

Yes sounds good

Is there anything obviously wrong in my usage?

No I don't see anything there why tool_choice would be "none". You'll need to use print debugging or an IDE debugger to root cause it.

An aside is consider upgrading to paper-qa>=5.6 as it has some fixes that increase reliability.
Also, consider using black or ruff and {} over dict() to make your code take less vertical whitespace, which makes it faster to read


allow etching runes which are below minimum for next block, but which are greater than or equal to the minimum for when the reveal transaction will be broadcast

Done.


Expose HTTP request from hooks

Please select if your request is either something new or an enhancement

[x] Enhancement of an existing Feature.
[ ] Request of a new feature.

Please select the area your request applies to. (Multiple selections are Possible. You can leave blank if you're not sure.)

[ ] Workspace - VSCode workspace, vaults, Intellisense/autocomplete, Dendron settings
[ ] Lookup - Dendron's Lookup Command
[ ] Views - Dendron Preview, Tree View, Side Panels in the UI
[ ] Schema - Dendron Schemas
[ ] Pod - Data import from / export to Dendron
[ ] Publish - External Site Publish
[ ] Markdown - Markdown features, such as syntax support and features

Is your feature request related to a problem? Please describe
If I want to use external data in a hook, I need to run an executable with execa. This makes the hook dependent on external files and is harder to maintain and share.
Describe the solution you'd like
I'd like it to be a popular, well documented library, to easily find what to do in all edge cases. Definitely needs to support both GET and POST, setting Content-Type, these kinds of things. This would be needed for JSON RPC, which I think makes a lot of sense for these integrations.
For the user interface, I would like this to be added to hook arguments in the template:
/**
 @params wsRoot: string, root of your current workspace
 @params note: Object with following properties https://github.com/dendronhq/dendron/blob/master/packages/common-all/src/types/foundation.ts#L66:L66
 @params NoteUtils: utilities for working with notes. [code](https://github.com/dendronhq/dendron/blob/master/packages/common-all/src/dnode.ts#L323:L323)
 @params execa: instance of [execa](https://github.com/sindresorhus/execa#execacommandcommand-options)
 @params fetch: function for HTTP requests with [Fetch API](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API/Using_Fetch)
 @params _: instance of [lodash](https://lodash.com/docs)
 */
module.exports = async function ({ wsRoot, note, NoteUtils, execa, fetch, _ }) {

Describe alternatives you've considered
I tried wget and curl, but on windows it's such a mess to set the options, that I gave up. Besides even if I did, the hook wouldn't work on my other machines with a different OS.
One could also write custom command line executables to get what they need using execa, but that requires considerably more work and a bit of skill.
Additional context
For me the immediate use for this feature would be improving integration with Zotero (academic reference manager) a lot. Let's say you create a note referencing a paper and it automagically fills with abstract and authors to find it easily later.

thanks for the feature request. we've been talking about this internally as well. going to add this as a backlog item as well as a crop candidate


Hi,
There is
pthread_mutex_init(&(thpool_p->thcount_lock), NULL);
pthread_cond_init(&thpool_p->threads_all_idle, NULL);

But I can't find pthread_mutex_destroy and pthread_cond_destroy call,
should those two functions be called at thpool_destroy()?
Thanks.

i think we need


Convert numpy.float32 output values to float

The output of certain huggingface models (e.g., pipelines with the task of token-classification) outputs values of type numpy.float32.  Data of this type is not JSON serializable.  Therefore, these models will fail when dumping the output, which is typically addressed with a post-processing step.
In mlserver-huggingface runtime.py before Line 86 could you add a post-processing fix to convert values from  numpy.float32 to float?
Minimum viable code to reproduce issue:
import json
from transformers import pipeline

nlp = pipeline("ner", model='dslim/bert-base-NER')
output = nlp("My name is Wolfgang and I live in Berlin")
json.dumps(output)

TypeError: Object of type float32 is not JSON serializable

For reference:
https://github.com/numpy/numpy/issues/16432

Hey @ryanpmccaffrey,
Thanks for raising this one.
MLServer supports the use of content types which let you describe how should MLServer treat each input and output. This is how, for instance, MLServer can encode back multiple response types on the MLflow runtime. However, it seems like the HuggingFace runtime forces every response back to be a JSON-encoded string.
We'll have a look into this to make the HF runtime more flexible (i.e. similar to how the MLServer-MLflow runtime behaves).

@adriangonz I have also recently seen a similar error when trying to create a custom runtime that might include numpy int types.
e.g.
async def predict(self, payload: types.InferenceRequest) -> types.InferenceResponse:
    request = self._extract_json(payload)[0]
    outcomes = self.clf.predict(request)
    output = {
        "id": request["id"],
        "target": outcome[0]
    }
    return types.InferenceResponse(
            id=payload.id,
            outputs=[
                types.ResponseOutput(
                    name=request['id'],
                    shape=[len(output)],
                    datatype="BYTES",
                    data=[output],
                    parameters=types.Parameters(content_type="str")
                )
            ]
        )

The key line is the use of output[0] from scikit-learn this variable is a numpy array and hence it's values numpy types and also hence not serializable.
I think this is outside the current content types since this is a custom input / output JSON-encoding where we input using JSON string and return JSON dict (essentially). The easy fix in this example is to change target vaue to list(output)[0] and then standard python types prevail. I wonder if within MLServer we should update the examples to discuss this or that we can extend the content types to types within types?
Just my thoughts, hope it can help! If you need I can raise a new bug?

Hey @dtpryce ,
Not sure I follow.
In this case, wouldn't you need to call json.dumps() on your output variable so that the output gets sent back as a string? If that's not the case, could you open up a separate issue?
Regarding the original issue, @ryanpmccaffrey this should be already fixed in #664, so I'll be closing this one.

I have the same problem when I run some Huggingface models using SeldonCore.
After checking the output data, I made some changes in this pr #692


Exposing prepared statement fetch size parameter

This exposes the fetch size parameter on the prepared statement. This is needed for Postgres connections for large data sets, or any connection that does not set a fetch size by default, so that the connection does not attempt to bring back all the data in one giant payload.
https://jdbc.postgresql.org/documentation/head/query.html
I also fixed the tests around reflection. The test coverage library seems to inject synthetic fields in all Java classes for measurements. All that needed to be done was filter out the synthetic fields in the reflection unit tests.
I also added a unit test around my change.

I'm having some trouble with the test coverage. There is not a lot of added code here to test, so initially I added some small additions to a couple existing tests, then tried adding a whole new test to see if that made the test coverage happy. Even only adding a setFetchSize stub and verifying it got called got denied.
What exactly am I doing wrong with the test coverage, or is this expected?


Not installing with react 18

Currently, I have created a new next js project with the latest configuration but I am unable to install this library

Same problem here. Tried to install the lib today with NPM in my React project, but got this error:

npm ERR! code ERESOLVE
npm ERR! ERESOLVE unable to resolve dependency tree
npm ERR!
npm ERR! While resolving<EMAIL_ADDRESS>npm ERR! Found<EMAIL_ADDRESS>npm ERR! node_modules/react
npm ERR<EMAIL_ADDRESS>from the root project
npm ERR!
npm ERR! Could not resolve dependency:
npm ERR! peer<EMAIL_ADDRESS>from<EMAIL_ADDRESS>npm ERR! node_modules/react-star-rating-component
npm ERR!   react-star-rating-component@"*" from the root project
npm ERR!
npm ERR! Fix the upstream dependency conflict, or retry
npm ERR! this command with --force or --legacy-peer-deps
npm ERR! to accept an incorrect (and potentially broken) dependency resolution.


Kepler-1137 problem in sweetcat

Seems to have wrong parameters

spectra was combined using data from another closeby star in the kepler field:  KOI-1431
Needs to be removed..


Sync eng/common directory with azure-sdk-tools for PR 1525

Sync eng/common directory with azure-sdk-tools for PR https://github.com/Azure/azure-sdk-tools/pull/1525  See eng/common workflow

/check-enforcer override

The error was an unrelated service error, but still proves the script did not regress live runs (as well as all the other live tests passing).


Freeze when collecting search result

Issue Type: Bug
Using find in files feature, type "iris" on the search input.
Hit Enter
And vs code will freeze (cannot do anything including profiling)
I think it is not about too much search result. The search result is few but it keep freezing.
For other keywords are fine (even with more larger search result). Not yet find another keyword which will cause the freeze.
 I might have fixed this already. If not, can you share this repo?
https://code.visualstudio.com/insiders

I am having the same issue when searching for "rightBar", it either freezes when I search for the word or when I click on one of the results
VS Code version: Code 1.36.1
OS version: MacOS Mojave 10.14.5

Seems to be fixed for vscode insiders

I am having the same problem from time to time with VSCode 1.37.0 on macOS Mojave. It doesn't happen all the time.

I will close this, anyone still having problems please open a new issue.
The only real known issue here is with matches in very long lines.


