Add vue-pano

vue-pano is a vue based component for displaying interactive panorama images

@kazupon
Of course it has English doc. Jut forgot to link that in the home page.
https://github.com/ChiChou/vue-pano/blob/master/README.en.md

Thanks!
However, we hope it's the default dry to English is global standard.

All done.

thanks!


low mAP on voc2007

I run the master branch with ptyroch0.4.0， but got low mAP.  I used pretrained_model: vgg16_caffe.pth.
Could someone give me some advice on hyperparameters?
CUDA_VISIBLE_DEVICES=0 python trainval_net.py --dataset pascal_voc --net vgg16 --bs 1 --nw 1 --lr 0.0001 --lr_decay_step 5 --cuda
python test_net.py --dataset pascal_voc --net vgg16 --cuda
AP for aeroplane = 0.3451
AP for bicycle = 0.3566
AP for bird = 0.3209
AP for boat = 0.2109
AP for bottle = 0.1610
AP for bus = 0.3453
AP for car = 0.5734
AP for cat = 0.4840
AP for chair = 0.2318
AP for cow = 0.2903
AP for diningtable = 0.2457
AP for dog = 0.3679
AP for horse = 0.5015
AP for motorbike = 0.3815
AP for person = 0.5461
AP for pottedplant = 0.1773
AP for sheep = 0.3042
AP for sofa = 0.2995
AP for train = 0.4304
AP for tvmonitor = 0.3754
Mean AP = 0.3474

I know how， just because I ignored to set checkepoch.
Run:
python test_net.py --dataset pascal_voc --net vgg16 --checksession 1 --checkepoch 20 --checkpoint 10021  --cuda
Then I got right mAP.

can you close this issue?


ZCPU Editor highlighter doesn't recognize character set [.@] correctly

Character set [.@] is tokenized as part of token IDENT (@lua/wire/client/hlzasm/hc_tokenizer.lua:310).
However, the editor's highlighter isn't aware of it (@lua/wire/client/texteditor.lua:3595) and attempts to highlight them as an opcode, register or keyword before falling back to normal highlighting.
Example of highlighting error:

Ignore error in validator, it's part of bug #817. The example code is completely valid.

The highlighter also fails to highlight hexadecimal numbers.
Relevant code: lua/wire/client/texteditor.lua:3591 and lua/wire/client/hlzasm/hc_tokenizer.lua:386


Need a better solution for the sip recipe

While #15 supports the most recent version 4.19.18, note that since PyQt 5.11 a private copy of sip needs to be built: https://www.riverbankcomputing.com/static/Docs/PyQt5/installation.html#downloading-sip
that is, for the purpose of supporting PyQt5, the flag --sip-module PyQt5.sip needs to be used when building sip, otherwise there would be a ModuleNotFoundError for PyQt5.sip. But, adding that flag also means import sip no longer works.
Currently, #15 does not have that flag, so a PyQt 5.12 recipe (corresponding to recently merged PR conda-forge/qt-feedstock#110) can not depend on conda-forge's sip anymore. Any suggestion?
This is for resolving conda-forge/pyqt-feedstock#58.

ref: https://www.riverbankcomputing.com/pipermail/pyqt/2018-October/041003.html

No up to date package should be using a top-level sip module.

See source. I guess this eliminates the majority need of having a standalone sip package except for backward compatibility...


invalid_arguments response when deleting messages

Problem:
Channel messages are not being deleted when the script is performed. Results in invalid_arguments error.
Solution:
Slack chat.delete usually requires argument as_user=True
so add the command line argument: --as_user

@sgratzl when I try with --as_user, the error changes from invalid_arguments to cant_delete_message, but it still errors.
I've tried installing the development version, and it still fails.
When run without --perform it announces 2558 messages will be deleted, so I assume the expected behaviour would be to delete them, but as said, it still fails with --as_user


Center Floating Label for Highlighted TextField

What I want to implement:

I want to implement a textfeild whose floating label is horizontally centered.
What I am able to implement:

my code:
TextField(
                textAlign: TextAlign.center,
                controller: emailController,
                decoration: InputDecoration(
                  labelText: "Email ID",
                  labelStyle: TextStyle(
                    color: Colors.grey,
                  ),
                  border: OutlineInputBorder(),
                  focusedBorder: OutlineInputBorder(),
                ),
              ),

Can't find a way to center the floating label. Any help?

Just Go in TextField.dart and make the required changes

Can we make label float upward even if it is not having any text and focus on it

Is there a solution to this yet? running into a similar issue. I simply want to make sure the floating label does not go outside the textfield border.

Any solution to this? 1 year later, no news?

That would be a great extension for the textfield! I'm waiting for it as well.

I need it too. Or at least, move the label a little bit to the right as it doesn't look good with the border radius I'm using.

I'd like to be able to do a label that is always fixed above the input - no float animation. All options seem to either destroy it, or have it move up to the top on focus. Any idea?
like this, on load, blur, focus, error - everything

Hello everyone! I need this solution too! But I have not fund this yet.
So i changed my code back to  textAlign: TextAlign.start and added this to InputDecoration of TextFormField (it should work in TextForm either) :
decoration: InputDecoration(
        isDense: true,
        contentPadding: EdgeInsets.symmetric(horizontal: 30),
      )

so label and text moved closer to center.
I know that it looks not so right, but I have not found a better decision.

I'd like to be able to do a label that is always fixed above the input - no float animation. All options seem to either destroy it, or have it move up to the top on focus. Any idea?
like this, on load, blur, focus, error - everything

You could use a TextFormFied like so:
TextFormField(
decoration: InputDecoration(floatingLabelBehavior:FloatingLabelBehavior.always),
),

I can look at adding this
@raveesh-me (and anyone interested) please chime in. If you were able to center the floating label, would it
a) always be centered?
b) only be centered when it was floating

I found this solution online :
try this --
FocusNode focusNode = FocusNode();
bool isFocused = false;
@override
void initState() {
focusNode.addListener(_onFocusChange);
super.initState();
}
void _onFocusChange() {
setState(() => isFocused = !isFocused);
}
here's your widget
            Stack(
              children: <Widget>[
                Container(
                  padding: const EdgeInsets.only(top: 10),
                  child: TextFormField(
                    focusNode: focusNode,
                    decoration: InputDecoration(
                      border: OutlineInputBorder(
                          borderRadius: BorderRadius.circular(5)),
                    ),
                  ),
                ),
                Align(
                  alignment: Alignment.topCenter,
                  child: Container(
                    color: Colors.white,
                    child: Padding(
                      padding: const EdgeInsets.symmetric(horizontal: 8.0),
                      child: Text(
                        'Address',
                        style:
                        isFocused ? TextStyle(color: Colors.blue[800]) : null,
                      ),
                    ),
                  ),
                ),
              ],
            ),

Someone found the solution?

Please take a look at https://github.com/flutter/flutter/pull/90157 and its design doc https://docs.google.com/document/d/1sxP91vZiY7McmSMmzrboklIJWQ_6pSS9MBSCrQq016M/edit?pli=1# if you're interested in this feature!

I need it too. Or at least, move the label a little bit to the right as it doesn't look good with the border radius I'm using.

@alejo9719 You can fix that by adding an appropriate InputDecoration.contentPadding, so that the start of the label and the text align with the right end of the border curve.


y-axis float precision is huge when y-axis defined

This may be a known issue, but not only is float_precision not usable when yaxis is defined, but it seems to set to 15 digits when enabled - which makes the y-axis labels overwhelm the chart.
Even if float_precision support isn't added, it'd be good to set the default precision to something more reasonable, like the default of "1"
Version of the card
Version: 1.9.0
To Reproduce
This is the configuration I used:
type: 'custom:apexcharts-card'
series:
  - entity: sensor.upstairs_running
    color: red
    group_by:
      func: max
      duration: 30m
    yaxis_id: left
  - entity: sensor.openweathermap_temperature
    color: yellow
    group_by:
      func: avg
      duration: 30m
    yaxis_id: right
graph_span: 14d
yaxis:
  - id: left
    apex_config:
      tickAmount: 4
  - id: right
    opposite: true
    apex_config:
      tickAmount: 4

Screenshots

You can override it yourself for now using the apex_config in yaxis.
But yeah, that's something I forgot and will probably deprecate the way it is currently done.

Btw the parameter is decimalsInFloat: xx. Put it under tickAmount

Works great, thanks.

