WoWs Info Version 1.7.7 (1.1.1)

For this update, I want to fix all broken screens and verify if all features are still up to date. Also, add some new features. This update will focus on iOS first and may add more native iOS features.

[x] Support only iOS 12+ (Drop 32bit support)

I will support iOS 11+, maybe iOS 12 next time if I still update anything

[x] Fix home settings button
[x] Collection can be changed back

There weren't enough data but now, there are quite a lot

[ ] Use native review button

This can be a nice one to have
Also, the previous one can be improved

[ ] Commander Skill

Data is empty
TO BE REMOVED

[ ] Player rank

It is empty so probably the app crashed

[x] Player achievement

Data is empty This has been fixed

[ ] Use WoWs Fitting Tool website
[x] #57
[ ] 3D Touch

[x] Support 3D Touch
[ ] Handle pending shortcut
[ ] Handle user account

[ ] Change Logo with a native view
[ ] App Widget
[ ] Today Extension
[ ] Improve loading and animation
[ ] Fix RS starting from iOS 14 or 13
[ ] Firebase integration

Ideally, this update can be live next year.

See #69. We will unify the mobile version from 1.8.


Is it possible to merge notebook.showCellStatusBar and notebook.showCellStatusBarAfterExecute to single setting

Testing #125013
Is it possible to have a single setting called notebook.cellStatusBarVisibility with values show, hidden and showAfterExecute?

duplicate of #125173


Error when checking models, OSError: [Errno 5] Input/output error

I am trying to submit a model following the tutorial here:
https://www.brain-score.org/tutorial/deepdive_2
However, when I run the local check check_models.check_base_models(__name__) I got the following error.
[yu_xie@node091 yudixie_resnet50_imagenet1kpret_0_240222]$ python model.py
/om/user/yu_xie/miniconda3/envs/brainscore/lib/python3.7/site-packages/brainscore_core/metrics/__init__.py:16: FutureWarning: xarray subclass Score should explicitly define __slots__
  class Score(DataAssembly):
Loaded model from yudixie_resnet50_imagenet1kpret_0_240222_weights.pth
Loaded model from yudixie_resnet50_imagenet1kpret_0_240222_weights.pth
Traceback (most recent call last):
  File "model.py", line 67, in <module>
    check_models.check_base_models(__name__)
  File "/weka/scratch/weka/dicarlo/yu_xie/projects/brain-score/brainscore_vision/model_helpers/check_submission/check_models.py", line 38, in check_base_models
    check_processing(model, module)
  File "/weka/scratch/weka/dicarlo/yu_xie/projects/brain-score/brainscore_vision/model_helpers/check_submission/check_models.py", line 46, in check_processing
    benchmark = _MockBenchmark()
  File "/weka/scratch/weka/dicarlo/yu_xie/projects/brain-score/brainscore_vision/model_helpers/check_submission/check_models.py", line 61, in __init__
    assembly_repetition = load_dataset("MajajHong2015.public").sel(region="IT").squeeze("time_bin")
  File "/weka/scratch/weka/dicarlo/yu_xie/projects/brain-score/brainscore_vision/__init__.py", line 34, in load_dataset
    return data_registry[identifier]()
  File "/weka/scratch/weka/dicarlo/yu_xie/projects/brain-score/brainscore_vision/data/majajhong2015/__init__.py", line 55, in <lambda>
    stimulus_set_loader=lambda: brainscore_vision.load_stimulus_set('hvm-public'),
  File "/weka/scratch/weka/dicarlo/yu_xie/projects/brain-score/brainscore_vision/data_helpers/s3.py", line 42, in load_assembly_from_s3
    stimulus_set = stimulus_set_loader()
  File "/weka/scratch/weka/dicarlo/yu_xie/projects/brain-score/brainscore_vision/data/majajhong2015/__init__.py", line 55, in <lambda>
    stimulus_set_loader=lambda: brainscore_vision.load_stimulus_set('hvm-public'),
  File "/weka/scratch/weka/dicarlo/yu_xie/projects/brain-score/brainscore_vision/__init__.py", line 41, in load_stimulus_set
    return stimulus_set_registry[identifier]()
  File "/weka/scratch/weka/dicarlo/yu_xie/projects/brain-score/brainscore_vision/data/majajhong2015/__init__.py", line 104, in <lambda>
    zip_version_id="UzgkNOtIWMXaMN7vUA0FVemXLTvdtH13")
  File "/weka/scratch/weka/dicarlo/yu_xie/projects/brain-score/brainscore_vision/data_helpers/s3.py", line 67, in load_stimulus_set_from_s3
    stimuli_paths = [Path(stimuli_directory) / local_path for local_path in os.listdir(stimuli_directory)
OSError: [Errno 5] Input/output error: '/home/yu_xie/.brainio/image_dicarlo_hvm-public'