Fix broken links to native image docs

The links to Oracle's native image documentation were broken by these commits:

https://github.com/oracle/graal/commit/176c01a88cd3d9767b6a26c434a8c62971258924#diff-cf99dae8e37c285d8fc01e5eef559090983afc091cbc2c9c4115f191c95bfb9b
https://github.com/oracle/graal/commit/176c01a88cd3d9767b6a26c434a8c62971258924#diff-8339839fc17c6b11c6baa3e35b0ee0d3c59006198a232f755091dc8259590fc6
https://github.com/oracle/graal/commit/24fe0c62c92b8cf9455126124d89315e207aaae6#diff-852aa4de16767c2bd78b3cb53021cd69b8bf6a7da3488f423d85afbb8963e869
https://github.com/oracle/graal/commit/13f8eac5b3e5497872038c42a48ae019d0e60a70#diff-dbb460466cf6fe74075a9dc7bae22018a1e51b9ba7dce7257a2d4e9ffff705aa

This patch updates the links; for the "Assisted Configuration..." link, I've added an anchor so the topic heading is easier to find.

thank you


Webhook is missing milestone_id changes field

The webhook field milestone_id is missing from the struct. It should be structured as follows:
 "changes": {
    "milestone_id": {
      "previous": 321,
      "current": 123
    }
}

Feel free to open a PR to add it 👍🏻


Use explicit atomics

makes dataProgress atomic as well.

ready.

@planetis-m thanks for the review!

Side Comment.  The atomics arrived with C11, so that must be accounted for in compilation dependencies.  (rayLib already has such dependencies with regard to structure initializations.)  Apparently there must also be #include <stdatomic.h> according to ISO C11 and C17.
I just confirmed that stdatomic.h is available to VS 2022 Build Tools.  I have not checked older Visual Studio versions.


tf.image.rgb_to_grayscale Bug?

Hi,
I have the same problem with this one with the current version of tensorflow under python 3.5. It seems that tf.image.rgb_to_grayscale cannot accept RGB input. Wonder if anyone could check this. Many thanks.
https://stackoverflow.com/questions/40924184/error-while-feeding-images-to-tensorflow-graph

That Stack Overflow question doesn't describe a bug... instead the original questioner was defining a placeholder of the wrong size (1 channel instead of 3 channels). I'm going to close this as working-as-intended, but feel free to reopen if you can show a minimal example that demonstrates a bug.


Next Summit to Attend

Open Source Summit in Vienna: https://events.linuxfoundation.org/open-source-summit-europe/ - September 16th - 17th
https://www.opencompute.org/summit/global-summit - OCP in San Jose -- October 15-17th

Laura to review at TSC on 6/13

OCP is the next summit that we are going to attend. We won't be part of LF booth so need another landing spot (possibly ARM).


Add AsyncRead::poll_read_buf and AsyncWrite::poll_write_buf

Add the methods mentioned in https://github.com/rust-lang-nursery/futures-rs/pull/1688.

Another change I'm thinking about is to add poll_{read,write}_buf like tokio's same name methods (poll_read_buf, poll_write_buf). This makes tokio::io::Async* and futures::io::Async* almost identical, so that tokio probably can use the futures's io traits. (Stabilizing this is probably blocked by bytes 0.5 release, but since poll_{read,write}_buf have default implementations, it could be made to work well by adding a feature gate. )

Probably blocked by bytes 0.5-alpha.
cc https://github.com/tokio-rs/tokio/issues/1297

Perhaps this feature should be initially marked as unstable. (Like current async-await and cfg-target-has-atomic features)

Closing -- see #1826


Update copyright header

Description
Update copyright header
Issues Resolved
N/A
Check List

[X] Commits are signed per the DCO using --signoff

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
For more information on following Developer Certificate of Origin and signing off your commits, please check here.

Codecov Report

Merging #298 (d98a08c) into feature/ip2geo (e06849d) will decrease coverage by 0.18%.
The diff coverage is n/a.

:mega: This organization is not using Codecov’s GitHub App Integration. We recommend you install it so Codecov can continue to function properly for your repositories. Learn more
@@                 Coverage Diff                  @@
##             feature/ip2geo     #298      +/-   ##
====================================================
- Coverage             86.79%   86.61%   -0.18%
+ Complexity              553      551       -2
====================================================
  Files                    73       73
  Lines                  2227     2227
  Branches                190      190
====================================================
- Hits                   1933     1929       -4
- Misses                  218      220       +2
- Partials                 76       78       +2

Impacted Files
Coverage Δ

.../geospatial/ip2geo/action/GetDatasourceAction.java
100.00% <ø> (ø)

...geospatial/ip2geo/action/GetDatasourceRequest.java
83.33% <ø> (ø)

...eospatial/ip2geo/action/GetDatasourceResponse.java
100.00% <ø> (ø)

...al/ip2geo/action/GetDatasourceTransportAction.java
80.00% <ø> (ø)

.../geospatial/ip2geo/action/PutDatasourceAction.java
100.00% <ø> (ø)

...geospatial/ip2geo/action/PutDatasourceRequest.java
93.82% <ø> (ø)

...al/ip2geo/action/PutDatasourceTransportAction.java
85.36% <ø> (ø)

...patial/ip2geo/action/RestGetDatasourceHandler.java
100.00% <ø> (ø)

...patial/ip2geo/action/RestPutDatasourceHandler.java
93.33% <ø> (ø)

...rch/geospatial/ip2geo/common/DatasourceFacade.java
87.71% <ø> (ø)

... and 10 more

... and 2 files with indirect coverage changes


Jvd w fix defined

Sorry about the previous change, that didn't work. I looked at the 'if $var == defined' construct but that isn't a valid puppet construct but I didn't know that. Puppet only knows about 'undef' of empty string ''.

No worries! Thanks!


feat: Leia/feat/argo admin workloads - WIP

Checklist

[ ] Architecture Design Records have been added as adr/*.md and appended to list in adr/_index.md, if applicable.
[ ] The values-schema.yaml file and test/** fixtures have been updated to reflect code changes, if applicable.
[ ] The OpenApi Schema from redkubes/otomi-api project is compatible with definitions from values-schema.yaml file, if applicable.
[ ] Helm releases are meeting otomi's baseline security policies, if applicable.
[ ] Helm chart and helmfile changes are tested against upgrade scenario, if applicable.

Could you add test/fixtures for admin workloads with and without namespace ?

Could you add test/fixtures for admin workloads with and without namespace ?

It's done

perfect!


Removing metadata changes order when sorted by date

Describe the bug
A clear and concise description of what the bug is.
I have some old photos which I cleared the metadata from them and they moved to the top of the gallery (I sort by date, newest first) which is annoying beacuse it messed up the entire gallery (just because I edited the metadata which yeah it does technically change the photo but it shouldn't move to top because the date of "creation" of the photos stays the same)
To Reproduce
Steps to reproduce the behavior:
Sort by date, newest first
Go to any photo that is not the first photo and clear metadata
Expected behavior
A clear and concise description of what you expected to happen.
Stays in its place, not moving to the top(first photo)

My fault actually I removed the date metadata


Add option to show complete frame

For some apps, the bottom of the screen is important. This allows the
developer to specify that this is the case.
Frameit will then shrink the screenshot (if necessary) to ensure that
the entire device and frame is visible in the framed output.
so - if the developer enables this option, it replaces this:

with this:

See issue: #1732

I had a go at this ages ago, but the pull request got behind master, and was closed in the transition to the new repo.
This version is somewhat simpler with a straightforward binary option.
https://github.com/fastlane/fastlane/issues/1732

Can anyone clarify the significance (if any) of the ci fail?
It looks like it failed due to a problem with Felix's password - so presumably nothing to do with this PR.

Ping: This has been up for a week. Any chance of some feedback?

@ConfusedVorlon Thanks for submitting this. We are working through our PRs, but can't guarantee when we'll be able to get to this one since it's a feature add that hasn't been discussed by the community.
We really appreciate that you are working with our community and will get to this! Please make sure that you include test coverage so when we do we'll be able to take care of it quickly.
Thanks!

The tests are failing on this, I'll take over and fix the remaining points, including some code style improvements

Moved over to https://github.com/fastlane/fastlane/pull/4929 🚀

Thanks for sorting it.


Can anyone tell me the difference between “pre_text” and “text” in Batch Prompt Schedule?

I want to know the difference between “pre_text” and “text” in Batch Prompt Schedule.

i got it


Initial developer loop and AIO 0.1.0.preview deployment samples

Dev container setup with K3D, Container registry, VS Code features and extensions
Deployment flow to install AIO in default mode
Created sample .NET application with Bridge to Kubernetes debugging (still work in progress)
Three samples to deploy AIO in developer environment in more controlled way with some Helm and CRDs.

@EliiseS I'd like to merge to main a first sample of all the scripts and setup. Do you want to give your feedback?


Lazy blockquote does not work

Subject of the issue
Lazy blockquote does not work on react-markdown if this plugin is applied.
Your environment

OS: Windows 10
Packages: react-markdown 5.0.3, remark-gfm 1.0.0
Env: React v17.0.2

Steps to reproduce
Use below markdown text.
Markdown allows you to be lazy and only put the `>` before the first
line of a hard-wrapped paragraph:

> This is a blockquote with two paragraphs. Lorem ipsum dolor sit amet,
consectetuer adipiscing elit. Aliquam hendrerit mi posuere lectus.
Vestibulum enim wisi, viverra nec, fringilla in, laoreet vitae, risus.