Don't close bot :)


[BUG] vSphere network selection fails due to ambiguity

Rancher Server Setup

Rancher version: v2.8.0
Installation option (Docker install/Helm Chart): helm

If Helm Chart, Kubernetes Cluster and version (RKE1, RKE2, k3s, EKS, etc): helm install rancher rancher-latest/rancher

Proxy/Cert Details:

Information about the Cluster

Kubernetes version: v1.27.9+k3s1
Cluster Type (Local/Downstream): local

If downstream, what type of cluster? (Custom/Imported or specify provider for Hosted/Infrastructure Provider):

User Information

What is the role of the user logged in? (Admin/Cluster Owner/Cluster Member/Project Owner/Project Member/Custom)

If custom, define the set of permissions: Admin

Describe the bug
network selector when using vSphere provider can't distinguish between the two networks /ADVLAB/network/Advlab and /ADVLAB/network/Cluster01_DSwitch/Advlab
To Reproduce
Create two networks with the same name - one in a vswitch one outside it.
Result
Drop-down resolves only the shorter path no matter which of the units is selected
Expected Result
networks should be distinguishable in drop-down
Screenshots

Additional context
mentioned in forums: https://forums.rancher.com/t/trouble-selecting-networks-in-vsphere/

🦗

We're having a really similar situation here.
Any news?
Thanks


Hama RGB E14 176549 Device did not appear with the intermediate firmware

Hey *,
I tried to flash these Hama RGB bulbs but couldn't.
The script output looks that way:
pi@raspberrypi:~/tuya-convert $ sudo ./start_flash.sh
Starting AP in a screen
Stopping any apache web server
Starting web server in a screen
Starting Mosquitto in a screen
Starting PSK frontend in a screen
======================================================
IMPORTANT

Connect any other device (a smartphone or something) to the WIFI vtrust-flash
This step is IMPORTANT otherwise the smartconfig will not work!
Put your IoT device in autoconfig/smartconfig/pairing mode (LED will blink fast). This is usually done by pressing and holding the primary button of the device
Press ENTER to continue

======================================================
Starting smart config pairing procedure
Waiting for the device to install the intermediate firmware
Put device in EZ config mode (blinking fast)
Sending SSID                  vtrust-flash
Sending wifiPassword
Sending token                 00000000
Sending secret                0101
................
SmartConfig complete.
Resending SmartConfig Packets
....../start_flash.sh: Zeile 133:  3667 Beendet                 ./smartconfig/main.py
...........................................................................................................
Device did not appear with the intermediate firmware
Check the *.log files in the scripts folder
Do you want to try flashing another device? [y/N] n
Exiting...
I did the same one a switch KKmoon WiFi Smart Lichtschalter DIY Breaker Modul Smart Life/APP  and it worked, got SONOFF working there.
smarthack-wifi.log
smarthack-mqtt.log
smarthack-psk.log
smarthack-web.log
Maybe one of you can help me.
Thanks in advance
Greets

Might be related to #354

@kueblc: Thanks a lot for your fast reply. I will try to change the things - in fake-registration-server.py - as proposed. I will report soon if this was the problem.

Might be related to #354

Hey "kueblc". The change in the fake-registration-server.py worked. Now I do have a "Sonoff/Tasmota" device. Once again thanks!!!

You're welcome, I'm glad it worked out!


Fix KeyErrors for attributes not available in model_kwargs

These are fixes for issues caught while executing the transformers tests. The issues are captured in https://github.com/huggingface/optimum-habana/issues/445 and https://github.com/huggingface/optimum-habana/issues/446
TCs impacted ( listed for t5 but these are common TCs impacting  all language models)
==============================================================
FAILED test_modeling_t5.py::T5ModelTest::test_beam_search_generate - KeyError: 'limit_hpu_graphs'
FAILED test_modeling_t5.py::T5ModelTest::test_beam_search_generate_dict_output - KeyError: 'limit_hpu_graphs'
FAILED test_modeling_t5.py::T5ModelTest::test_beam_search_generate_dict_outputs_use_cache - KeyError: 'limit_hpu_graphs'
FAILED test_modeling_t5.py::T5ModelTest::test_constrained_beam_search_generate - KeyError: 'limit_hpu_graphs'
FAILED test_modeling_t5.py::T5ModelTest::test_constrained_beam_search_generate_dict_output - KeyError: 'limit_hpu_graphs'
FAILED test_modeling_t5.py::T5ModelTest::test_greedy_generate - KeyError: 'bucket_size'
FAILED test_modeling_t5.py::T5ModelTest::test_greedy_generate_dict_outputs - KeyError: 'bucket_size'
FAILED test_modeling_t5.py::T5ModelTest::test_greedy_generate_dict_outputs_use_cache - KeyError: 'bucket_size'
FAILED test_modeling_t5.py::T5ModelTest::test_sample_generate - KeyError: 'limit_hpu_graphs'
FAILED test_modeling_t5.py::T5ModelTest::test_sample_generate_dict_output - KeyError: 'limit_hpu_graphs'
@regisss  : Please help with the review.

Status After the fixes :
 (conda_qnpu1) (anneog_fix_bucket_size) anneog@anneog-vm-u20:t5 $ python -m pytest -vs test_modeling_t5.py
===================================================================================================== test session starts ======================================================================================================
platform linux -- Python 3.8.18, pytest-7.4.2, pluggy-1.3.0 -- /home/anneog/anaconda3/envs/conda_qnpu1/bin/python
cachedir: .pytest_cache
hypothesis profile 'default' -> database=DirectoryBasedExampleDatabase('/home/anneog/github/ankurneog/optimum-habana/tests/transformers/tests/models/t5/.hypothesis/examples')
metadata: {'Python': '3.8.18', 'Platform': 'Linux-5.4.0-163-generic-x86_64-with-glibc2.17', 'Packages': {'pytest': '7.4.2', 'pluggy': '1.3.0'}, 'Plugins': {'hypothesis': '6.87.1', 'xdist': '3.3.1', 'metadata': '3.0.0', 'html': '4.0.2', 'timeout': '2.1.0', 'forked': '1.6.0', 'random-order': '1.0.2'}}
Test order randomisation NOT enabled. Enable with --random-order or --random-order-bucket=<bucket_type>
rootdir: /home/anneog/github/ankurneog/optimum-habana
configfile: setup.cfg
plugins: hypothesis-6.87.1, xdist-3.3.1, metadata-3.0.0, html-4.0.2, timeout-2.1.0, forked-1.6.0, random-order-1.0.2
collecting ... [WARNING|utils.py:179] 2023-10-18 08:28:06,329 >> optimum-habana v1.8.0.dev0 has been validated for SynapseAI v1.12.0 but habana-frameworks v<IP_ADDRESS> was found, this could lead to undefined behavior!
[WARNING|utils.py:196] 2023-10-18 08:28:06,349 >> Could not run hl-smi, please follow the installation guide: https://docs.habana.ai/en/latest/Installation_Guide/index.html.
collected 152 items
test_modeling_t5.py::T5ModelTest::test_assisted_decoding_matches_greedy_search SKIPPED (test is slow)
test_modeling_t5.py::T5ModelTest::test_assisted_decoding_sample ============================= HABANA PT BRIDGE CONFIGURATION ===========================
PT_HPU_LAZY_MODE = 1
PT_RECIPE_CACHE_PATH =
PT_CACHE_FOLDER_DELETE = 0
PT_HPU_RECIPE_CACHE_CONFIG =
PT_HPU_MAX_COMPOUND_OP_SIZE =<PHONE_NUMBER>854775807
PT_HPU_LAZY_ACC_PAR_MODE = 1
PT_HPU_ENABLE_REFINE_DYNAMIC_SHAPES = 0
---------------------------: System Configuration :---------------------------
Num CPU Cores : 8
CPU RAM       : 40852220 KB
PASSED
test_modeling_t5.py::T5ModelTest::test_attention_outputs PASSED
test_modeling_t5.py::T5ModelTest::test_beam_sample_generate SKIPPED (Beam search sampling is not supported by optimum-habana yet)
test_modeling_t5.py::T5ModelTest::test_beam_sample_generate_dict_output SKIPPED (Beam search sampling is not supported by optimum-habana yet)
test_modeling_t5.py::T5ModelTest::test_beam_search_generate PASSED
test_modeling_t5.py::T5ModelTest::test_beam_search_generate_dict_output PASSED
test_modeling_t5.py::T5ModelTest::test_beam_search_generate_dict_outputs_use_cache PASSED
test_modeling_t5.py::T5ModelTest::test_can_use_safetensors PASSED
test_modeling_t5.py::T5ModelTest::test_config PASSED
test_modeling_t5.py::T5ModelTest::test_config_and_model_silu_gated PASSED
test_modeling_t5.py::T5ModelTest::test_constrained_beam_search_generate PASSED
test_modeling_t5.py::T5ModelTest::test_constrained_beam_search_generate_dict_output PASSED
test_modeling_t5.py::T5ModelTest::test_contrastive_generate PASSED
test_modeling_t5.py::T5ModelTest::test_contrastive_generate_dict_outputs_use_cache PASSED
test_modeling_t5.py::T5ModelTest::test_contrastive_generate_low_memory PASSED
test_modeling_t5.py::T5ModelTest::test_correct_missing_keys Some weights of T5ForSequenceClassification were not initialized from the model checkpoint at /tmp/tmpyu2jq74p and are newly initialized: ['classification_head.out_proj.bias', 'classification_head.dense.weight', 'classification_head.out_proj.weight', 'classification_head.dense.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
PASSED
test_modeling_t5.py::T5ModelTest::test_cpu_offload SKIPPED (test requires CUDA)
test_modeling_t5.py::T5ModelTest::test_decoder_model_past PASSED
test_modeling_t5.py::T5ModelTest::test_decoder_model_past_with_3d_attn_mask PASSED
test_modeling_t5.py::T5ModelTest::test_decoder_model_past_with_attn_mask PASSED
test_modeling_t5.py::T5ModelTest::test_decoder_model_past_with_large_inputs PASSED
test_modeling_t5.py::T5ModelTest::test_determinism PASSED
test_modeling_t5.py::T5ModelTest::test_disk_offload SKIPPED (Does not work on the tiny model as we keep hitting edge cases.)
test_modeling_t5.py::T5ModelTest::test_encoder_decoder_shared_weights PASSED
test_modeling_t5.py::T5ModelTest::test_export_to_onnx SKIPPED (Test has a segmentation fault on torch 1.8.0)
test_modeling_t5.py::T5ModelTest::test_feed_forward_chunking PASSED
test_modeling_t5.py::T5ModelTest::test_forward_signature PASSED
test_modeling_t5.py::T5ModelTest::test_from_pretrained_no_checkpoint PASSED
test_modeling_t5.py::T5ModelTest::test_generate_from_inputs_embeds_decoder_only PASSED
test_modeling_t5.py::T5ModelTest::test_generate_with_head_masking SKIPPED (Segmentation fault is observed)
test_modeling_t5.py::T5ModelTest::test_generate_with_past_key_values SKIPPED (Skipped for Gaudi)
test_modeling_t5.py::T5ModelTest::test_generate_without_input_ids PASSED
test_modeling_t5.py::T5ModelTest::test_gradient_checkpointing_backward_compatibility PASSED
test_modeling_t5.py::T5ModelTest::test_gradient_checkpointing_enable_disable PASSED
test_modeling_t5.py::T5ModelTest::test_greedy_generate PASSED
test_modeling_t5.py::T5ModelTest::test_greedy_generate_dict_outputs PASSED
test_modeling_t5.py::T5ModelTest::test_greedy_generate_dict_outputs_use_cache PASSED
test_modeling_t5.py::T5ModelTest::test_group_beam_search_generate SKIPPED (Group beam search is not supported by optimum-habana)
test_modeling_t5.py::T5ModelTest::test_group_beam_search_generate_dict_output SKIPPED (Group beam search is not supported by optimum-habana)
test_modeling_t5.py::T5ModelTest::test_head_pruning PASSED
test_modeling_t5.py::T5ModelTest::test_head_pruning_integration PASSED
test_modeling_t5.py::T5ModelTest::test_head_pruning_save_load_from_config_init PASSED
test_modeling_t5.py::T5ModelTest::test_head_pruning_save_load_from_pretrained PASSED
test_modeling_t5.py::T5ModelTest::test_headmasking PASSED
test_modeling_t5.py::T5ModelTest::test_hidden_states_output PASSED
test_modeling_t5.py::T5ModelTest::test_initialization PASSED
test_modeling_t5.py::T5ModelTest::test_inputs_embeds PASSED
test_modeling_t5.py::T5ModelTest::test_left_padding_compatibility PASSED
test_modeling_t5.py::T5ModelTest::test_load_save_without_tied_weights PASSED
test_modeling_t5.py::T5ModelTest::test_load_with_mismatched_shapes Some weights of T5ForSequenceClassification were not initialized from the model checkpoint at /tmp/tmpeadng85e and are newly initialized because the shapes did not match:

classification_head.out_proj.weight: found shape torch.Size([2, 32]) in the checkpoint and torch.Size([42, 32]) in the model instantiated
classification_head.out_proj.bias: found shape torch.Size([2]) in the checkpoint and torch.Size([42]) in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Some weights of T5Model were not initialized from the model checkpoint at /tmp/tmpeadng85e and are newly initialized because the shapes did not match:
transformer.shared.weight: found shape torch.Size([99, 32]) in the checkpoint and torch.Size([10, 32]) in the model instantiated
transformer.encoder.embed_tokens.weight: found shape torch.Size([99, 32]) in the checkpoint and torch.Size([10, 32]) in the model instantiated
transformer.decoder.embed_tokens.weight: found shape torch.Size([99, 32]) in the checkpoint and torch.Size([10, 32]) in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
PASSED
test_modeling_t5.py::T5ModelTest::test_model PASSED
test_modeling_t5.py::T5ModelTest::test_model_common_attributes PASSED
test_modeling_t5.py::T5ModelTest::test_model_from_pretrained SKIPPED (test is slow)
test_modeling_t5.py::T5ModelTest::test_model_is_small PASSED
test_modeling_t5.py::T5ModelTest::test_model_main_input_name PASSED
test_modeling_t5.py::T5ModelTest::test_model_outputs_equivalence PASSED
test_modeling_t5.py::T5ModelTest::test_model_parallel_beam_search SKIPPED (test requires multiple GPUs)
test_modeling_t5.py::T5ModelTest::test_model_parallel_equal_results SKIPPED (test requires multiple GPUs)
test_modeling_t5.py::T5ModelTest::test_model_parallelism SKIPPED (test requires multiple GPUs)
test_modeling_t5.py::T5ModelTest::test_model_parallelization SKIPPED (test requires multiple GPUs)
test_modeling_t5.py::T5ModelTest::test_model_v1_1 PASSED
test_modeling_t5.py::T5ModelTest::test_model_weights_reload_no_missing_tied_weights Some weights of T5Model were not initialized from the model checkpoint at /tmp/tmpx_1cw9xh and are newly initialized: ['encoder.block.1.layer.0.SelfAttention.v.weight', 'encoder.block.0.layer.0.SelfAttention.q.weight', 'decoder.block.1.layer.1.layer_norm.weight', 'decoder.block.1.layer.2.DenseReluDense.wo.weight', 'encoder.final_layer_norm.weight', 'encoder.block.0.layer.1.DenseReluDense.wi.weight', 'decoder.block.1.layer.1.EncDecAttention.o.weight', 'decoder.block.0.layer.1.EncDecAttention.q.weight', 'decoder.block.0.layer.0.SelfAttention.k.weight', 'decoder.embed_tokens.weight', 'encoder.block.1.layer.0.layer_norm.weight', 'decoder.block.0.layer.2.DenseReluDense.wi.weight', 'decoder.block.1.layer.0.SelfAttention.k.weight', 'decoder.block.0.layer.0.layer_norm.weight', 'decoder.final_layer_norm.weight', 'decoder.block.1.layer.0.SelfAttention.q.weight', 'encoder.block.1.layer.0.SelfAttention.o.weight', 'decoder.block.1.layer.2.DenseReluDense.wi.weight', 'encoder.block.1.layer.0.SelfAttention.k.weight', 'decoder.block.0.layer.0.SelfAttention.o.weight', 'decoder.block.0.layer.1.EncDecAttention.v.weight', 'decoder.block.0.layer.1.EncDecAttention.o.weight', 'encoder.block.0.layer.0.SelfAttention.k.weight', 'decoder.block.1.layer.0.SelfAttention.v.weight', 'encoder.block.1.layer.1.DenseReluDense.wi.weight', 'decoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight', 'decoder.block.1.layer.0.SelfAttention.o.weight', 'encoder.block.1.layer.1.DenseReluDense.wo.weight', 'decoder.block.1.layer.1.EncDecAttention.q.weight', 'encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight', 'encoder.block.0.layer.1.DenseReluDense.wo.weight', 'decoder.block.0.layer.2.DenseReluDense.wo.weight', 'decoder.block.1.layer.2.layer_norm.weight', 'decoder.block.1.layer.0.layer_norm.weight', 'encoder.block.0.layer.0.layer_norm.weight', 'encoder.block.0.layer.1.layer_norm.weight', 'decoder.block.0.layer.1.layer_norm.weight', 'decoder.block.0.layer.2.layer_norm.weight', 'encoder.block.1.layer.1.layer_norm.weight', 'decoder.block.1.layer.1.EncDecAttention.k.weight', 'encoder.block.0.layer.0.SelfAttention.v.weight', 'decoder.block.0.layer.1.EncDecAttention.k.weight', 'shared.weight', 'encoder.embed_tokens.weight', 'encoder.block.0.layer.0.SelfAttention.o.weight', 'decoder.block.0.layer.0.SelfAttention.v.weight', 'decoder.block.1.layer.1.EncDecAttention.v.weight', 'decoder.block.0.layer.0.SelfAttention.q.weight', 'encoder.block.1.layer.0.SelfAttention.q.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Some weights of T5ForConditionalGeneration were not initialized from the model checkpoint at /tmp/tmpus62arwo and are newly initialized: ['encoder.block.1.layer.0.SelfAttention.v.weight', 'encoder.block.0.layer.0.SelfAttention.q.weight', 'decoder.block.1.layer.1.layer_norm.weight', 'decoder.block.1.layer.2.DenseReluDense.wo.weight', 'encoder.final_layer_norm.weight', 'encoder.block.0.layer.1.DenseReluDense.wi.weight', 'decoder.block.1.layer.1.EncDecAttention.o.weight', 'decoder.block.0.layer.1.EncDecAttention.q.weight', 'decoder.block.0.layer.0.SelfAttention.k.weight', 'decoder.embed_tokens.weight', 'encoder.block.1.layer.0.layer_norm.weight', 'decoder.block.0.layer.2.DenseReluDense.wi.weight', 'decoder.block.1.layer.0.SelfAttention.k.weight', 'decoder.block.0.layer.0.layer_norm.weight', 'lm_head.weight', 'decoder.final_layer_norm.weight', 'decoder.block.1.layer.0.SelfAttention.q.weight', 'encoder.block.1.layer.0.SelfAttention.o.weight', 'decoder.block.1.layer.2.DenseReluDense.wi.weight', 'encoder.block.1.layer.0.SelfAttention.k.weight', 'decoder.block.0.layer.0.SelfAttention.o.weight', 'decoder.block.0.layer.1.EncDecAttention.v.weight', 'decoder.block.0.layer.1.EncDecAttention.o.weight', 'encoder.block.0.layer.0.SelfAttention.k.weight', 'decoder.block.1.layer.0.SelfAttention.v.weight', 'encoder.block.1.layer.1.DenseReluDense.wi.weight', 'decoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight', 'decoder.block.1.layer.0.SelfAttention.o.weight', 'encoder.block.1.layer.1.DenseReluDense.wo.weight', 'decoder.block.1.layer.1.EncDecAttention.q.weight', 'encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight', 'encoder.block.0.layer.1.DenseReluDense.wo.weight', 'decoder.block.0.layer.2.DenseReluDense.wo.weight', 'decoder.block.1.layer.2.layer_norm.weight', 'decoder.block.1.layer.0.layer_norm.weight', 'encoder.block.0.layer.0.layer_norm.weight', 'encoder.block.0.layer.1.layer_norm.weight', 'decoder.block.0.layer.1.layer_norm.weight', 'decoder.block.0.layer.2.layer_norm.weight', 'encoder.block.1.layer.1.layer_norm.weight', 'decoder.block.1.layer.1.EncDecAttention.k.weight', 'encoder.block.0.layer.0.SelfAttention.v.weight', 'decoder.block.0.layer.1.EncDecAttention.k.weight', 'shared.weight', 'encoder.embed_tokens.weight', 'encoder.block.0.layer.0.SelfAttention.o.weight', 'decoder.block.0.layer.0.SelfAttention.v.weight', 'decoder.block.1.layer.1.EncDecAttention.v.weight', 'decoder.block.0.layer.0.SelfAttention.q.weight', 'encoder.block.1.layer.0.SelfAttention.q.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Some weights of T5ForSequenceClassification were not initialized from the model checkpoint at /tmp/tmp24kk_52d and are newly initialized: ['encoder.block.1.layer.0.SelfAttention.v.weight', 'encoder.block.0.layer.0.SelfAttention.q.weight', 'decoder.block.1.layer.1.layer_norm.weight', 'decoder.block.1.layer.2.DenseReluDense.wo.weight', 'encoder.final_layer_norm.weight', 'encoder.block.0.layer.1.DenseReluDense.wi.weight', 'decoder.block.1.layer.1.EncDecAttention.o.weight', 'decoder.block.0.layer.1.EncDecAttention.q.weight', 'decoder.block.0.layer.0.SelfAttention.k.weight', 'decoder.embed_tokens.weight', 'encoder.block.1.layer.0.layer_norm.weight', 'decoder.block.0.layer.2.DenseReluDense.wi.weight', 'decoder.block.1.layer.0.SelfAttention.k.weight', 'decoder.block.0.layer.0.layer_norm.weight', 'decoder.final_layer_norm.weight', 'decoder.block.1.layer.0.SelfAttention.q.weight', 'encoder.block.1.layer.0.SelfAttention.o.weight', 'decoder.block.1.layer.2.DenseReluDense.wi.weight', 'classification_head.dense.bias', 'encoder.block.1.layer.0.SelfAttention.k.weight', 'decoder.block.0.layer.0.SelfAttention.o.weight', 'decoder.block.0.layer.1.EncDecAttention.v.weight', 'classification_head.out_proj.bias', 'decoder.block.0.layer.1.EncDecAttention.o.weight', 'encoder.block.0.layer.0.SelfAttention.k.weight', 'decoder.block.1.layer.0.SelfAttention.v.weight', 'encoder.block.1.layer.1.DenseReluDense.wi.weight', 'decoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight', 'decoder.block.1.layer.0.SelfAttention.o.weight', 'encoder.block.1.layer.1.DenseReluDense.wo.weight', 'decoder.block.1.layer.1.EncDecAttention.q.weight', 'encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight', 'encoder.block.0.layer.1.DenseReluDense.wo.weight', 'decoder.block.0.layer.2.DenseReluDense.wo.weight', 'classification_head.out_proj.weight', 'decoder.block.1.layer.2.layer_norm.weight', 'decoder.block.1.layer.0.layer_norm.weight', 'encoder.block.0.layer.0.layer_norm.weight', 'encoder.block.0.layer.1.layer_norm.weight', 'decoder.block.0.layer.1.layer_norm.weight', 'decoder.block.0.layer.2.layer_norm.weight', 'encoder.block.1.layer.1.layer_norm.weight', 'decoder.block.1.layer.1.EncDecAttention.k.weight', 'encoder.block.0.layer.0.SelfAttention.v.weight', 'decoder.block.0.layer.1.EncDecAttention.k.weight', 'shared.weight', 'encoder.embed_tokens.weight', 'classification_head.dense.weight', 'encoder.block.0.layer.0.SelfAttention.o.weight', 'decoder.block.0.layer.0.SelfAttention.v.weight', 'decoder.block.1.layer.1.EncDecAttention.v.weight', 'decoder.block.0.layer.0.SelfAttention.q.weight', 'encoder.block.1.layer.0.SelfAttention.q.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Some weights of T5ForQuestionAnswering were not initialized from the model checkpoint at /tmp/tmp5lyes6p6 and are newly initialized: ['encoder.block.1.layer.0.SelfAttention.v.weight', 'encoder.block.0.layer.0.SelfAttention.q.weight', 'decoder.block.1.layer.1.layer_norm.weight', 'decoder.block.1.layer.2.DenseReluDense.wo.weight', 'encoder.final_layer_norm.weight', 'encoder.block.0.layer.1.DenseReluDense.wi.weight', 'decoder.block.1.layer.1.EncDecAttention.o.weight', 'decoder.block.0.layer.1.EncDecAttention.q.weight', 'decoder.block.0.layer.0.SelfAttention.k.weight', 'decoder.embed_tokens.weight', 'encoder.block.1.layer.0.layer_norm.weight', 'decoder.block.0.layer.2.DenseReluDense.wi.weight', 'decoder.block.1.layer.0.SelfAttention.k.weight', 'decoder.block.0.layer.0.layer_norm.weight', 'qa_outputs.bias', 'decoder.final_layer_norm.weight', 'decoder.block.1.layer.0.SelfAttention.q.weight', 'encoder.block.1.layer.0.SelfAttention.o.weight', 'decoder.block.1.layer.2.DenseReluDense.wi.weight', 'encoder.block.1.layer.0.SelfAttention.k.weight', 'decoder.block.0.layer.0.SelfAttention.o.weight', 'decoder.block.0.layer.1.EncDecAttention.v.weight', 'decoder.block.0.layer.1.EncDecAttention.o.weight', 'encoder.block.0.layer.0.SelfAttention.k.weight', 'decoder.block.1.layer.0.SelfAttention.v.weight', 'encoder.block.1.layer.1.DenseReluDense.wi.weight', 'decoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight', 'decoder.block.1.layer.0.SelfAttention.o.weight', 'encoder.block.1.layer.1.DenseReluDense.wo.weight', 'decoder.block.1.layer.1.EncDecAttention.q.weight', 'encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight', 'encoder.block.0.layer.1.DenseReluDense.wo.weight', 'decoder.block.0.layer.2.DenseReluDense.wo.weight', 'decoder.block.1.layer.2.layer_norm.weight', 'decoder.block.1.layer.0.layer_norm.weight', 'encoder.block.0.layer.0.layer_norm.weight', 'encoder.block.0.layer.1.layer_norm.weight', 'decoder.block.0.layer.1.layer_norm.weight', 'decoder.block.0.layer.2.layer_norm.weight', 'encoder.block.1.layer.1.layer_norm.weight', 'decoder.block.1.layer.1.EncDecAttention.k.weight', 'encoder.block.0.layer.0.SelfAttention.v.weight', 'decoder.block.0.layer.1.EncDecAttention.k.weight', 'shared.weight', 'encoder.embed_tokens.weight', 'qa_outputs.weight', 'encoder.block.0.layer.0.SelfAttention.o.weight', 'decoder.block.0.layer.0.SelfAttention.v.weight', 'decoder.block.1.layer.1.EncDecAttention.v.weight', 'decoder.block.0.layer.0.SelfAttention.q.weight', 'encoder.block.1.layer.0.SelfAttention.q.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