This seems to be an OS error from the call to os.listdir.
Is that directory somehow exceedingly large? (maybe related: https://stackoverflow.com/q/66747762/2225200)

This can also occur (according to some other internet sources) if either memory or disk space has been exceeded. Are you maxing out a storage quota anywhere?

I just checked, and I have ample space on the disk

Gotcha- do you have read/write permissions for /home/yu_xie/.brainio/image_dicarlo_hvm-public?
@mschrimpf is this indicative of something we need to change in load_stimulus_set_from_s3?

Yes, I have.
If this information helps: /home/yu_xie/.brainio is a symbolic link to a directory on om. I use this because openmind has a small home quota.

we have used this function for the last years without a problem, so my guess would be that this is random OpenMind shenanigans. FWIW I did sometimes see similar OS errors (outside of Brain-Score for other code) with a similar symlink setup

I tried to test this multiple times on openmind at different times during the day, and this error persists. Have people been able to run check_models.check_base_models(__name__) successfully on openmind?

We have had test runs on Openmind of the check_models function, but I will confirm this again

@YudiXie Just wanted to follow up on this


Align UnionMessageReply with the various replies that processMessage

UnionMessageReply was not fully in sync with the actual replies.

data is actually within record for a RecordsReadReply.
modified events to entries for EventsGetReply and EventsQueryReply
modified messages to entries for MessagesGetReply

This way all of the individual replies can be represented by:
/**
 * Catch-all message reply type. It is recommended to use GenericMessageReply or a message-specific reply type wherever possible.
 */
export type UnionMessageReply = GenericMessageReply & {
  /**
   * Resulting message entries or events returned from the invocation of the corresponding message.
   * e.g. the resulting messages from a RecordsQuery, or array of messageCid strings for EventsGet or EventsQuery
   * Mutually exclusive with `record`.
   */
  entries?: QueryResultEntry[] | MessagesGetReplyEntry[] | string[];

  /**
   * Record corresponding to the message received if applicable (e.g. RecordsRead).
   * Mutually exclusive with `entries` and `cursor`.
   */
  record?: RecordsWriteReply;

  /**
   * A cursor for pagination if applicable (e.g. RecordsQuery).
   * Mutually exclusive with `record`.
   */
  cursor?: string;
};

Codecov Report
All modified and coverable lines are covered by tests :white_check_mark:

Comparison is base (a6f95fb) 98.83% compared to head (56643c9) 98.83%.

Additional details and impacted files
@@            Coverage Diff             @@
##             main     #648      +/-   ##
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  Lines        9240     9232       -8
  Branches     1391     1391
==========================================
- Hits         9132     9124       -8
  Misses        100      100
  Partials        8        8

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Add findGitDir function

Fixes #57.

Add tests. This project already is a git repository and there are subdirs.


Add some cmake options useful for embedding

While following the recommended way to incorporate cpu_features into a cmake project some cpu_features lib build artefacts become included into the target project.
For example, list_cpu_features executable will be built and installed among project target binaries.
This pull request proposes to include some cmake options making cpu_features library embedding more flexible:

BUILD_SAMPLE_APP
This option controls if list_cpu_features will be built and included into the install target. Default is ON.
INCLUDE_INSTALL
This option allows to omit providing install targets, disabling installing cpu_features lib build artefacts into the main project install targets. Default in ON.
INCLUDE_TESTS
This option disables testing target for cpu_features at all. Since cpu_features uses standard cmake CTest BUILD_TESTING variable then it could interfere with the main project cmake settings. Having additional option solves this issue. Default is ON.