> Donec sit amet nisl. Aliquam semper ipsum sit amet velit. Suspendisse
id sem consectetuer libero luctus adipiscing.

Expected behavior

Actual behavior

I'm facing the same issue. Is there are any workaround or fix available?

This is the same issue as GH-3, so see my last comment there: https://github.com/remarkjs/remark-gfm/issues/3#issuecomment-789814194

Duplicate of #3


Integrate date-picker calendar input auto-focus option and functionality

What problem does this feature solve?
Allow to optionally automatically focus the date input field on panel open.
This would allow quick editing.
The proposed implementation uses "document.querySelector" which probably you would like to avoid.
What does the proposed API look like?
@Input() @InputBoolean() calInputAutoFocus: boolean;
@ViewChild('datepicker') datepicker: NzDatePickerComponent;

ngOnInit() {
    if (calInputAutoFocus) {
      this.subscribeInputFocus();
    }

}

private subscribeInputFocus(): Subscription {
    return this.datepicker.nzOnOpenChange.subscribe((open: boolean) => {
      if (open) {
        setTimeout(() => {
          const calInput = document.querySelector(
            'calendar-input input.ant-calendar-input'
          ) as HTMLInputElement;
          calInput.focus();
        });
      }
    });
}

This would be great!


provide a way to register callback on selection file

Good. But need a way to register callback on selection file.

Hi @shek2994, could you give an example of what you would like to achieve? Are you looking for a callback function to be called whenever the user presses the select button?

As a quick test I've added a register_callback(f) function that takes a function name. When you select a file or a folder and then click the select/change button it will execute that function.
@majulier, @shek2994 please let me know if that's what your were looking for. If it is then I'll push it and publish version 0.3.1.

Perfect.

Thanks for confirming, @majulier. Version 0.3.1 is now available on PyPi (https://pypi.org/project/ipyfilechooser/). I'll mark this issue as resolved.

@crahan thanks for your work!
Could you please help me, how to access callback function output. The return value from the callback function is written to the console, not to the cell output. Can you please fix it? Thanks!

Hi @idrigo, try using a callback class that has both the callback function and a variable that can store the value you would like to have returned or keep track of. In the below example, the retval variable is set to "Empty" when creating a CallBackClass object. Once the callback_func function is executed, the retval value will be set to "Hello world".
Step 1: create the callback class that has both the callback function and the variable to store what you want to track.
class CallBackClass:
    def __init__(self):
        self.retval = 'Empty'

    def callback_func(self, chooser):
        chooser.title = '<b>Callback function executed</b>'
        self.retval = 'Hello world'

Step 2: initialize a CallBackClass object, and print its retval variable (you should see it print "Empty")
cb = CallBackClass()
print(cb.retval)

Step 3: register the callback function on your filechooser object.
fdialog.register_callback(cb.callback_func)

Step 4: select a file on the filechooser object to trigger the callback function.
Step 5: print the value of the retval variable again (you should now see it return "Hello world")
print(cb.retval)


Select searchable

Added the get method as default and throws an exception if the select parameter is not defined

Thanks!


Fine Tuning GROVER on a custom dataset/domain

Feature Request
Hi I was wondering is there is a way to fine-tune Grover on a custom dataset like we have scripts for gpt2 ?
As we might not of the resources to train it all together on a new dataset but would still like to play around with fine tuning on a new customised dataset.

yes that would be great!

I'd be grateful if someone showed how to do it

I am trying to do fine tuning too. Is it possible to just load the checkpoint files and train the last layers? I haven't checked the checkpoint files though.

Any updates on this?

Any updates on this?

Any updates?


Quartile

Would you like add quartile? This is very useful information.

@wojtpl2, we already have quartiles and percentiles, see https://perfdotnet.github.io/BenchmarkDotNet/Configuration/Columns.htm

Ok Thanks.


Read metadata from schema

Only present required metadata fields to the user
Configured existing default values
To be tested with https://github.com/ONSdigital/eq-survey-runner/pull/1718

@benhowes @insacuri  Ready for re-review

@jonnyshaw89 forgot to mention that this will still need a change to the docker-compose file environment variables, because the survey runner is not available on localhost:5000 from within the launcher's container. I guess that will actually require a new environment variable, because localhost:5000 is fine for the redirect to survey, but eq-survey-runner:5000 needs to be used for loading the schemas.

Well this is rather annoying. i'm going to have to introduce a new var just for use within docker 🤦♂

@dwyeradam ready for you to test https://github.com/ONSdigital/eq-compose/pull/16

Worth disabling the launch button if you haven't chosen a survey?

@MebinAbraham ready for you to look at again

I'm also getting Mebin's error of
2018/08/08 15:42:49 Survey Runner Schema URL: http://localhost:5000
2018/08/08 15:43:11 write tcp [::1]:8000->[::1]:55904: write: broken pipe
2018/08/08 15:43:11 http: multiple response.WriteHeader calls

Didn't seem to get it in compose but now i'm testing locally.

Go launch seems to keep the connection to runner open longer than it needs to. If I run runner with threaded=True, I don't get the long loading problem, or if I stop runner, go launch will load immediately.

@MebinAbraham where are you setting threaded=True

@benhowes I was chatting to @dwyeradam about this yesterday, there is a larger issue that when it is not ticked it is not included in the postdata, therefore i would need to re-request the schema from runner on launch to check for the metadata again

@jonnyshaw89 the easiest thing might be to post JSON from the frontend instead of form data, because you can quite easily build it from the info you have in the frontend.

Running from EQ compose seems to work for all field types now. However, I don't know enough to test the quick launch (i think that was what it was called?).

@dwyeradam Have another check, Charlotte said they are doing tests with census schemas today and this branch is needed to be able to load census schemas.

@MebinAbraham the reason that some test schemas show under business is becasue they dont have a prefix of test_

@MebinAbraham can you re-test


Running MapLibre Open GL project on Apple Silicon device has better Simulator performance on x86_64 than arm64.

The MapLibre project provides iOS binaries for both x86_64 & arm64 which allows for Simulator development on either Intel or Apple Silicon architectures.
When using the Mapbox.xcframework for a 3rd party map project, there are several issues when targeting the iOS Simulator for arm64.

Issues

MapLibre targeting arm64 iOS Simulators 14.5 & 16.0 have a drawing error that is eventually fixed when the map is at steady state.
MapLibre targeting arm64 iOS Simulators 15.4 & 15.5 have a drawing error, followed by an exception when moving the map.
MapLibre targeting x86_64 iOS Simulators fixes drawing & exception issues for SDKs 14.5, 15.4, 15.5, & 16.0.

Questions

Why does setting EXCLUDED_ARCHS, and force targeting x86_64 seem to be the accepted workaround for Simulators on Apple Silicon?

EXCLUDED_ARCHS[sdk=iphonesimulator*]" = arm64

What changed from iOS Simulator 14.5 to 15.5 and to 16.0?

Why so many log warnings for 'cyclone' is not a recognized processor for this target (ignoring processor)?  These are fixed when targeting x86_64.

Test environment

macOS Ventura 13.0 Beta (22A5295i).  Occurs on Big Sur on M1 Apple Silicon, too.
Xcode Version 14.0 beta 3 (14A5270f).  Occurs with Xcode 13.4, too.

Xcode Version 14.0 beta 4 (14A5284g).

M1 Apple Silicon
No issue when running on iOS Devices

Simulator SDK version
Target Architecture
Notes

14.5
arm64
a. Issue type 1) Missing a polygon for a solid area that eventually is drawn.    b. Log contains 'cyclone' is not a recognized processor for this target (ignoring processor)

15.4
arm64
a. Issue type 2) Poorly drawn initial frame, then exception after moving map. EXC_BAD_ACCESS (code =2, address).    b. Log contains 'cyclone' is not a recognized processor for this target (ignoring processor)

15.5
arm64
a. Issue type 2) Exception right away.  EXC_BAD_ACCESS (code =2, address)

16.0 (beta 3)
arm64
a. Issue type 1)  Missing a polygon for a solid area that eventually is drawn.    b. Log contains 'cyclone' is not a recognized processor for this target (ignoring processor)

14.5
x86_64
No visible issues after EXCLUDED_ARCHS[sdk=iphonesimulator*]" = arm64

15.4
x86_64
"

15.5
x86_64
"

16.0
x86_64
"

Test Results
Test iOS Simulator targeting arm64
Four iOS Simulators:
↖ 14.5 — 15.4 ↗
↙ 15.5 — 16.0 ↘ (Xcode 14.0 beta 3 (14A5270f))
https://user-images.githubusercontent.com/118112/179642179-d0f2f20e-ea19-4bec-8e84-ecc0892173ec.mov

Test iOS Simulator targeting x86_64
Four iOS Simulators:
↖ 14.5 — 15.4 ↗
↙ 15.5 — 16.0 ↘ (Xcode 14.0 beta 3 (14A5270f))
https://user-images.githubusercontent.com/118112/179642155-13e689a0-0571-4cbb-b515-ed75aaf83997.mov

Mapbox.xcframework lipo information
# lipo -info Mapbox.xcframework/ios-arm64_armv7/Mapbox.framework/Mapbox
Architectures in the fat file: Mapbox.xcframework/ios-arm64_armv7/Mapbox.framework/Mapbox are: armv7 arm64

# lipo -info Mapbox.xcframework/ios-arm64_i386_x86_64-simulator/Mapbox.framework/Mapbox
Architectures in the fat file: Mapbox.xcframework/ios-arm64_i386_x86_64-simulator/Mapbox.framework/Mapbox are: x86_64 i386 arm64

Mapbox.xcframework for devices & simulators.

arm64 Simulator throws many not recognized processors.