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test_modeling_t5.py::T5ModelTest::test_multi_gpu_data_parallel_forward SKIPPED (test requires multiple GPUs)
test_modeling_t5.py::T5ModelTest::test_past_key_values_format PASSED
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test_modeling_t5.py::T5ModelTest::test_resize_embeddings_untied PASSED
test_modeling_t5.py::T5ModelTest::test_resize_position_vector_embeddings PASSED
test_modeling_t5.py::T5ModelTest::test_resize_tokens_embeddings SKIPPED (Skipped for Gaudi)
test_modeling_t5.py::T5ModelTest::test_retain_grad_hidden_states_attentions PASSED
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test_modeling_t5.py::T5ModelTest::test_save_load PASSED
test_modeling_t5.py::T5ModelTest::test_save_load_fast_init_from_base Some weights of CopyClass were not initialized from the model checkpoint at /tmp/tmp_k74wbhw and are newly initialized: ['classification_head.out_proj.weight', 'classification_head.dense.weight', 'classification_head.dense.bias', 'classification_head.out_proj.bias', 'decoder.block.0.layer.2.layer_norm.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Some weights of CopyClass were not initialized from the model checkpoint at /tmp/tmp_k74wbhw and are newly initialized: ['classification_head.out_proj.weight', 'classification_head.dense.weight', 'classification_head.dense.bias', 'classification_head.out_proj.bias', 'decoder.block.0.layer.2.layer_norm.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Some weights of CopyClass were not initialized from the model checkpoint at /tmp/tmpectzs0s6 and are newly initialized: ['decoder.block.0.layer.1.layer_norm.weight', 'qa_outputs.bias', 'qa_outputs.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Some weights of CopyClass were not initialized from the model checkpoint at /tmp/tmpectzs0s6 and are newly initialized: ['decoder.block.0.layer.1.layer_norm.weight', 'qa_outputs.bias', 'qa_outputs.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
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test_modeling_t5.py::T5ModelTest::test_save_load_fast_init_to_base Some weights of CopyClass were not initialized from the model checkpoint at /tmp/tmpc7s_4xpb and are newly initialized: ['transformer.encoder.block.0.layer.0.SelfAttention.q.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Some weights of CopyClass were not initialized from the model checkpoint at /tmp/tmpc7s_4xpb and are newly initialized: ['transformer.encoder.block.0.layer.0.SelfAttention.q.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Some weights of CopyClass were not initialized from the model checkpoint at /tmp/tmp37rnccg_ and are newly initialized: ['decoder.block.0.layer.0.SelfAttention.o.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Some weights of CopyClass were not initialized from the model checkpoint at /tmp/tmp37rnccg_ and are newly initialized: ['decoder.block.0.layer.0.SelfAttention.o.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
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test_modeling_t5.py::T5ModelTest::test_torchscript_output_attentions SKIPPED (test is slow)
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test_modeling_t5.py::T5ModelTest::test_training SKIPPED (Skipped for Gaudi : TODO)
test_modeling_t5.py::T5ModelTest::test_training_gradient_checkpointing SKIPPED (Skipped for Gaudi : TODO)
test_modeling_t5.py::T5ModelTest::test_v1_1_resize_embeddings PASSED
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test_modeling_t5.py::T5EncoderOnlyModelTest::test_model_parallel_beam_search SKIPPED (test requires multiple GPUs)
test_modeling_t5.py::T5EncoderOnlyModelTest::test_model_parallel_equal_results SKIPPED (test requires multiple GPUs)
test_modeling_t5.py::T5EncoderOnlyModelTest::test_model_parallelism SKIPPED (test requires multiple GPUs)
test_modeling_t5.py::T5EncoderOnlyModelTest::test_model_parallelization SKIPPED (test requires multiple GPUs)
test_modeling_t5.py::T5EncoderOnlyModelTest::test_model_weights_reload_no_missing_tied_weights Some weights of T5EncoderModel were not initialized from the model checkpoint at /tmp/tmp7n8sg07g and are newly initialized: ['encoder.block.1.layer.1.DenseReluDense.wo.weight', 'encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight', 'encoder.block.1.layer.0.SelfAttention.v.weight', 'encoder.block.0.layer.1.DenseReluDense.wo.weight', 'encoder.final_layer_norm.weight', 'encoder.block.0.layer.1.DenseReluDense.wi.weight', 'encoder.block.1.layer.1.DenseReluDense.wi.weight', 'encoder.block.0.layer.0.layer_norm.weight', 'encoder.block.0.layer.1.layer_norm.weight', 'encoder.block.1.layer.1.layer_norm.weight', 'encoder.block.1.layer.0.layer_norm.weight', 'encoder.block.0.layer.0.SelfAttention.v.weight', 'shared.weight', 'encoder.block.1.layer.0.SelfAttention.q.weight', 'encoder.embed_tokens.weight', 'encoder.block.1.layer.0.SelfAttention.o.weight', 'encoder.block.0.layer.0.SelfAttention.o.weight', 'encoder.block.1.layer.0.SelfAttention.k.weight', 'encoder.block.0.layer.0.SelfAttention.k.weight', 'encoder.block.0.layer.0.SelfAttention.q.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
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test_modeling_t5.py::T5EncoderOnlyModelTest::test_save_load_fast_init_to_base Some weights of CopyClass were not initialized from the model checkpoint at /tmp/tmpgu9tx7sa and are newly initialized: ['decoder.block.1.layer.1.layer_norm.weight', 'decoder.block.1.layer.2.DenseReluDense.wo.weight', 'encoder.block.0.layer.1.DenseReluDense.wi.weight', 'decoder.block.1.layer.1.EncDecAttention.o.weight', 'decoder.block.0.layer.1.EncDecAttention.q.weight', 'decoder.block.0.layer.0.SelfAttention.k.weight', 'decoder.block.1.layer.0.SelfAttention.k.weight', 'decoder.block.0.layer.2.DenseReluDense.wi.weight', 'decoder.block.0.layer.0.layer_norm.weight', 'decoder.final_layer_norm.weight', 'decoder.block.1.layer.0.SelfAttention.q.weight', 'decoder.block.1.layer.2.DenseReluDense.wi.weight', 'decoder.block.0.layer.0.SelfAttention.o.weight', 'decoder.block.0.layer.1.EncDecAttention.v.weight', 'decoder.block.0.layer.1.EncDecAttention.o.weight', 'decoder.block.1.layer.0.SelfAttention.v.weight', 'decoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight', 'decoder.block.1.layer.0.SelfAttention.o.weight', 'decoder.block.1.layer.1.EncDecAttention.q.weight', 'decoder.block.0.layer.2.DenseReluDense.wo.weight', 'decoder.block.1.layer.2.layer_norm.weight', 'decoder.block.1.layer.0.layer_norm.weight', 'decoder.block.0.layer.1.layer_norm.weight', 'decoder.block.0.layer.2.layer_norm.weight', 'decoder.block.1.layer.1.EncDecAttention.k.weight', 'decoder.block.0.layer.1.EncDecAttention.k.weight', 'decoder.block.0.layer.0.SelfAttention.v.weight', 'decoder.block.1.layer.1.EncDecAttention.v.weight', 'decoder.block.0.layer.0.SelfAttention.q.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Some weights of CopyClass were not initialized from the model checkpoint at /tmp/tmpgu9tx7sa and are newly initialized: ['decoder.block.1.layer.1.layer_norm.weight', 'decoder.block.1.layer.2.DenseReluDense.wo.weight', 'encoder.block.0.layer.1.DenseReluDense.wi.weight', 'decoder.block.1.layer.1.EncDecAttention.o.weight', 'decoder.block.0.layer.1.EncDecAttention.q.weight', 'decoder.block.0.layer.0.SelfAttention.k.weight', 'decoder.block.1.layer.0.SelfAttention.k.weight', 'decoder.block.0.layer.2.DenseReluDense.wi.weight', 'decoder.block.0.layer.0.layer_norm.weight', 'decoder.final_layer_norm.weight', 'decoder.block.1.layer.0.SelfAttention.q.weight', 'decoder.block.1.layer.2.DenseReluDense.wi.weight', 'decoder.block.0.layer.0.SelfAttention.o.weight', 'decoder.block.0.layer.1.EncDecAttention.v.weight', 'decoder.block.0.layer.1.EncDecAttention.o.weight', 'decoder.block.1.layer.0.SelfAttention.v.weight', 'decoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight', 'decoder.block.1.layer.0.SelfAttention.o.weight', 'decoder.block.1.layer.1.EncDecAttention.q.weight', 'decoder.block.0.layer.2.DenseReluDense.wo.weight', 'decoder.block.1.layer.2.layer_norm.weight', 'decoder.block.1.layer.0.layer_norm.weight', 'decoder.block.0.layer.1.layer_norm.weight', 'decoder.block.0.layer.2.layer_norm.weight', 'decoder.block.1.layer.1.EncDecAttention.k.weight', 'decoder.block.0.layer.1.EncDecAttention.k.weight', 'decoder.block.0.layer.0.SelfAttention.v.weight', 'decoder.block.1.layer.1.EncDecAttention.v.weight', 'decoder.block.0.layer.0.SelfAttention.q.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
PASSED
test_modeling_t5.py::T5EncoderOnlyModelTest::test_save_load_keys_to_ignore_on_save PASSED
test_modeling_t5.py::T5EncoderOnlyModelTest::test_tie_model_weights PASSED
test_modeling_t5.py::T5EncoderOnlyModelTest::test_tied_weights_keys PASSED
test_modeling_t5.py::T5EncoderOnlyModelTest::test_torch_fx PASSED
test_modeling_t5.py::T5EncoderOnlyModelTest::test_torch_fx_output_loss PASSED
test_modeling_t5.py::T5EncoderOnlyModelTest::test_torchscript_output_attentions SKIPPED (test is slow)
test_modeling_t5.py::T5EncoderOnlyModelTest::test_torchscript_output_hidden_state SKIPPED (test is slow)
test_modeling_t5.py::T5EncoderOnlyModelTest::test_torchscript_simple SKIPPED (test is slow)
test_modeling_t5.py::T5EncoderOnlyModelTest::test_training SKIPPED (Skipped for Gaudi : TODO)
test_modeling_t5.py::T5EncoderOnlyModelTest::test_training_gradient_checkpointing SKIPPED (Skipped for Gaudi : TODO)
test_modeling_t5.py::T5ModelIntegrationTests::test_contrastive_search_t5 SKIPPED (test is slow)
test_modeling_t5.py::T5ModelIntegrationTests::test_small_byt5_integration_test SKIPPED (test is slow)
test_modeling_t5.py::T5ModelIntegrationTests::test_small_generation SKIPPED (test is slow)
test_modeling_t5.py::T5ModelIntegrationTests::test_small_integration_test SKIPPED (test is slow)
test_modeling_t5.py::T5ModelIntegrationTests::test_small_v1_1_integration_test SKIPPED (test is slow)
test_modeling_t5.py::T5ModelIntegrationTests::test_summarization SKIPPED (test is slow)
test_modeling_t5.py::T5ModelIntegrationTests::test_torch_quant SKIPPED (test is slow)
test_modeling_t5.py::T5ModelIntegrationTests::test_translation_en_to_de SKIPPED (test is slow)
test_modeling_t5.py::T5ModelIntegrationTests::test_translation_en_to_fr SKIPPED (test is slow)
test_modeling_t5.py::T5ModelIntegrationTests::test_translation_en_to_ro SKIPPED (test is slow)
test_modeling_t5.py::TestAsymmetricT5::test_defaulting_to_symmetry PASSED
test_modeling_t5.py::TestAsymmetricT5::test_small_decoder PASSED