I'm not sure about INCLUDE_TESTS, user can use ExternalProject_Add and pass BUILD_TESTSING with another paramater in CMAKE_ARGS like this:
include(ExternalProject)
ExternalProject_Add(cpu_features
  GIT_REPOSITORY    https://github.com/google/cpu_features.git
  GIT_TAG           main
  SOURCE_DIR        "${CMAKE_BINARY_DIR}/cpu_features-src"
  BINARY_DIR        "${CMAKE_BINARY_DIR}/cpu_features-build"
  CMAKE_ARGS
    -DBUILD_TESTING=OFF
)

@Mizux, what do you think?

Well, I did try using 'external project' cmake module before, but actually this pulls out more problems, for example, it's not possible to use 'find_package' until cpu_features install target's done.
So I followed the recommended way of embedding cpu_features.

My 2 cents:

BUILD_SAMPLE_APP: ok on the principle to be able to disable this executable

INCLUDE_INSTALL: not sure is useful, which scenario need it ?
maybe want to static link with libcpu_feature and integrate the sources using fetch or add_subdirectory ?

INCLUDE_TESTS: not OK, we already have BUILD_TESTING you can set to disable tests also in another PR test will be disable by default if cpu_feature is not the cmake root project

side note
Now, Fetch() nicely interact with find_package() thanks to the 3.24 OVERRIDE_FIND_PACKAGE
see: https://cmake.org/cmake/help/latest/module/FetchContent.html#commands
Since variable are directory scoped you can add the FetchContent() in a subdir CMakeLists.txt and just before you add
set(BUILD_TESTING OFF)  thus all FetchContent will be run with BUILD_TESTING=OFF (even if it set to ON for the super project)

2. INCLUDE_INSTALL: not sure is useful, which scenario need it ?
maybe want to static link with libcpu_feature and integrate the sources using fetch or add_subdirectory ?

Attached is the sample app which exactly reflects what we have in our project. This illustrates the problems I'm trying to solve with this pull request.

3. INCLUDE_TESTS: not OK, we already have BUILD_TESTING you can set to disable tests also in another PR test will

Excellent! I'll remove this from my PR and will wait for that PR to land.

Now, Fetch() nicely interact with find_package() thanks to the 3.24 OVERRIDE_FIND_PACKAGE

Unfortunately, we can't currently use cmake 3.24, the latest version available to use is 3.13.

Part of this PR has been submitted in https://github.com/google/cpu_features/pull/317. amitin@ do you still think this PR is necessary?

Part of this PR has been submitted in #317. @amitin do you still think this PR is necessary?

Yes. That PR only partially solves the problem of embedding which we have to get solved.
My PR also includes an option to disable any 'install' targets, so we don't need to install cpu_features include files and static lib.
Additionally, it's not really clear why you didn't land my PR which I submitted six month ago but landed a new one.

Part of this PR has been submitted in #317. @amitin do you still think this PR is necessary?

Yes. That PR only partially solves the problem of embedding which we have to get solved. My PR also includes an option to disable any 'install' targets, so we don't need to install cpu_features include files and static lib.

AFAIU:

is solved by #317
remains to be solved
is solved as per https://github.com/google/cpu_features/pull/303#issuecomment-1479495172

I'll have a look at 2. tomorrow.

Additionally, it's not really clear why you didn't land my PR which I submitted six month ago but landed a new one.

My apologies for this, this PR went out of my radar and I started reviewing the most recent PRs first. It's unfortunate that #317 went in before yours.