'cyclone' is not a recognized processor for this target (ignoring processor)
'cyclone' is not a recognized processor for this target (ignoring processor)

'cyclone' is not a recognized processor for this target (ignoring processor)

The issue of "intermittent missing polygons in iOS 16" still exists with Xcode Version 14.0 beta 4 (14A5284g).
The same test conditions were applied:  using the MapLibre distributed binaries via Swift Package on a 3rd party map project, tested on Apple Silicon.

Issue still exists under the same test conditions with Xcode Version 14.0 beta 5 (14A5294e) which was released on Aug 8, 2022.

targeting arm64 iOS Simulator 16.0 has a drawing error (the missing polygon issue) that is eventually fixed when the map is at steady state.
targeting arm64 iOS Simulator 15.5 has an exception

Issue still exists under the same test conditions with Xcode Version 14.0 beta 6 (14A5294g) which was released on Aug 23, 2022.  Today's Xcode Beta 6 was also tested with macOS 13 beta 6 (22A5331f).

targeting arm64 iOS Simulator 16.0 has a drawing error (the missing polygon issue) that is eventually fixed when the map is at steady state.
targeting arm64 iOS Simulator 15.5 has an exception

Issue still exists under the same test conditions with Xcode Version 14.0 RC (14A309) which was released on Sep 7, 2022.

targeting arm64 iOS Simulator 16.0 has a drawing error (the missing polygon issue) that is eventually fixed when the map is at steady state.
targeting arm64 iOS Simulator 15.5 has an exception.

Is it resolved with the final xcode 14?

Closing this. With Metal support running MapLibre Native in the simulator works much better.


Update Game CRUD Pages to Reflect New Game Attributes

We need to update our CRUD game pages to reflect the updated game attributes added in #92. This involves adding fields to the details page, the create page and the edit page.
As an example, our current create page is missing most game attributes fields (e.g., complexity, rules, min_playtime, max_playtime, year_published, image):

Oops. This issue already exists #131


How get seed phrase from wallet.dat?

I forgot my seed phrase. But wallet.dat still is in my computer.  How get seed phrase from wallet.dat?

You can't. But you can export keys with:
bitcoin-cli -rpcwallet=ord listdescriptors true

Yeah, unfortunately seed phrases only go one way.
Just merged #3048 which allows dumping and restoring from descriptors. It's basically just a wrapper around the command mentioned above but easier to use.


Implement logic for calculating C2-C4 impacts for template model

Description: This task requires the creation of logic for using the datasets created in #30 to calculate C2-C4 impacts for the template model. This task should output the C2-C4 impacts of the template model to be shared with the frontend.

Use the code that we worked on in #23

@mchaf0126, can we have both a1-a3.csv and c2-c4.csv have the same heading—i.e., GWPb, GWPf, etc., without the '_mfg' and '_eol' decorators? Otherwise, have the decorators uniformly. Currently, everything except for the 'GWP-LULUC' has these decorators. I prefer the first option.

I agree that it will make it easier to read the data in. Do you think there could be some concerns about mixing up mfg and eol factors if we take those out? My concern is that we could mix it up easier if we don't have them labeled that way. Let me know what you think.

Then I suggest using the decorator for 'GWP-LULUC' as well—'GWP-LULUC_mfg' and 'GWP-LULUC_eol'.


🎈 4.0.0

🎈 Release
Preparation for 4.0.0 release.
Comparison to 3.11.1: https://github.com/ignitionrobotics/ign-common/compare/ignition-common3_3.11.1...ign-common4
Targets new branch: ign-common4.
Requires:

~~https://github.com/ignitionrobotics/ign-math/pull/202~~
~~https://github.com/ignitionrobotics/ign-utils/pull/18~~

https://github.com/ignition-tooling/release-tools/issues/421
Checklist

[x] Asked team if this is a good time for a release
[x] There are no changes to be ported from the previous major version
[x] No PRs targeted at this major version are close to getting in (wait for #182 and #183)
[x] Bumped minor for new features, patch for bug fixes
[x] Updated changelog
[ ] Updated migration guide (as needed)
[ ] Link to PR updating dependency versions in appropriate repository in ignition-release (as needed):

Note to maintainers: Remember to use Squash-Merge

Dependencies have been released, this is ready


Remap keys from specific keyboard(s)

Description of the new feature / enhancement
Being able to add a key remapping that only works on a specific keyboard. As in, If I hit key A on my external keyboard, it will type B, but if I hit A on my primary keyboard, it will still type A.
Scenario when this would be used?
Other people probably have better use cases than me, but I got a handheld keyboard/mouse tool and want to remap the media controls on it, which are shaped like a d-pad, to wasd so I can play games with it. However, I don't want to then not be able to turn the volume up or down from my main keyboard. So I want to be able to remap the keys on the handheld keyboard without affecting the key-mappings on my actual keyboard.
Additionally, it's be cool to be able to map keys on a specific keyboard to mouse stuff, like using the arrow keys to move the cursor up/down/left/right by a configurable amount, but that's probably beyond the scope of this ticket.
Supporting information
Other people wanting to do the same thing who I found while searching for this myself:

https://www.reddit.com/r/AutoHotkey/comments/8bvetm/remapping_specific_keyboard/
https://superuser.com/questions/514024/how-can-i-remap-the-keys-for-a-specific-keyboard
https://stackoverflow.com/questions/13742630/can-you-remap-keys-of-a-specific-keyboard

Duplicate of #1460


Don't show underline for leading white space in source examples.

Suppose in this example, there were an error spanning the entire if block. BetterErrors currently underlines (and makes red) the white space between the numbers and the text. I believe it looks much better if this is not the case.
 193 |             if (isMainAxisRow && isStyleDimDefined(child.contents, CSS_FLEX_DIRECTION_ROW)) {
 194 |
 195 |               /* The width is definite, so use that as the flex basis.*/
 196 |               child.contents.layout.flexBasis = fmaxf(child.contents.style.width, getPaddingAndBorderAxis(child.contents, CSS_FLEX_DIRECTION_ROW));
 197 |             } else if (not <| isMainAxisRow && isStyleDimDefined(child.contents, CSS_FLEX_DIRECTION_COLUMN)) {
 198 |
 199 |               /* The height is definite, so use that as the flex basis.*/

How about without underline?
After:

Before:


chore: 🔧 releasing 1.2.5

React version bump to 18.1.0
For https://github.com/react-boilerplate/react-boilerplate-cra-template/issues/168
Changes:
Bump React version to 18.1.0
Update render methods
Updated testing methods

Pull Request Test Coverage Report for Build<PHONE_NUMBER>

0 of 0   changed or added relevant lines in 0 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage remained the same at 99.678%

Totals

Change from base Build<PHONE_NUMBER>:
0.0%

Covered Lines:
249

Relevant Lines:
249

💛  - Coveralls

@rejochandran nice one!!  💯  Really needed, the first thing I do after creating a new project with this template is update react hehe
@Can-Sahin considere it 👍

@rejochandran I think you also can change the tsconfig.json file, in the CompilerOptions the "jsx" field with :
"jsx": "react-jsx",
Regards,

@rejochandran I think you also can change the tsconfig.json file, in the CompilerOptions the "jsx" field with :
"jsx": "react-jsx",
Regards,

@Markkos89, this is already in place:
https://github.com/react-boilerplate/react-boilerplate-cra-template/blob/dc7a46381b6fc6614580a4c4c6269d9fb75cba95/tsconfig.json#L18
Did you refer to any other reference?

@rejochandran I think you also can change the tsconfig.json file, in the CompilerOptions the "jsx" field with :
"jsx": "react-jsx",
Regards,

@Markkos89, this is already in place:
https://github.com/react-boilerplate/react-boilerplate-cra-template/blob/dc7a46381b6fc6614580a4c4c6269d9fb75cba95/tsconfig.json#L18
Did you refer to any other reference?

you are right, apologies, and thanks for the PR !


Add system for creating component attributes + new #[component] attribute

I'm sorry in advance for potentially bad code! I'm still not very comfortable with Rust. Also, I didn't update the changelog, because I don't fully understand how it works. And it hasn't been updated in 2 years, so I'm guessing it's not used anyway.
The details of the actual functionality can be found here: packages/core-macro/src/component_body/mod.rs.
Here's an overview:

New #[component] attribute. What it does:

Renames component function name to PascalCase from snake_case, without generating any warnings. However, everything else other than the function name will still raise a warning if it's not snake_case
Automatically uses #[inline_props] if there's more than 1 parameter in the function.
Maybe more stuff in the future. What I wanted is to have a more "standardized" way of creating components.

New system for making component macros. Both #[component] and #[inline_props] use this. It allows macros to take arguments, use each other, and eliminates repetition. It also verifies the validity of the component function. Right now that only includes checking if:

The component has a Scope argument.
The component has an Element return type.