======================================================================================= warnings summary =======================================================================================
../../test_modeling_common.py:2043
/home/anneog/github/ankurneog/optimum-habana/tests/transformers/tests/test_modeling_common.py:2043: PytestUnknownMarkWarning: Unknown pytest.mark.accelerate_tests - is this a typo?  You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html
@mark.accelerate_tests
../../test_modeling_common.py:2081
/home/anneog/github/ankurneog/optimum-habana/tests/transformers/tests/test_modeling_common.py:2081: PytestUnknownMarkWarning: Unknown pytest.mark.accelerate_tests - is this a typo?  You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html
@mark.accelerate_tests
../../test_modeling_common.py:2117
/home/anneog/github/ankurneog/optimum-habana/tests/transformers/tests/test_modeling_common.py:2117: PytestUnknownMarkWarning: Unknown pytest.mark.accelerate_tests - is this a typo?  You can register custom marks to avoid this warning - for details, see https://docs.pytest.org/en/stable/how-to/mark.html
@mark.accelerate_tests
tests/transformers/tests/models/t5/test_modeling_t5.py::T5ModelTest::test_beam_search_generate
tests/transformers/tests/models/t5/test_modeling_t5.py::T5ModelTest::test_beam_search_generate_dict_output
tests/transformers/tests/models/t5/test_modeling_t5.py::T5ModelTest::test_beam_search_generate_dict_outputs_use_cache
/home/anneog/github/ankurneog/optimum-habana/optimum/habana/transformers/generation/utils.py:1921: UserWarning: max_length is deprecated in this function, use stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length)) instead.
warnings.warn(
tests/transformers/tests/models/t5/test_modeling_t5.py::T5ModelTest::test_beam_search_generate
tests/transformers/tests/models/t5/test_modeling_t5.py::T5ModelTest::test_constrained_beam_search_generate
tests/transformers/tests/models/t5/test_modeling_t5.py::T5ModelTest::test_contrastive_generate_low_memory
tests/transformers/tests/models/t5/test_modeling_t5.py::T5ModelTest::test_sample_generate
/home/anneog/github/ankurneog/optimum-habana/optimum/habana/transformers/generation/utils.py:430: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )
warnings.warn(
tests/transformers/tests/models/t5/test_modeling_t5.py::T5ModelTest::test_constrained_beam_search_generate
tests/transformers/tests/models/t5/test_modeling_t5.py::T5ModelTest::test_constrained_beam_search_generate_dict_output
/home/anneog/github/ankurneog/optimum-habana/optimum/habana/transformers/generation/utils.py:2573: UserWarning: max_length is deprecated in this function, use stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length)) instead.
warnings.warn(
tests/transformers/tests/models/t5/test_modeling_t5.py::T5ModelTest::test_greedy_generate
tests/transformers/tests/models/t5/test_modeling_t5.py::T5ModelTest::test_greedy_generate_dict_outputs
tests/transformers/tests/models/t5/test_modeling_t5.py::T5ModelTest::test_greedy_generate_dict_outputs_use_cache
/home/anneog/github/ankurneog/optimum-habana/optimum/habana/transformers/generation/utils.py:1210: UserWarning: max_length is deprecated in this function, use stopping_criteria=StoppingCriteriaList([MaxLengthCriteria(max_length=max_length)]) instead.
warnings.warn(
tests/transformers/tests/models/t5/test_modeling_t5.py::T5ModelTest::test_sample_generate
tests/transformers/tests/models/t5/test_modeling_t5.py::T5ModelTest::test_sample_generate_dict_output
/home/anneog/github/ankurneog/optimum-habana/optimum/habana/transformers/generation/utils.py:1614: UserWarning: max_length is deprecated in this function, use stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length)) instead.
warnings.warn(
-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html
=================================================================== 107 passed, 45 skipped, 17 warnings in 751.38s (0:12:31) ===================================================================