For 2 and to be loosely consistent (and also since i like it) you could (should ?) follow the abseil-cpp style
https://github.com/abseil/abseil-cpp/blob/197bac8574c511b1937b60ce6405462f9af678ce/CMakeLists.txt#L65-L71
(note: prefer to use ENABLE_INSTALL since cpu_feature don't prefix its options)
note: https://cmake.org/cmake/help/latest/module/ExternalProject.html#explicit-step-management

Install Step Options:
Passing an empty string as the  makes the install step do nothing.

Other proposal
https://github.com/google/googletest/blob/main/CMakeLists.txt#L20 INSTALL_GTEST (suffix)
https://github.com/protocolbuffers/protobuf/blob/main/CMakeLists.txt#L27 protobuf_INSTALL (prefix)
https://github.com/pybind/pybind11/blob/master/CMakeLists.txt#L86-L91 PYBIND11_INSTALL (prefix with master check)

Opened https://github.com/google/cpu_features/pull/325 as I created another patch which includes the most recent changes related to my previous PR.


Add use_bfc_allocator parameter to StreamExecutor C API

use_bfc_allocator parameter will decide whether to wrap pluggable device SubAllocator with BFC allocator or not.
I am thinking it can be used as follows:
See lines here:
https://github.com/tensorflow/tensorflow/blob/2c31420a533f839d19af384c3d83038fde3583f8/tensorflow/core/common_runtime/pluggable_device/pluggable_device_process_state.cc#L115
They should be replaced with something like
Allocator * device_allocator = nullptr;
if (use_bfc_allocator) {
  PluggableDeviceBFCAllocator* device_bfc_allocator =
        new PluggableDeviceBFCAllocator(
            sub_allocator, total_bytes, options,
            strings::StrCat("PluggableDevice_", tf_device_id.value(), "_bfc"));
  device_allocator = device_bfc_allocator;
} else {
  PluggableDeviceSimpleAllocator* simple_allocator = new PluggableDeviceBFCAllocator(sub_allocator);
  device_allocator = simple_allocator;
}

PluggableDeviceSimpleAllocator should just be a minimal wrapper over the sub_allocator (currently not implemented as a part of this PR). This seems much simpler to me than what I was thinking of originally (sorry for the delay).
PTAL @penpornk @kulinseth, @jzhoulon

@annarev  Thanks for the PR!
Do you mean the PluggableDeviceSimpleAllocator will directly call the stream_exec_->AllocateArray(), which calls SP_StreamExecutor::allocate(const SP_Device* device, uint64_t size, int64_t memory_space,SP_DeviceMemoryBase* mem ),  and plugin authors decides whether allocate is build on top of a custom allocator? if it is, I think it is better to add an field such as void* custom_allocator in SP_Device? since the allocator should be per-device? Thanks

Thanks @annarev for the PR. SimpleAllocator can be something straightforward calling into AllocateRaw and that would be fine for us. We had brief discussion of this during the design phase.  To keep it simple we can leverage SubAllocator currently. After the pluggable device implementation merges we can use the use_bfc_allocator flag to pass this information in device_process_state..
There are few places in pluggable_device_process_state like AllocatorParts will require changes to accomodate the SimpleAllocator design, currently it assumes the Device memory allocator is BFC. I can upload a PR which I have tested locally works with our design. Please let me know.

@annarev Thanks for the PR!
Do you mean the PluggableDeviceSimpleAllocator will directly call the stream_exec_->AllocateArray(), which calls SP_StreamExecutor::allocate(const SP_Device* device, uint64_t size, int64_t memory_space,SP_DeviceMemoryBase* mem ), and plugin authors decides whether allocate is build on top of a custom allocator? if it is, I think it is better to add an field such as void* custom_allocator in SP_Device? since the allocator should be per-device? Thanks

Sounds about right. use_bfc_allocator will decide whether to use a simple allocator or a BFC allocator. So, a plugin that wants to use their own custom allocation strategy and doesn't want to wrap with BFC, can set this to true. Do you mean add use_bfc_allocator parameter per device? I was just thinking having one setting for all device types is easier to pass through on TF end. Do we have a usecase for using or not using bfc allocator per device?