Add appropriate spans for errors. That means that errors will highlight the section of code that is causing the error, instead of just highlighting the macro. This doesn't apply everywhere. The only macros I touched were #[inline_props], #[derive(Props)] and #[component].
Add global paths in quote!s (:: before each path) in packages/core-macro/src/props/mod.rs. This should fix #1445.
Fix some typos.

The failing checks all seem to be from different packages than core-macro. So not really my fault.

The failing checks all seem to be from different packages than core-macro. So not really my fault.

Core macro is re-exported in the dioxus crate which is used in some tests. This PR doesn't cause any errors within the core macro crate but it does cause errors in users code when you use the core macro crate. It look like several things are broken in the macro. One of these issue is this PR doesn't apply type bounds to generic the inline props macro (causing this example to break)

Core macro is re-exported in the dioxus crate which is used in some tests. This PR doesn't cause any errors within the core macro crate but it does cause errors in users code when you use the core macro crate. It look like several things are broken in the macro. One of these issue is this PR doesn't apply type bounds to generic the inline props macro (causing this example to break)

My bad. I guess at some point I removed the where clause parsing and forgot to add it back. I assume that will clear up a lot of the errors. However, before I start fixing the errors, is this a feature that you would merge?

Yeah, that's all the errors gone. The only check not passing is Clippy, because LinkProps is apparently too complex, but I certainly didn't change it. However, I did change the Props macro, but I only added fully qualified paths, so that bugs like the mentioned #1445 wouldn't happen. Could that be why?

Yeah, that's all the errors gone. The only check not passing is Clippy, because LinkProps is apparently too complex, but I certainly didn't change it. However, I did change the Props macro, but I only added fully qualified paths, so that bugs like the mentioned #1445 wouldn't happen. Could that be why?

Maybe the extra few characters trigger the lint? We can just allow(complex_types) for the link props

I don't know if renaming the function to PascalCase is the right choice here.

You're probably right. I was just thinking that PascalCase function = bad, but I suppose with the warning system it doesn't matter.

I love the lints! but this can stop some valid code from compiling if users use type aliases. We should add a way to opt out of these lints.

Also true. I didn't really consider this because I don't think anyone would create a type alias for Scope or Element, but it wouldn't hurt to add an argument that disables it, just in case.
Tasks

[ ] Remove snake_case -> PascalCase converter, but rather enforce PascalCase.
[ ] Add argument that disables type lints. There's already an argument that disables the snake_case enforcer lint and conversion to PascalCase, so I think it should be kept separate.
[ ] Put the second function inside the main one instead of besides it. Right now this:

#[component]
fn MyFunction(cx: Scope) -> Element {
    todo!()
}

Expands to this:
#[allow(non_snake_case)]
fn MyFunction(cx: Scope) -> Element {
    todo!()
}

#[inline(always)]
fn __my_function(cx: Scope) -> Element {
    todo!()
}

but putting the second one inside the first one would be better.

Perhaps a lint + argument system would also be good.

Adding #[allow(clippy::type-complexity)] doesn't satisfy Clippy. I assume it's because LinkProps isn't the problem, but rather a type generated by derive(Props) is. I'll allow the lint for the entire file. Not a fan, but I don't know how else to do it.

Snake case components that are allowed in rsx because they have at least one _ now require no_case_check:
#[component(no_case_check)]
fn valid_component(cx: Scope) -> Element {
    render! {
        div{}
    }
}

Actually, that might not be final. I think it might be a better idea to unify this under a lint system which the ComponentBody uses. That way every macro that uses ComponentBody has the same lints. No documentation repetition, and it's going to be suuper easy to create new lints. Right now, the "lints" are just randomly scattered in the code.

Here's what I mean:
use syn::{ItemFn, Error};

pub trait ComponentLint {
    fn passes(&self, source: &ItemFn) -> Option<Error>;
}

pub struct ComponentLinter {
    pub lints: Vec<Box<dyn ComponentLint>>,
}

impl ComponentLinter {
    pub fn passes(&self, source: &ItemFn) -> Option<Error> {
        for lint in &self.lints {
            if let Some(error) = lint.passes(source) {
                return Some(error);
            }
        }

        None
    }
}

struct CaseCheckLint;

impl ComponentLint for CaseCheckLint {
    fn passes(&self, source: &ItemFn) -> Option<Error> {
        let ident = &source.sig.ident;

        if !is_pascal_case(&ident.to_string()) {
            Some(Error::new(ident.span(), "lint::ComponentLint - This component does not use PascalCase."))
        } else {
            None
        }
    }
}

fn is_pascal_case(input: &str) -> bool {
    // ...
}

That looks nice! Reusing the allow(snake_case_lint_name), warn(snake_case_lint_name) (using this pattern), and deny(snake_case_lint_name) syntax from normal rust could it a bit easier to learn. There is a lot of overlap here with the dioxus-check crate. With that architecture it seems fairly easy to reuse the visitor in the check crate inside of the component macro for linting.

I never implemented lints that can be used like normal warnings with allow, so I'm not sure how to go about it. However, even then, wouldn't the user need to run a command to check the lints, like cargo clippy? The macro does it as you type, although I don't think there's a way to report a warning rather than an error. And about dioxus-check, what does it do exactly? On GitHub, it says it reports errors in Dioxus code, such as the rules of hooks. On crates.io, it's listed as dioxus-autofmt, stating that it formats rsx! code, but the CLI already has dx fmt. I'm confused :sweat_smile:

1 change: It would let us integrate dx check (for the rules of hooks) into the macro itself

2 change: Reusing the allow(snake_case_lint_name), warn(snake_case_lint_name), and deny(snake_case_lint_name) syntax

So, from this I'm getting the notion that something like this is the desired result:
type ElemAlias<'a> = Element<'a>;

// 2 change:
#[component(allow(no_element_return))]
pub fn App(cx: Scope) -> ElemAlias {
    render! { "..." }
}

// 2 change:
#[component(allow(no_pascal_case_name), warn(no_element_return))]
pub fn snake_case(cx: Scope) -> Element {
    let you_are_happy = true;
    let you_know_it = true;

    if you_are_happy && you_know_it {
      let something = use_state(cx, || "hands");
      //              ^^^^^^^^^
      // hook called conditionally: `use_state` (inside `if`)
      // note: `if you_are_happy && you_know_it { … }` is the conditional

      // 1 change: The above error is caused by the macro, not a command like `dx check`
      println!("clap your {something}");
    }

    render! { "..." }
}

Or a separate #[component_lint] macro so that it's easier to understand what's being allowed/other controls.

The problem is that proc macros are quite limited, since they run before rustc. That's why, for example, aliases cause issues. Maybe it would be better to ditch the macro lints and instead make a Dioxus fork of Clippy.

So, from this I'm getting the notion that something like this is the desired result:
...
The problem is that proc macros are quite limited, since they run before rustc. That's why, for example, aliases cause issues. Maybe it would be better to ditch the macro lints and instead make a Dioxus fork of Clippy.

Yes, dx check could use some of the internals of clippy's analysis. Right now, the analysis is very local which makes it possible to integrate with the component macro for users that do not have the CLI installed/want quicker feedback from rust analyzer. If we switch to a more global form of analysis like clippy, integrating with the component macro makes less sense. Alases are not a huge issue, it would just be nice if there was some way to opt out of part of the linting when aliases cause issues

If we switch to a more global form of analysis like clippy, integrating with the component macro makes less sense.

Of course, but it would be more reliable and powerful. There's things which the component macro can do just fine, but it is limited. With Clippy, we could use it for basically everything. As I mentioned, warnings for one are something that macros can't do, but warnings are something Dioxus would benefit from, with it's custom rsx syntax.

The problem is that proc macros are quite limited, since they run before rustc. That's why, for example, aliases cause issues. Maybe it would be better to ditch the macro lints and instead make a Dioxus fork of Clippy. That would also allow us to make warning lints, since I'm not aware of a way to raise warnings in proc macros.

You can create an very ugly, much less readable version of warnings in a macro:
#[deprecated(note = "this is a custom warning")]
#[allow(unused)]
struct CustomWarning;

const _: CustomWarning = CustomWarning;

That's why I'm suggesting Clippy

Any opinion on this? If you don't want to use Clippy, I can make the lint system in the macro, but right now I'm just waiting on your input.

That's why I'm suggesting Clippy

@ealmloff Any opinion on this? If you don't want to use Clippy, I can make the lint system in the macro, but right now I'm just waiting on your input.

Clippy would definitely be better. If we can get Clippy working, linting in the macro isn't needed

Not including a lint system in this PR, and working on a Clippy-backed system in another PR might be the best option.

Okay then. I'll remove type checking (cx: Scope, -> Element), but I'll keep the functionality that allows a PascalCase component with no warnings, since that was why made this PR in the first place.

Clippy is failing because I made the #[inline_props] macro deprecated. I can remove it if requested, but I think it makes sense to deprecate it, since #[component] automatically inlines props if there's more than 1 argument, along with some additional features.

For the reasons above, I would also like to remove #[inline_props] completely in the future, but that's too drastic for now. Otherwise, it's ready to merge!