@ankurneog I took a closer look at these tests. What they do is to basically compare the outputs of generation with a given decoding strategy (e.g. beam search) calling model.generate and calling directly the method associated to the decoding strategy (e.g. model.beam_search). This is just a test to make sure generate works properly.
The issue here is that the generate method does a lot of processing before calling beam_search (or any other decoding strategy), and, in particular, it sets the model kwargs: https://github.com/huggingface/optimum-habana/blob/8c9aade03f801f4b69642c7aafa66d5d123b4ef1/optimum/habana/transformers/generation/utils.py#L578
Now, users should never call beam_search directly, they will always call generate. Let's look at the test from closer:

When calling generate, it passes : https://github.com/huggingface/optimum-habana/blob/8c9aade03f801f4b69642c7aafa66d5d123b4ef1/tests/transformers/tests/generation/test_utils.py#L395
When directly calling beam_search, it fails: https://github.com/huggingface/optimum-habana/blob/8c9aade03f801f4b69642c7aafa66d5d123b4ef1/tests/transformers/tests/generation/test_utils.py#L426
The reason is that model_kwargs are not set by generate (since we bypass it by directly calling beam_search) and are manually set here: https://github.com/huggingface/optimum-habana/blob/8c9aade03f801f4b69642c7aafa66d5d123b4ef1/tests/transformers/tests/generation/test_utils.py#L425
So you should add the missing model kwargs in this dictionary to have the tests pass. That's really as simple as that and that's the reason you get all these errors.

Thanks @regisss  for the clear explanation, Please help check this : https://github.com/huggingface/optimum-habana/pull/482


Make app.excalidraw.com a PWA

I recently upgraded to pro due to sync issues. I was enjoying having the desktop PWA from chrome. Unfortunately with the pro version I have to keep a pinned tab instead.

Excalidraw Plus would entail way more complexity implementing ServiceWorkers for (at least in theory, but my experience with SW for these kind of apps is limited) as opposed to excalidraw.com which is in comparison way simpler (it's mostly static). Another consideration was the caching issues we have on excalidraw.com.
All in all, it was deemed too risky and challenging at this stage. We may revisit the idea as we are more mature.

It wouldn't be hard to add a rudimentary service worker which does nothing except serves an offline.html and would allow me to have an installable PWA.


Adds an option to the action to run in "fix" mode

This PR is a small change which adds an additional, optional, input to the action called mode. My goal with this change is to make it so CI pipelines can automatically apply the licenses to PRs which lack them (applying the changes would be handled by a different action like this one). I looked around and did not see any mention of such functionality; apologies if I missed it.
Some additional notes:

mode can take one of two values: check or fix. I set the default to check to preserve existing behavior.
I have also updated the README with the new input description.
I see from the diff that my IDE removed some trailing spaces; if you really want those back I can redo the changes with vim or something
I have tested that this works as intended with one of my own repositories; if you want proper unit tests I will need guidance/help on how to go about doing that.

@kezhenxu94 I added a subsection detailing fix mode usage per your request.


Inomurko/231 boba deployer fix

Closes (https://github.com/bobanetwork/boba/issues/231)
Overview
Occasionally the boba deployer would die because of hiccups in DAO deployment.
Changes

Grep docker compose logs and detect DAO exit and restart the boba deploy container.

Testing
/ Should reduce occasional integration test failure.

There is one problem when we redeploy theboba-deployer. The CDM Fast address is changed and it's different from what we have in SDK. The message relayer fast won't work.

There is one problem when we redeploy theboba-deployer. The CDM Fast address is changed and it's different from what we have in SDK. The message relayer fast won't work.

Can you tell me more? How is it that the CDM Fast is registering a stale address?

There is one problem when we redeploy theboba-deployer. The CDM Fast address is changed and it's different from what we have in SDK. The message relayer fast won't work.

Can you tell me more? How is it that the CDM Fast is registering a stale address?

The CDM Fast address is hardcoded in https://github.com/bobanetwork/boba/blob/6e034ed2817105846a19aac3f50241fade1e77f1/packages/sdk/src/utils/contracts.ts#L127. Redeploying the CDM Fast contract will generate another contract address. The message relayer fast still uses the contract address in SDK, because it uses SDK to query and analyze events.

Aha. Yes, makes sense!

Codecov Report

Merging #232 (e1f32fd) into develop (9a5000e) will increase coverage by 26.36%.
The diff coverage is n/a.