Thanks @annarev for the PR. SimpleAllocator can be something straightforward calling into AllocateRaw and that would be fine for us. We had brief discussion of this during the design phase. To keep it simple we can leverage SubAllocator currently. After the pluggable device implementation merges we can use the use_bfc_allocator flag to pass this information in device_process_state..

Yep that makes sense. I misread it back then as custom allocator wrapper itself being something customized by the plugin.

There are few places in pluggable_device_process_state like AllocatorParts will require changes to accomodate the SimpleAllocator design, currently it assumes the Device memory allocator is BFC. I can upload a PR which I have tested locally works with our design. Please let me know.

Sounds good, you can upload the PR and we can comment on it if anything needs changing.

@annarev Thanks for the PR!
Do you mean the PluggableDeviceSimpleAllocator will directly call the stream_exec_->AllocateArray(), which calls SP_StreamExecutor::allocate(const SP_Device* device, uint64_t size, int64_t memory_space,SP_DeviceMemoryBase* mem ), and plugin authors decides whether allocate is build on top of a custom allocator? if it is, I think it is better to add an field such as void* custom_allocator in SP_Device? since the allocator should be per-device? Thanks

@jzhoulon Sounds about right. use_bfc_allocator will decide whether to use a simple allocator or a BFC allocator. So, a plugin that wants to use their own custom allocation strategy and doesn't want to wrap with BFC, can set this to true. Do you mean add use_bfc_allocator parameter per device? I was just thinking having one setting for all device types is easier to pass through on TF end. Do we have a usecase for using or not using bfc allocator per device?

No, I mean an opaque handle (void* custom_allocator) in SP_Device. See the following code example:
void create_device (SP_Platform platform, SE_CreateDeviceParams params, status) {
 params->custom_allocator = new CustomAllocator()
}

void allocate(SP_Device device, unit64_t size, ...) {
  CustomAllocator* allocator = static_cast<CustomAllocator*>(device->custom_allocate);
  allocator->allocate_raw(size, ...)
}

@annarev Thanks for the PR!
Do you mean the PluggableDeviceSimpleAllocator will directly call the stream_exec_->AllocateArray(), which calls SP_StreamExecutor::allocate(const SP_Device* device, uint64_t size, int64_t memory_space,SP_DeviceMemoryBase* mem ), and plugin authors decides whether allocate is build on top of a custom allocator? if it is, I think it is better to add an field such as void* custom_allocator in SP_Device? since the allocator should be per-device? Thanks

@jzhoulon Sounds about right. use_bfc_allocator will decide whether to use a simple allocator or a BFC allocator. So, a plugin that wants to use their own custom allocation strategy and doesn't want to wrap with BFC, can set this to true. Do you mean add use_bfc_allocator parameter per device? I was just thinking having one setting for all device types is easier to pass through on TF end. Do we have a usecase for using or not using bfc allocator per device?

No, I mean an opaque handle (void* custom_allocator) in SP_Device. See the following code example:
void create_device (SP_Platform platform, SE_CreateDeviceParams params, status) {
 params->custom_allocator = new CustomAllocator()
}

void allocate(SP_Device device, unit64_t size, ...) {
  CustomAllocator* allocator = static_cast<CustomAllocator*>(device->custom_allocate);
  allocator->allocate_raw(size, ...)
}

I think setting a custom allocator makes things more complicated. I think we should do it only if we think of a usecase that cannot be covered by just implementing allocate/deallocate in the StreamExecutor API: https://cs.opensource.google/tensorflow/tensorflow/+/master:tensorflow/c/experimental/stream_executor/stream_executor.h;l=245.

I think setting a custom allocator makes things more complicated. I think we should do it only if we think of a usecase that cannot be covered by just implementing allocate/deallocate in the StreamExecutor API: https://cs.opensource.google/tensorflow/tensorflow/+/master:tensorflow/c/experimental/stream_executor/stream_executor.h;l=245.

Agreed, I can queue up the PR after #45784 merges.

Agreed, I can queue up the PR after #45784 merges.