Depreciating #[inline_props] makes sense. We should keep it in for at least one major release before completely removing it because of how common it is

I'll update the docs and occurrences of #[inline_props] when it's merged.

I try to avoid merging PRs that have checks failing. I understand why clippy is failing and I agree it isn't much of an issue, but if I merge this without checks passing, all new PRs before you create your fix PR will have tests failing for a unrelated reason which makes finding PRs that are ready for a review more difficult

I try to avoid merging PRs that have checks failing

I understand. I'll replace it then. I'll also update the docsite and create a PR there. Otherwise, does the state of this look good?

I changed all references of #[inline_props] in examples/ and packages/ to #[component]. I also sometimes removed #![allow(non_snake_case)] but not always. Anyway, there's still references to #[inline_props] in the docs/ directory, but that's going to be removed, so I'm not sure what to do there. Should I just remove the folder? I can copy the changes from #1408 then push them here to fix Clippy.

I changed all references of #[inline_props] in examples/ and packages/ to #[component]. I also sometimes removed #![allow(non_snake_case)] but not always. Anyway, there's still references to #[inline_props] in the docs/ directory, but that's going to be removed, so I'm not sure what to do there. Should I just remove the folder? I can copy the changes from #1408 then push them here to fix Clippy.

Either one works. Both removing the folder or switching inline_props to components works should fix it. If you remove the docs folder, I can just close my original PR

Removed the folder. Did all the tests on my machine, so it should be good.


[1.7.10] Weird Issue with Mekanism Reactor

I having issues with the mekanism reactor turning off, it seems to be at random times when i log off then come on later on like hours or days after and the reactor is off. The reactor isn't chunk-loaded while im not online.
I dont think fuel is a issue as it seems to run fine for hours if im staying online, when i come back online and its off it seems the injection rate changes to 0??
Not really sure what the issue is here.
Im using Thermos and latest forge and Mekanism

Simpley place a chunk loader. This should/can fix your problem.
Since I does it with chunk loading, I have never had any on/off problems.

I am using a chunk loader im using a Dimensional anchor

Think i have found the issue i will reply back here and close the issue when i confirm its the issue im having

Fixed it was my issue

Im still getting a issue with the injection rate changing back to 0 after server restart @aidancbrady @chaos234

i can confirm this is happening in MP as well. also chunk loaders are a bad workaround cause some will only work if the player that owns then is on.

@omgalof what setup do you have for mp like what mod pack/server jar/plugins and that

the Ferret Business, it is the mineyourmind server, but details about the server i don't know

@aidancbrady I know your probably busy, but any thoughts on this issue?

Happens to me too

In my experience this has been a persistent problem with reactor across multiple versions, chunk loaders help while you are online but in teh case of a server they always seam to break after going away for  some time, i personally suspect it has to do with others loading the chunks but not activating whatever you use for chunkloading, personally the servers i play on have always used the railcraft personal anchors.

I made a few fixes that should hopefully solve this issue.

@aidancbrady for the 1.7.10 version, if so will there be a download? i only ask as im going to be making a update for my mod pack so would be amazing if i could include it in the update


Some comments recognized as regex

See #95
From @josher19
For example
//   [Showdown]: http://attacklab.net/showdown/

Basically have to make sure that the text between the first / and the last / is only valid regex characters

This was fixed by d83ffb8839a1103a9ca0ed848ddb05eccceff1cc


Running humaneval against llama-3-8b-instruct exl2 quant results in a  silent OOM when samples per task > 7

Evaluation runs to the finish when -spt is set to 7 or less though (Windows 11, 64GB RAM, RTX 4070Ti Super16GB VRAM, ExllamaV2 0.1.4 from the dev branch). This happens with https://huggingface.co/turboderp/Llama-3-8B-Instruct-exl2/tree/6.0bpw quant.
Windows Event Viewer shows this:
Faulting application name: python.exe, version: 3.11.9150.1013, time stamp: 0x660bda91
Faulting module name: c10.dll, version: <IP_ADDRESS>, time stamp: 0x66145ad7
Exception code: 0xc0000005
Fault offset: 0x0000000000063064
Faulting process id: 0x0x4FC
Faulting application start time: 0x0x1DAB8C46E059709
Faulting application path: C:\Users\xxx\AppData\Local\Programs\Python\Python311\python.exe
Faulting module path: C:\Users\xxx\AppData\Local\Programs\Python\Python311\Lib\site-packages\torch\lib\c10.dll
Report Id: aee8a1bf-1ff5-46e8-aadd-120bb5d65edb
Faulting package full name:
Faulting package-relative application ID:

However evaluating Mistral works just fine (Tested against this quant: https://huggingface.co/turboderp/Mistral-7B-v0.2-exl2/tree/6.0bpw)
Here's what I tried:

reinstalling exllamav2, pytorch, safetensors, tokenizers, numpy, flash-attention libraries
updating Nvidia drivers to the newest ones (I was running 552.22, now it's 555.99)
setting CUDA System Fallback Policy on and off
testing against a different LLM - Mistral 7Bv0.2 - and it worked without a hitch with -spt 10

List of installed packages in my system:
Package            Version
------------------ ------------
aiohttp            3.9.5
aiosignal          1.3.1
attrs              23.2.0
blinker            1.8.2
certifi            2024.2.2
charset-normalizer 3.3.2
click              8.1.7
colorama           0.4.6
cramjam            2.8.3
datasets           2.19.1
dill               0.3.8
einops             0.8.0
einops             0.8.0
exllamav2          0.1.4
fastparquet        2024.5.0
filelock           3.13.1
fire               0.6.0
flash-attn         2.5.9.post1
Flask              3.0.3
frozenlist         1.4.1
fsspec             2024.2.0
huggingface-hub    0.23.1
human-eval         1.0.3
idna               3.7
intel-openmp       2021.4.0
itsdangerous       2.2.0
Jinja2             3.1.3
markdown-it-py     3.0.0
MarkupSafe         2.1.5
mdurl              0.1.2
mkl                2021.4.0
mpmath             1.3.0
multidict          6.0.5
multiprocess       0.70.16
networkx           3.2.1
ninja              <IP_ADDRESS>
numpy              1.26.4
packaging          24.0
pandas             2.2.2
pillow             10.2.0
pip                24.0
pyarrow            16.1.0
pyarrow-hotfix     0.6
Pygments           2.18.0
pynvml             11.5.0
python-dateutil    2.9.0.post0
pytz               2024.1
PyYAML             6.0.1
regex              2024.5.15
requests           2.32.2
rich               13.7.1
safetensors        0.4.3
sentencepiece      0.2.0
setuptools         65.5.0
six                1.16.0
sympy              1.12
tbb                2021.11.0
termcolor          2.4.0
tokenizers         0.19.1
torch              2.3.1+cu121
torchaudio         2.3.1+cu121
torchvision        0.18.1+cu121
tqdm               4.66.4
typing_extensions  4.9.0
tzdata             2024.1
urllib3            2.2.1
waitress           3.0.0
websockets         12.0
Werkzeug           3.0.3
wheel              0.43.0
xxhash             3.4.1
yarl               1.9.4

Also running evaluate_functional_correctness humaneval_output.json gives results such as
{'pass@1': 0.0, 'pass@10': 0.0} for both Llama & Mistral. However I narrowed it down to humaneval relying on signal.setitimer which is for UNIX systems only:
{"task_id": "HumanEval/2", "completion": "    return number - int(number)", "result": "failed: module 'signal' has no attribute 'setitimer'", "passed": false}
I guess no humnaneval results grading for us Windows folks unless going down the WSL2 road XD

That looks like a valid result, so I assume you're right about the evaluation not working under Windows. Perhaps you could run it in WSL.
As for where it fails while running the test, I would suspect it's a bug in the tokenizers library, maybe? At least I can't think of another reason it while fail while creating the jobs, because there's nothing happening during that process that's at all different between Mistral and Llama3, except Mistral uses sentencepiece instead.
You should be able to use Mistral with a tokenizer.json file instead of tokenizer.model, which would cause ExLlama to rely on tokenizers instead of sentencepiece. That may still not fail, though, since Llama3 is a tiktoken model, so it's going to be a different code path regardless. I'll try and see if I can replicate the fault under Windows. Thanks for the detailed feedback.

No problemo, it is I who should be thanking you for taking the time to troubleshoot this :)
Some udpates:

I managed to fix the evaluation with the help of GPT4o by relacing the signal.setitimer with threading & multiprocessing modules(but it's unrelated to your project, just FYI)

I also managed to reproduce this OOM with Mistral, it just took more than 10 samples per task. Actually it seems that it crashes with more than 25:

You say OOM.. can you actually see memory usage go up in task manager or whatever as it's creating the jobs? It might be some sort of memory leak in PyTorch then.

I've been experiencing some possibly related errors in an unrelated project, using the HF tokenizer for Sentencepiece models. I get inexplicable segfaults when I run more than about 80,000 encoding operations in a row, and this is without any ExLlama code. I don't seem to be getting the same errors when using non-Sentencepiece models. I think it might be related since most of what happens in the loop that fails for you is just calling the tokenizer thousands of times.
So, could you possibly try with a model that doesn't have a tokenizer.model file? Like maybe Llama3-8B? It could help narrow it down.