@@             Coverage Diff              @@
##           develop     #232       +/-   ##
============================================
+ Coverage    47.22%   73.58%   +26.36%
============================================
  Files           99       65       -34
  Lines         3867     1950     -1917
  Branches       762      322      -440
============================================
- Hits          1826     1435      -391
+ Misses        2041      515     -1526

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Coverage Δ

...ba/contracts/contracts/DAO/governance/Timelock.sol

packages/boba/contracts/contracts/TokenPool.sol

...ontracts/contracts/standards/L2GovernanceERC20.sol

...ges/boba/contracts/contracts/L2BillingContract.sol

...ckages/boba/contracts/contracts/ERC721Registry.sol

...s/boba/contracts/contracts/bridges/L2NFTBridge.sol

...ntracts/contracts/standards/xL2GovernanceERC20.sol

.../contracts/libraries/Lib_ResolvedDelegateProxy.sol

...ontracts/DAO/governance/GovernorBravoDelegator.sol

...a/contracts/contracts/standards/L2CustomERC721.sol

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[ADR] 0001 - Adopt Multiaddr

Summary
This pull request adds an Architecture Decision Record (ADR) for adopting the multiaddr addressing format to simplify and consolidate node identity in our system. The ADR explains the context, problem statement, decision drivers, considered options, and the decision outcome, along with the positive and negative consequences of the chosen option.
The chosen option is to adopt the multiaddr format as it provides a standardized, extensible, and future-proof solution for addressing nodes in our network.
Please review the ADR and provide feedback on:

The clarity of the problem statement and context
The thoroughness of the decision drivers and considered options
The appropriateness of the chosen option given the decision drivers
Any additional considerations that may have been overlooked
Any changes in formatting, grammar, or language for better readability

@bryanchriswhite Bump

@bryanchriswhite You think this one is good to merge in?


nixos/postgresql: add statements option to replace the ensureDatabases and ensureUsers options

Motivation for this change
I have been brainwashed convinced in a reasonably fashion by @grahamc that the ensure* options aren't a good fit for NixOS. I'm seeking to remove these options from both the mysql and postgresql modules, but still leave other module authors who wish to provision databases with a reasonable way to do so. My plan for postgresql and mysql was as follows:

remove the ensureDatabases and ensureUsers options
add a statements (this is a horrible name, please someone come up with a better one) option which module authors (and NixOS users) can use to write idempotent sql statements which will be executed as the mysql/postgresql super admin user to do things like provision local accounts, databases, etc... (ie. replace the ensureDatabases and ensureUsers options with a new option that provides the same functionality, but with even more flexibility)
replace all usage of ensureDatabases and ensureUsers in NixOS with the new statements option
strongly caution NixOS users against carelessly using the statements option, very explicitly explaining how it leads to systems which aren't reproducible and why that is bad
separate the execution of the sql in the statements option from the postgresql/mysql systemd service so a full restart of postgresql/mysql is not required every time someone alters statements (this step would be done in a separate PR immediately following the acceptance and merging of this PR)

I wrote some code for the mysql module to prove this idea works, and I was relatively happy with the results.  I was surprised to learn postgresql doesn't provide a very convenient way to write idempotent statements for database and user creation like mysql does :scream: You can't write create database if not exists! I searched around and found what appears to be the "best" solution and adopted that method for this PR. For example, creating a database: select 'create database nextcloud owner nextcloud' where not exists (select from pg_database where datname = 'nextcloud')\gexec
You might ask why should we get rid of the ensure* options?
Aside from promoting systems which aren't reproducible, the ensure* options have a few other major problems with postgresql, mainly dealing with encoding and extensions (which @Ma27 ended up being the victim of, see the recent matrix-synapse fiasco for details on that :disappointed:). Currently all attempts to extend the ensureDatabase options have led to scenarios where a users configuration.nix could very easily not match the reality of what the database actually looks like. After investigating what it would take to make the ensure* options truly reproducible and actually enforce what they describe I came to the conclusion this is simply a bad idea because any database changes we might make can lead to data corruption and require a backup before making them - something we don't want to do every time the user does a rebuild.
I am very interested in feedback from postgresql users to see how they feel about this PR. I'm guessing that some of you will argue that users can simply add sql statements to the .postStart of the postgresql systmed unit - while I'm not completely opposed to that idea, I do see it as somewhat hacky, provides no documentation, and causes postgresql restarts with no easy and user-facing-api friendly way correct that.
If you have the capacity to please take a few moments to consider this proposal and comment with your thoughts. I'm very interested in improving the database ecosystem we have here, but will need to rely on the postgresql experts because I live in a mysql (mariadb) world.
Also note that this is a draft PR and the code is very sloppy proof of concept. I'll get around to cleaning it up after I get some feedback to see if I'm heading in the right direction here.
Thanks :tada:
Things done

[ ] Tested using sandboxing (nix.useSandbox on NixOS, or option sandbox in nix.conf on non-NixOS linux)
Built on platform(s)

[ ] NixOS
[ ] macOS
[ ] other Linux distributions

[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[ ] Tested compilation of all pkgs that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review wip"
[ ] Tested execution of all binary files (usually in ./result/bin/)
[ ] Determined the impact on package closure size (by running nix path-info -S before and after)
[ ] Ensured that relevant documentation is up to date
[ ] Fits CONTRIBUTING.md.

cc @flokli

:+1: , also your idea about "reloading script" is implementation of last section of https://github.com/NixOS/nixpkgs/issues/65296
I also agree that without separate reloader script this statements is only slightly better than patching .postStart.

Random thought:
I don't speak SQL at all. The ensureDatabase option was nice because it enabled me not to lookup the right SQL syntax and also hid some differences between SQL dialects. Also note that you found it difficult to discover the right idempotent postgresql incantation--think about SQL noobs like me. My conclusion is that the casual user may be better off with a solution which does not involve setting the statements option themselves.
If you are to remove ensureDatabase because it does not fit all scenarios, is it possible to provide a function in lib for example which takes arguments like ensureDatabase and evaluates to the right value of the statements option ? This would only be limited to easy cases, like ensureDatabase today.

@symphorien I'd assume most nixos modules will still provide some sql statements out of the box, if your database is local.

@flokli exactly.
@symphorien see nixos/modules/services/misc/gitea.nix as an example. Also keep in mind that we don't want to encourage the use of this option, especially for "SQL noobs" like you. Why? The ensureDatabases options has given you a very false sense of security. It isn't reproducible and if we extend the option any further there is serious potential using that option could end up eating your data.

@Ma27 thanks for your comments. The "eating your data" comment is something that would be more of a concern if we extend the current ensure* options to include things like locale. Upstream documentation states databases should always be backed up immediately before operations like changing locale.
I agree we need more people in this conversation. Who should we ping?

I avoid using these options because of their lack of flexibility.
Instead I uses a Sqitch project that contains all the things I need to ensure. I have created a sqitch NixOS module to run the deployments:
{ config, lib, pkgs, ... }:
with lib;
let

  cfg = config.services.sqitch;

  deploymentOptions = {
    options = {
      bundle = mkOption {
        type = types.package;
        example = "pkgs.mybundle";
        description = ''
          PostgreSQL Sqitch bundle to deploy.
        '';
      };

      targetURI = mkOption {
        type = types.str;
        example = "db:pg://postgres@localhost:5432/postgres";
        description = ''
          Database database connection URI of the target. Only PostgreSQL targets are supported.
        '';
      };

      user = mkOption {
        type = types.str;
        default = "postgres";
        description = ''
          User running the Sqitch deployment.
        '';
      };

      group = mkOption {
        type = types.str;
        default = "postgres";
        description = ''
          Group running the Sqitch deployment.
        '';
      };

      requires = mkOption {
        default = [];
        type = types.listOf types.str;
        description = ''
          systemd `Requires=` dependencies to add to the Sqitch deployment service.
        '';
      };

      after = mkOption {
        default = [];
        type = types.listOf types.str;
        description = ''
          systemd `After=` dependencies to add to the Sqitch deployment service.
        '';
      };
    };
  };

in

{
  options = {

    services.sqitch = {

      enable = mkOption {
        type = types.bool;
        default = true;
        description = ''
          Whether to run Sqitch deployments.
        '';
      };

      deployments = mkOption {
        default = { };
        type = with types; attrsOf (submodule deploymentOptions);
        description = ''
          Attribute set of deployments to run. Creates
          <literal>sqitch-''${deployment}.service</literal> systemd units for
          each deployments defined here. Other services can add dependencies
          to those services if they rely on the deployments.
        '';
      };
    };
  };

  config = mkIf (cfg.enable && cfg.deployments != { }) {

    assertions = let
      deployments = (mapAttrsToList (deployment: options: options) cfg.deployments);
    in [
      {
        assertion = all (deployment: hasPrefix "db:pg://" deployment.targetURI) deployments;
        message = ''
          Options `service.sqitch.deployments.<name>.targetURI` must start with "db:pg://" because only PostgreSQL is supported.
        '';
      }
    ];

    environment.systemPackages = [
      pkgs.sqitchPg
    ];

    systemd.services =
    let
      toServicePair = deployment: options:
        let
          deploymentService = {
            description = "Sqitch deployment for ${deployment}";

            requires = options.requires;
            after = options.after;

            path = [
              config.services.postgresql.package
              pkgs.sqitchPg
            ];

            script = ''
              sqitch --cd ${options.bundle} deploy --target ${options.targetURI}
            '';

            serviceConfig = {

              Type = "oneshot";

              RemainAfterExit = true;

              User = options.user;
              Group = options.group;

              PrivateTmp = true;

              ProtectSystem = "strict";
              ProtectHome = true;

              ProtectKernelTunables = true;
              ProtectKernelModules = true;
              ProtectControlGroups = true;

              SyslogIdentifier = "sqitch-${deployment}";
            };
          };
        in {
          name = "sqitch-${deployment}";
          value = deploymentService;
        };
      toServices = deployments: listToAttrs (mapAttrsToList toServicePair deployments);
    in
      toServices cfg.deployments;
  };
}

@datafoo thanks for sharing that. I'm sure some people would find that very useful.
The ensureDatabase options continue to grow into more and more of a problem. Now a zabbix update is being held back as the default ensureDatabase encoding isn't compatible (for mysql). The ensureDatabase options aren't powerful enough, but if we extend them any further they aren't reproducible. I really want to proceed with this PR, but feel like more feedback, or at least people nodding in agreement (or even just giving this comment :+1:) is required.
Anyone?