That would be awesome. Thank you very much, Kulin!


bounding box of polygon covers whole image and blocks more polygons from being added

Describe the bug
I'm trying to digitize clouds which have erratic boundaries and can cover a whole scene. I can't digitize the attached example with a single polygon since the polygon self intersects so I'm trying to do it in two polygons. But I can't digitize the cloud shadows in the right-middle of the image because the cloud bounding boxes are blocking and when I click with the polygon tool it just adds a shadow tag to the cloud objects I've digitized. I've attached the problem image.

To Reproduce
Steps to reproduce the behavior:

Go to polygon tool
draw a polygon that covers most of the image from top to bottom.
assign it a tag
try to digitize another polygon that falls within the bounding box but no the other polygon. the bounding box blocks you from drawing another polygon.

Expected behavior
No blocking
Screenshots
If applicable, add screenshots to help explain your problem.
Desktop (please complete the following information):

OS: Ubuntu 18.04
VOTT 2.1 for linux

Additional context
Add any other context about the problem here.

Hi Ryan, can you check if "locking" regions works? Ctrl + L. It should allow drawing new regions on top of the existing ones, at least that feature was implemented in the editor component, I'm not sure if it is already exposed in the VoTT interface.

It doesn't seem to do anything, and the lock symbol near the symbols to search for and create and delete tags doesn't seem to do anything when I click it


add try except to listen

The issue is that if the connection is disrupted (due to a Redis restart or temporary connectivity issues), the Taskiq worker crashes, interrupting all ongoing handlers. For example, any long-running tasks will be interrupted and will not complete their work.

Can you please describe when this feature is required, because taskiq worker automatically restarts if anything goes wrong?

Sure, I was going to open an issue.
The issue is that if the connection is disrupted (due to a Redis restart or temporary connectivity issues), the Taskiq worker crashes, interrupting all ongoing handlers. For example, any long-running tasks will be interrupted and will not complete their work.

@s3rius

@s3rius did you have a chance to look at it?

Please rebase onto main branch and we will merge it.


fix: push napi image using a simple command

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  Lines      25159   25159
  Branches    4529    4529
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List of icon names?

Seems like youtube-play from Font Awesome won't work. Frustrating because there's no documentation on how the icon names are getting messed with.