Same specs, same issue

Hi @turboderp , I've created a PR with a workoraund for this issue. I hope you'll find some time to have a look, cheers!
https://github.com/turboderp/exllamav2/pull/597


Projector texture on lights showing no effect

Bugsquad edit: Originally seen in 1.1 stable
A texture can be specified as projector for a spotlight, however I can see no effect. I'm expecting the texture to be projected onto the objects. Or at least the light from the spotlight having the shape of the texture.

no milestone for this issue?

Example from @zoiba from #2506 -- http://ffxrunner2.ffx.it/godot_testcases/testcase_projector_lamp.zip

Moving for 3.0, as 2.2 won't happen, plus, rendering engine is currently refactored :smile:

kicked to 3.1, as it needs to use the same architecture as decals.

Most rendering features are now planned to be implemented once we have the Vulkan renderer (scheduled for 4.0). As such, moving to milestone 4.0.

Wow, it's that old huh?
So is there a possibility this hits 3.2 with decals?

I'll close this in favor of https://github.com/godotengine/godot-proposals/issues/451, as feature proposals are now tracked in the Godot Proposals repository.


Remove Constant::eval from Term::reduce

Problem:
Constant::eval is either unsound or nearly useless in strict mode.
Solution:
Make preludes define their own operations on constants, always. Then just remove the whole Constant::eval concept.

Also remove the bar braced constant reduction syntax |x + y|.


FlTechincs - Error 404 "Page Not Found"

ENVIROMENT: production
COMPANY: FlTechincs
URL: FlTechincs peviitor.ro
Browser: Google Chrome
Device: laptop
OS: MacOS
Precondition:
The site must be open in browser.
Steps:
Click on Firme from footer.
Enter FlTechincs in the search bar.
Click the button under the company's logo.
Expected result:
The Scraper-UI related to the company opens.
Actual result:
Error 404 "Page Not Found"
https://github.com/peviitor-ro/Scrapers-UI/assets/137428958/e8f871b1-1052-4b09-aee4-2b69b2a4e0f9

Fixed


ACK! :) Any consideration when using HA alertmanager?

I've been following the linked AM issue for a while and seeing that it goes nowhere was contemplating to write something similar and then this came out, bravo!
Didn't try it yet, but was wondering if there's any consideration when running alertmanager in HA setup? should this sidecar be attached to both? one of?
Any advice?

I think that you need to run a single instance against the entire cluster. Otherwise both might try to extend same silence and you end up with duplicates.

thanks, make sense!


Fixed typo in issue template

Description
Changed zappa_settings.py to zappa_settings.json in the issue template

Coverage decreased (-0.03%) to 73.194% when pulling cf74147d5429cdee83010d5ba6ff363bddd8acdb on OrangutanGaming:patch-1 into 3ccf7490a8d8b8fa74a61ee39bf44234f3567739 on Miserlou:master.

Coverage decreased (-0.03%) to 73.194% when pulling cf74147d5429cdee83010d5ba6ff363bddd8acdb on OrangutanGaming:patch-1 into 3ccf7490a8d8b8fa74a61ee39bf44234f3567739 on Miserlou:master.

Coverage decreased (-0.03%) to 73.194% when pulling cf74147d5429cdee83010d5ba6ff363bddd8acdb on OrangutanGaming:patch-1 into 3ccf7490a8d8b8fa74a61ee39bf44234f3567739 on Miserlou:master.

Coverage decreased (-0.03%) to 73.194% when pulling cf74147d5429cdee83010d5ba6ff363bddd8acdb on OrangutanGaming:patch-1 into 3ccf7490a8d8b8fa74a61ee39bf44234f3567739 on Miserlou:master.


09/11/2018 02:07 : Create Pull Request for branch master -> prTesting1_2018091102

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🚀 Deploy this!

This is very cool would highly recommend deploying this!
Would recommend throwing up a UI in next or really anything and using https://vercel.com/docs/serverless-functions/supported-languages#python as cloud functions to generate the image and return to the client. Then launch it on Product Hunt :rocket:
Also looking forward to chatting next week!

is this application compatible with macOS?


Add random or configurable delay on each image download

I think this may be needed because Pixiv really doesn't like users rapidly downloading tons of images and it also makes site slower for others.

See comment in last commit, you forgot to change one enableDelay to downloadDelay, everything's not working.

updated

Appears to be working now. Can't say anything about usefulness yet.

well, you can set to 0 to disable.
As far as I know, I never hit error without setting any delay with Pixiv.
I know some sites will throw 429 Too Many Request if you download too much imaged 😛

Ok, testing of this feature feels like it's  increased overall throughput of PixivUtil2. On the order of 30% less time taken to process my hueg list.txt

Prop delay between separate pages of manga?

no, it is only for each post.


lars max l1 norm test

@Saurabh7 can you just put this test into the other branch PR, that way travis will test everything at once...


ALTER PRIMARY KEY doc updates

This PR includes the following changes:

Added note on NOT NULL requirement for new PK columns to ALTER PRIMARY KEY page.
Without this constraint, the statement will return the following error:
ERROR: cannot use nullable column "valid" in primary key
SQLSTATE: 42P15

Removed outdated requirement note in PARTITION BY docs.
This fixes https://github.com/cockroachdb/docs/issues/9507.

This change is

Online preview: http://cockroach-docs-review.s3-website-us-east-1.amazonaws.com/301e585a50e004e7fd24d399998b9e3cc353f8ad/
Edited pages:

v20.1/alter-primary-key.md
v20.1/partition-by.md
v20.2/alter-primary-key.md
v20.2/partition-by.md
v21.1/alter-primary-key.md
v21.1/partition-by.md


Bump up allowed Poison version

Some Phoenix libraries like Addict are not yet ready for Phoenix 1.2 due to dependency issues. Mailgun is one, since it is not allowing Poison beyond 2.0. When creating new Phoenix 1.2 applications, Poison is being locked at 2.2.0.

Yep, I'm at the same point. A quick change would be awesome :)

@chrismccord Pinging for review. Is this repo abandoned?

Might as well make this also OK with poison 3.0 and above at this point (though it doesn't seem likely that this will be merged).


Concat and convolve / Encoder and dot

Hello,
What is the difference in performance between the Concat-Convolve and Encoder-Dot architectures ?
Thanks!

Encoder-dot architecture generally performs better in terms of downstream classification performance. But we only evaluated this thoroughly with images.


[flash_ctrl/dv] Add full checking of scb mem model

Hi,
This PR is adding a full check of the scoreboard memory model at the end of each test in the scoreboard check_phase.
This also required to do full updates of the model after each backdoor initialization.
This PR resolves issue #12565
Thanks!
Signed-off-by: Eitan Shapira<EMAIL_ADDRESS>

@nikola-miladinovic i'm not quite following what you mean. If the scoreboard is enabled, what's the reason we should not do this backdoor check all the time?

Due to parallel threads issued when non blocking reads are issued, i have distinguished checks of blocking and non-blocking reads - lines 102-104 in the scoreboard. As I remember,in the non-blocking reads,task for returning d.data was not collecting transactions correctly (i think all zeros were returned).

Due to parallel threads issued when non blocking reads are issued, i have distinguished checks of blocking and non-blocking reads - lines 102-104 in the scoreboard. As I remember,in the non-blocking reads,task for returning d.data was not collecting transactions correctly (i think all zeros were returned).

but if i understood correctly, the check from @eshapira is more about ensuring the scoreboard programmed / erase state is reflective of what's actually there right? So I don't think we have to worry about reads do we? It's more about whether all the programs / erases, backdoor or frontdoor are correctly collected in the scoreboard.

@tjaychen That's right. It's mainly to make sure there aren't any unexpected changes of data in the flash memory.
@nikola-miladinovic I'm not quite understand your concern, but notice that the check I added is also gated by scb_check and I assume that most of the tests running with scb_check=1 would be better have this full check.
In addition, I also ran local regression on all the tests with 2 seeds for each of the tests and they all passed (except the 2 stress_all tests, which I'm pretty sure didn't passed before this change as well).

thanks all, i will merge this now.

Thanks!


Different behavior between inline type and type alias

TypeScript Version:  3.4.0-dev.20190131
I'm trying to find base types of some class properties. For example, for a given X[], trying to find X.
I use same definition inline and as a type alias. Type alias has error whereas inline type works.
Search Terms: Type alias, different behavior, conditional
Code
Below is simplified version of the actual code. Generics A1 and A2 in class method whereRelated are equal, except A1 uses inline type and A2 uses a type alias.
interface RelationFields {
  x: A;
  y: A[];
  z: A[];
}
type Name = keyof RelationFields;
type ShouldA<RF extends RelationFields, N extends Name> = RF[N] extends A[] ? RF[N][0] : never;

class A {
  x: A;
  y: A[];
  z: A[];

  whereRelated<
    RF extends RelationFields = RelationFields,
    N extends Name = Name,
    A1 extends A = RF[N] extends A[] ? RF[N][0] : never, // Works
    A2 extends A = ShouldA<RF, N> // Type is same as A1, but is not assignable to type A
  >(): number {
    return 1;
  }
}

Expected behavior:
Either both A1 and A2 work or both have errors.
Actual behavior:
A1 works, but A2 has an error: ... Type 'RelationFields[N][0]' is not assignable to type 'A'.
Playground Link: See here
Related Issues: #15973 seems similar, but I can not be sure. If this is an expected behavior, an advise how to use a type alias is appreciated.
Thanks,

I've run into similar problems before. Not quite your exact situation, though.
Myack-y workaround is to add a comment saying I'm about to use a workaround, and then do,
//For some reason, I need to surround this with Extract<>
//TODO Investigate
A2 extends A = Extract<ShouldA<RF, N>, A>

@AnyhowStep Thanks for the tip for Extract. I will wrap original alias with Extract until finding a direct solution:
type ShouldA<RF extends RelationFields, N extends Name> = Extract<RF[N] extends A[] ? RF[N][0] : never, A>;

I'm wondering if #29698 is also fundamentally related to this---inlining Record produces the correct errors.