Sorry, was having a bad day :(

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"md-restaurant","md-restaurant-outline","md-restaurant-sharp","md-return-down-back","md-return-down-back-outline","md-return-down-back-sharp","md-return-down-forward","md-return-down-forward-outline","md-return-down-forward-sharp","md-return-up-back","md-return-up-back-outline","md-return-up-back-sharp","md-return-up-forward","md-return-up-forward-outline","md-return-up-forward-sharp","md-ribbon","md-ribbon-outline","md-ribbon-sharp","md-rocket","md-rocket-outline","md-rocket-sharp","md-rose","md-rose-outline","md-rose-sharp","md-sad","md-sad-outline","md-sad-sharp","md-save","md-save-outline","md-save-sharp","md-scan","md-scan-circle","md-scan-circle-outline","md-scan-circle-sharp","md-scan-outline","md-scan-sharp","md-school","md-school-outline","md-school-sharp","md-search","md-search-circle","md-search-circle-outline","md-search-circle-sharp","md-search-outline","md-search-sharp","md-send","md-send-outline","md-send-sharp","md-server","md-server-outline","md-server-sharp","md-settings","md-settings-outline","md-settings-sharp","md-shapes","md-shapes-outline","md-shapes-sharp","md-share","md-share-outline","md-share-sharp","md-share-social","md-share-social-outline","md-share-social-sharp","md-shield","md-shield-checkmark","md-shield-checkmark-outline","md-shield-checkmark-sharp","md-shield-outline","md-shield-sharp","md-shirt","md-shirt-outline","md-shirt-sharp","md-shuffle","md-shuffle-outline","md-shuffle-sharp","md-skull","md-skull-outline","md-skull-sharp","md-snow","md-snow-outline","md-snow-sharp","md-speedometer","md-speedometer-outline","md-speedometer-sharp","md-square","md-square-outline","md-square-sharp","md-star","md-star-half","md-star-half-outline","md-star-half-sharp","md-star-outline","md-star-sharp","md-stats-chart","md-stats-chart-outline","md-stats-chart-sharp","md-stop","md-stop-circle","md-stop-circle-outline","md-stop-circle-sharp","md-stop-outline","md-stop-sharp","md-stopwatch","md-stopwatch-outline","md-stopwatch-sharp","md-subway","md-subway-outline","md-subway-sharp","md-sunny","md-sunny-outline","md-sunny-sharp","md-swap-horizontal","md-swap-horizontal-outline","md-swap-horizontal-sharp","md-swap-vertical","md-swap-vertical-outline","md-swap-vertical-sharp","md-sync","md-sync-circle","md-sync-circle-outline","md-sync-circle-sharp","md-sync-outline","md-sync-sharp","md-tablet-landscape","md-tablet-landscape-outline","md-tablet-landscape-sharp","md-tablet-portrait","md-tablet-portrait-outline","md-tablet-portrait-sharp","md-tennisball","md-tennisball-outline","md-tennisball-sharp","md-terminal","md-terminal-outline","md-terminal-sharp","md-text","md-text-outline","md-text-sharp","md-thermometer","md-thermometer-outline","md-thermometer-sharp","md-thumbs-down","md-thumbs-down-outline","md-thumbs-down-sharp","md-thumbs-up","md-thumbs-up-outline","md-thumbs-up-sharp","md-thunderstorm","md-thunderstorm-outline","md-thunderstorm-sharp","md-time","md-time-outline","md-time-sharp","md-timer","md-timer-outline","md-timer-sharp","md-today","md-today-outline","md-today-sharp","md-toggle","md-toggle-outline","md-toggle-sharp","md-trail-sign","md-trail-sign-outline","md-trail-sign-sharp","md-train","md-train-outline","md-train-sharp","md-transgender","md-transgender-outline","md-transgender-sharp","md-trash","md-trash-bin","md-trash-bin-outline","md-trash-bin-sharp","md-trash-outline","md-trash-sharp","md-trending-down","md-trending-down-outline","md-trending-down-sharp","md-trending-up","md-trending-up-outline","md-trending-up-sharp","md-triangle","md-triangle-outline","md-triangle-sharp","md-trophy","md-trophy-outline","md-trophy-sharp","md-tv","md-tv-outline","md-tv-sharp","md-umbrella","md-umbrella-outline","md-umbrella-sharp","md-videocam","md-videocam-outline","md-videocam-sharp","md-volume-high","md-volume-high-outline","md-volume-high-sharp","md-volume-low","md-volume-low-outline","md-volume-low-sharp","md-volume-medium","md-volume-medium-outline","md-volume-medium-sharp","md-volume-mute","md-volume-mute-outline","md-volume-mute-sharp","md-volume-off","md-volume-off-outline","md-volume-off-sharp","md-walk","md-walk-outline","md-walk-sharp","md-wallet","md-wallet-outline","md-wallet-sharp","md-warning","md-warning-outline","md-warning-sharp","md-watch","md-watch-outline","md-watch-sharp","md-water","md-water-outline","md-water-sharp","md-wifi","md-wifi-outline","md-wifi-sharp","md-wine","md-wine-outline","md-wine-sharp","md-woman","md-woman-outline","md-woman-sharp"]

["add","add-circle","add-circle-outline","add-circle-sharp","add-outline","add-sharp","airplane","airplane-outline","airplane-sharp","alarm","alarm-outline","alarm-sharp","albums","albums-outline","albums-sharp","alert","alert-circle","alert-circle-outline","alert-circle-sharp","alert-outline","alert-sharp","american-football","american-football-outline","american-football-sharp","analytics","analytics-outline","analytics-sharp","aperture","aperture-outline","aperture-sharp","apps","apps-outline","apps-sharp","archive","archive-outline","archive-sharp","arrow-back","arrow-back-circle","arrow-back-circle-outline","arrow-back-circle-sharp","arrow-back-outline","arrow-back-sharp","arrow-down","arrow-down-circle","arrow-down-circle-outline","arrow-down-circle-sharp","arrow-down-outline","arrow-down-sharp","arrow-forward","arrow-forward-circle","arrow-forward-circle-outline","arrow-forward-circle-sharp","arrow-forward-outline","arrow-forward-sharp","arrow-redo","arrow-redo-circle","arrow-redo-circle-outline","arrow-redo-circle-sharp","arrow-redo-outline","arrow-redo-sharp","arrow-undo","ar

which this list belong to? MaterialIcon/ FontAwesome?? THanks


Brew using "master" branch when using --HEAD, despite that not being default branch

brew config output
HOMEBREW_VERSION: 3.5.7-2-g4baff3e
ORIGIN: https://github.com/Homebrew/brew
HEAD: 4baff3eec111af70599c3fb517576d87ca74e9a2
Last commit: 11 hours ago
Core tap ORIGIN: https://github.com/Homebrew/homebrew-core
Core tap HEAD: e0aeb1e4965edde4ceaafd13ecb868e427af434e
Core tap last commit: 2 hours ago
Core tap branch: master
HOMEBREW_PREFIX: /opt/homebrew
HOMEBREW_CASK_OPTS: []
HOMEBREW_DEVELOPER: set
HOMEBREW_EDITOR: vim
HOMEBREW_GITHUB_API_TOKEN: set
HOMEBREW_MAKE_JOBS: 10
Homebrew Ruby: 2.6.8 => /System/Library/Frameworks/Ruby.framework/Versions/2.6/usr/bin/ruby
CPU: 10-core 64-bit arm_firestorm_icestorm
Clang: 13.1.6 build 1316
Git: 2.37.1 => /opt/homebrew/bin/git
Curl: 7.79.1 => /usr/bin/curl
macOS: 12.5-arm64
CLT: <IP_ADDRESS>.1.1651278267
Xcode: 13.4.1
Rosetta 2: false

brew doctor output
Your system is ready to brew.

Verification

[X] I ran brew update and am still able to reproduce my issue.
[X] I have resolved all warnings from brew doctor and that did not fix my problem.

What were you trying to do (and why)?
Attempting to install a formula from my personal tap at --HEAD, in order to test unreleased commits.

You can amend this by altering your /Users/cblecker/.bash_profile file.
==> Caveats
Bash completion has been installed to:
/opt/homebrew/etc/bash_completion.d
==> Summary
🍺  /opt/homebrew/Cellar/ocm/HEAD-9f3afbe: 7 files, 11.6MB, built in 11 seconds
==> Running brew cleanup ocm...
Disable this behaviour by setting HOMEBREW_NO_INSTALL_CLEANUP.
Hide these hints with HOMEBREW_NO_ENV_HINTS (see man brew).
What did you expect to happen?
That brew would choose the branch named "main" not "master", which is the default branch in github (https://github.com/openshift-online/ocm-cli/branches)
Step-by-step reproduction instructions (by running brew commands)
1. Ensure cache doesn't have cached git pull: `rm -rf $(brew --cache)/ocm--git`
2. Install from tap: `brew install cblecker/tap/ocm --HEAD`

You can do head "https://github.com/openshift/oc.git", branch: "main"

The branch needs to be specified in the formula. git clone knows how to fetch the default branch, but updating a cached repository does not, so we assume the branch is master unless specified.

@carlocab This is on a fresh pull, no cache

There's also already a function to query the default branch, which I've confirmed that git command successfully returns "main"

Sure. Let me put what I said earlier differently. Specifying the default branch, when the default branch is not called master, is required in the formula. Not specifying the default branch at all is deprecated, so you will likely eventually need to specify it even if it the default branch is master.
We'll review a PR that allows brew to work out the default branch without having to specify it in the PR.

Thanks, that makes sense!


Add Dackup

Tools/Dackup - https://github.com/huobazi/dackup
Dackup is a .NET Core based backup tool for your files and database to Cloud.

Sorry, but the community hasn't rallied behind this project to get it included. Thanks for your contribution.