@jack-williams nope.


[DNM] Test openmm 8.1 release candidate

Description
PR to test Openmm 8.1 release candidate. Do not merge.

Did changes happen in the Beta from the RC? I kicked of a GPU runner here https://github.com/choderalab/perses/actions/runs/6893843427

@mikemhenry Not sure, but beta seemed to have been released more than a month ago, while rc is from ~6 days ago.

Closing. Superseded by #1243


Implement Integer#chr in Rust

Fixes GH-186.
This PR adds support for Integer#chr when called with no argument. Integer#chr with no argument requires that self convert to a u8 and creates a String with a single byte Vec<u8>.

Ran into a segfault when implementing this which I worked around with a4ffbe3.
Upstream report: https://github.com/mruby/mruby/issues/4660


Structured report

Hey,
I love the work you've done here.
I'm currently working to build an online viewer and I've run into a problem with Structured Reports. This WADO image loader doesn't handle it very gracefully. (No pixel representation or bits allocated). I can probably detect whether it's a structured report before loading it, however, after that I don't know what to do with it. It's not exactly a PDF. Any ideas would be greatly appreciated.

DICOM SR is a complex document which contains information about one or more images.  Since cornerstone deals with displaying images in a single viewport, direct interpretation of DICOM SR is outside the scope of cornerstone and should therefore be done at a higher level.


Search from a list of patients (eg. pre-enrolled) in a FHIR datastore on the device or from the server

This is a subtask of https://github.com/OpenSRP/fhircore/issues/50

@f-odhiambo is this different from https://github.com/opensrp/fhircore/issues/20?

Closing this issue duplicate


Add query action parsing

Purpose
$subject.
Fixes #23286
Approach
N/A
Samples
N/A
Remarks
N/A
Check List

[x] Read the Contributing Guide
[ ] Updated Change Log
[ ] Checked Tooling Support (#)
[x] Added necessary tests

[x] Unit Tests
[ ] Spec Conformance Tests
[ ] Integration Tests
[ ] Ballerina By Example Tests

[x] Increased Test Coverage
[x] Added necessary documentation

[ ] API documentation
[ ] Module documentation in Module.md files
[ ] Ballerina By Examples

Codecov Report

Merging #23407 into master will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##           master   #23407   +/-   ##
=======================================
  Coverage   14.59%   14.59%
=======================================
  Files          51       51
  Lines        1411     1411
  Branches      219      219
=======================================
  Hits          206      206
  Misses       1189     1189
  Partials       16       16

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[FEATURE] Instant View support

Describe the solution you'd like
Implement instant view support for notes with links.
It's quite often that people post notes containing links. It would be a huge UX win if amethyst support almost immediate view of those links provided they are instant view enabled.
Bounty (in Bitcoin sats) offered for the implementation
I offer 25000 sats.

Last update was 4 years ago.
It looks like an abandoned project?

It looks like an abandoned project?

The spec is done, telegram has created and implemented it and published for content providers and other apps to follow. Currently many prominent web sites support it including nostr ecosystem.

This looks very centralizing. Somebody has to approve templates for EACH website so that the app can render. That happens on a fixed server and that server can lie to everyone. Besides that, the server operator can now track every link a user sees and build list of interests for that user.

The request is not about adding instant view to nostr notes. The request is about rendering external links sent via nostr.


Compiling with USE_TVMOP flaky due to downloading

[2020-05-04T03:39:44.167Z] -- Prepare external packages for TVM...

[2020-05-04T03:39:44.167Z]   % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current

[2020-05-04T03:39:44.167Z]                                  Dload  Upload   Total   Spent    Left  Speed

[2020-05-04T03:39:44.167Z]
  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0

[2020-05-04T03:50:03.354Z]
 32  325M   32  105M    0     0   112M      0  0:00:02 --:--:--  0:00:02  112M
 44  325M   44  143M    0     0  67.2M      0  0:00:04  0:00:02  0:00:02 31.8M
[...]
 44  325M   44  143M    0     0   238k      0  0:23:15  0:10:14  0:13:01     0

[2020-05-04T03:50:03.354Z] curl: (56) OpenSSL SSL_read: SSL_ERROR_SYSCALL, errno 110

Suggest to rely on system LLVM instead of downloading LLVM as part of configuration.
http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/mxnet-validation%2Funix-cpu/detail/PR-18226/1/pipeline

Yet another issue : unrelated PR #18206
curl: (7) Failed to connect to releases.llvm.org port 443: Connection timed out

unix-cpu
http://jenkins.mxnet-ci.amazon-ml.com/blue/rest/organizations/jenkins/pipelines/mxnet-validation/pipelines/unix-cpu/branches/PR-18206/runs/7/nodes/47/log/?start=0
unix-gpu
http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/mxnet-validation%2Funix-gpu/detail/PR-18206/6/pipeline

Another one #18261
http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/mxnet-validation%2Funix-gpu/detail/PR-18261/1/pipeline
http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/mxnet-validation%2Funix-cpu/detail/PR-18261/1/pipeline
@leezu
clang failed but not sure if its related to this issue
Is this 500 internal server error in google protobuf related to tvm?
http://jenkins.mxnet-ci.amazon-ml.com/blue/organizations/jenkins/mxnet-validation%2Fclang/detail/PR-18261/1/pipeline

No, that's due to using Makefile build

I think this issue requires developers to navigate through the set-up of CI pipeline and eventually come to the responsible script, so it's not a good first issue without pointers on the code base.

This is being invoked here:
https://github.com/apache/incubator-mxnet/blob/3840786a25b16d0cfe6411e26f25aba8d3b574ff/cmake/BuildTVM.cmake#L19
that tries to download the llvm archive here:
https://github.com/apache/incubator-mxnet/blob/3840786a25b16d0cfe6411e26f25aba8d3b574ff/contrib/tvmop/prepare_tvm.sh#L48


maxLength / minLength (enhancements)

Thank you for this great implementation of TipTap.
I wonder if you are planning to implement some of the basic textArea properties (min & max length + maybe rows/min-height).
Keep up the great work ! 🎉

hey @a-zog,
I could possibly do something with min and max length, although generally when working with rich content like this it is for long form content that you'd usually not have those kinds of limits on.
As far as the height goes there are classes on the component that you can use to overwrite the styling to suit your needs, but since Tiptap doesn't work directly on a Textarea input it's not possible to use the rows() method to adjust the height of the editor.

Support for minLength and maxLength has been added to latest release. v0.2.6


Diagrams with more than 1 page get corrupted

Using plugin version 1.5.0 on Windows any diagram with more than 1 page will get corrupted for me.
Steps to reproduce:

View a diagram with 2 pages
Switch to the second page you will get an error saying "Cannot set property 'innerHtml' of null"
Switch to any other document
Switch back to the diagram and all but the first pages will have been deleted.
The plugin will save the corrupted diagram to disk erasing the pages permanently.

I ran into this as well, if it weren't for good backups I'd be in a world of hurt :)


Interface with UI referee

Since the UI will also be able to send referee commands, we must also be able to take UI ref commands and convert to the local ref object.

UI ref commands go through the GC, so the data path is the same between command sources.


WHY WIDTH MUST BE DEFINED ??

please make the width auto base on the container width/flex just like height:0
it's easier to make it auto
Thank You.

There have some issues that I haven't solved. But PR welcome.
https://github.com/dohooo/react-native-reanimated-carousel/issues/268

Maybe I'll solve it soon.

Hello @dohooo is this fix? can i use width 100% now?
<Carousel width={'100%'}/>
OR
const baseOptions = isVertical
    ? ({
        vertical: true,
        width: PAGE_WIDTH,
        height: PAGE_WIDTH / 2,
      } as const)
    : ({
        vertical: false,
        width: '100%',
        height: 0,
      } as const);

No, not yet.


Parameters, data format?

Is there any documentation on the data format or the parameters that you take in?

pipeline_id: 1
extract_features:
  column_id: example_id
  impute_function: tsfresh.utilities.dataframe_functions.impute
  default_fc_parameters: MinimalFCParameters()
model_training:
  model_args:
  model: sklearn.ensemble.RandomForestClassifier

The training specific parameters are specific to tsfresh, tpot, and sklearn.

extract_features
model - any sklearn-learn compatible classifier
model_args - arguments for associated sklearn classifier

For the data format, you have to use the same format as shown in the data folder. Check out the appendix of the Jupyter Notebook to see how I generated the data.