I agree that the options are currently problematic and I ended up creating a custom postStart solution that handles the functionality I needed (just the initial creation of the databases with extensions, apps will handle schema migrations).
I have minor preference for statements with proper disclaimers over insufficient ensure options.
By the way, there was a PostgreSQL patch trying to add CREATE DATABASE … IF NOT EXISTS but it seems to have fizzled out: https://www.postgresql.org/message-id/flat/CAFcNs%2BpQZ4QzGF-mBy%3D6pkyw__E62inX9DubWu7y2goahvT9sA%40mail.gmail.com

@jtojnar thanks for your comments. My thought would be to eliminate and replace the ensureDatabase and ensureUsers options from both mysql and postgresql, as opposed to supplementing them. I think the ensure* options are fundamentally a less than favourable match for NixOS.

:+1: for getting rid of ensureDatabases and friends. I have also had to do custom scripts for most things anyway.

Thanks @peterhoeg! Would you see any value in a statements option, or would you encourage users to append to the service postStart option?

As I see it, there are 2 problems with the current situation:

Many times more flexibility is needed than what ensureXX supports (ie, creating a role with a given password for pg)

initialScript isn't expected to be idempotent and thus changing it after it has run once, has no effect.

Writing idempotent sql is hard and thus people will inevitably end up writing custom sql for their special cases anyway.

@peterhoeg if we're looking to cover simple cases and give a reasonable amount of flexibility to users, with warning around breaking reproducibility, I think the statement option has some value... but I agree for a number of use cases something custom still might be required.
So is the statements option worth it in your opinion, even if not something you would personally use?

ping @peterhoeg

So is the statements option worth it in your opinion, even if not something you would personally use?


Wanna help? :D

Can you try registry-mirror out and share what you think of it and how would you make it better? Pleaaaase :)
You can find on the README the quickest way to test it out - https://github.com/diasdavid/registry-mirror#quick-setup-probably-all-that-you-need
Right now, our clone only has 30555 out of 499877 modules available on npm, but it is steadily growing. What this means is that modules that have dependencies that are not downloaded yet, won't be able to finish, but modules like bignumber which doesn't have any dependencies, are installed right away :)
How to check how many modules IPFS has available:
$ ipfs files stat /npm-registry
QmVp8xYepc77iYhUdusYdSCQUH1kyK9qidpVQaoyLAP4FT
Size: 0
CumulativeSize:<PHONE_NUMBER>9
ChildBlocks: 30555

Thank you :)

https://github.com/diasdavid/registry-mirror/pull/11
https://github.com/diasdavid/registry-mirror/issues/7
https://github.com/diasdavid/registry-mirror/pull/6
Keep them coming :D

How about something that automatically sets the registry in npm for you, e.g. if I run registry-mirror --ipfs --set-registry ?

@Dignifiedquire it would be nice but I think this module should be as simple as possible.
What do you think of making the module usable by other, that way you could create a wrapper that used registry-mirror api and was responsible to tweak ~/.npmrc?

@fampinheiro sounds like a good idea yes


[To rel/0.12] [IOTDB-1584] Doesn't support order by time desc in cluster mode

The type of batchdata is not serialized in cluster which leads to the information is lost.
WE NEED TO TEST Whether this modification is right.

Correctness check:

release note...


libomxil-bellagio: autoreconf should be removed

Should autoreconf be needed? CentOS doesn't need it.
And -Wall -Werror is removed.
Contributing to homebrew-xorg:
You can erase any parts of this template not applicable to your Pull Request.

[x] Have you followed the guidelines in our Contributing document?
[x] Have you checked to ensure there aren't other open Pull Requests for the same update/change?
[ ] Have you included a log of the failed build without your patch using brew gist-logs <formula>?
[ ] Does your submission pass
brew audit --strict <formula> (where <formula> is the name of the formula you're submitting)?
[ ] Have you built your formula locally prior to submission with brew install <formula>?

Could you please report the result of:
brew sh
echo $CFLAGS

[skpark@nodemaster01 ~]$ brew sh
Your shell has been configured to use Homebrew's build environment;
this should help you build stuff. Notably though, the system versions of
gem and pip will ignore our configuration and insist on using the
environment they were built under (mostly). Sadly, scons will also
ignore our configuration.
When done, type `exit'.
brew ~$ echo $CFLAGS

brew ~$

Yes. I intend to override CFLAGS set in the Makefile.

add CFLAGS= to the configure command and remove them from make commands`

I have succeeded in installing in my system. But I cannot pass ci/circleci.

Ok. I will try.

I failed when
 31     system "./configure", *args, "CFLAGS="
 32     ENV.deparallelize
 33     system "make"
 34     system "make", "install"
 35     system "make", "check" if build.with?("test")


Add operators to the charging_state attribute

Is your feature request related to a problem? Please describe.
One of my devices only exposes whether or not it is charging trough it's icon. This icon can be, depending on the current battery level, any of the following: mdi:battery-charging-100, mdi:battery-charging-90, mdi:battery-charging-80,...
I'd like to use the many possibilities your card provides while also being able to see whether or not my device is charging.
Describe the solution you'd like
I'd like to be able to use operators (like the ones in filter objects) on the attribute object of the charging state object.
This would allow me to check whether the icon contains the word "charging" and show the correct icon.
Additional context
The config would look like this:
...
entities:
  - entity: sensor.my_device_battery_level
    charging_state:
      attribute:
        name: icon
        operator: contains
        value: 'charging'
...

Closing for now as this #347 should solve this issue I think. Please let me know if it's not sufficient. This will be available in v2.0.0

That solves my issue indeed, thanks!


New version of Samsung Internet browser

Version 5 of Samsung Internet is released. The site is limited till version 4 of Samsung Internet. See this link: http://developer.samsung.com/internet

Duplicate of https://github.com/Fyrd/caniuse/issues/3320 Currently looking into getting a way to test the browser, once I have that I'll add it.


Feature/scoped authtoken

https://github.com/blockstack/gaia/issues/142

I didn't know about find().  I updated the code to use it instead.


Connectivity: One Robot at a Time Tooltip

As a user, I need to know that I have to disconnect from all robots before connecting to any other robots.
Acceptance Criteria

‘Connect’ button inactive for all robots other than currently connected robot
User informed that they have to disconnect from all other robots before connecting to a robot (e.g. hover tooltip on connect button)

Design

https://projects.invisionapp.com/share/V5FI4I26QWD#/screens/275374451

Dependent on a tooltips framework.

Tooltip framework added in #1855!

I am closing this for now.
Still a valid feature, but not a prioritized one (no reported issues around this yet)


Fix DatePicker not saving drafts

Details

Fixed Issues

$ https://github.com/Expensify/App/issues/31612
Tests

[x] Check all screens, that contain DateInputs

[x] Date of birth
[x] Money request date
[x] Money request edit date
[x] Company step
[x] Identity form
[x] Additional details
[x] IOU request step date

[x] Check error flow

Go to Settings > Workspaces > Select Workspace > Bank Account.
Go trough the add bank account flow until you reach a page that you have to enter any date, like Company information or Personal Information.
Trigger the enter valid data error message on the date field (select the filed but do not select any date and move to another field).
Select a valid date.

[x] Verify that no errors appear in the JS console

Offline tests

QA Steps

[x] Verify that no errors appear in the JS console

PR Author Checklist

[x] I linked the correct issue in the ### Fixed Issues section above
[x] I wrote clear testing steps that cover the changes made in this PR

[x] I added steps for local testing in the Tests section
[x] I added steps for the expected offline behavior in the Offline steps section
[x] I added steps for Staging and/or Production testing in the QA steps section
[x] I added steps to cover failure scenarios (i.e. verify an input displays the correct error message if the entered data is not correct)
[x] I turned off my network connection and tested it while offline to ensure it matches the expected behavior (i.e. verify the default avatar icon is displayed if app is offline)
[x] I tested this PR with a High Traffic account against the staging or production API to ensure there are no regressions (e.g. long loading states that impact usability).

[x] I included screenshots or videos for tests on all platforms
[x] I ran the tests on all platforms & verified they passed on:

[x] Android: Native
[x] Android: mWeb Chrome
[x] iOS: Native
[x] iOS: mWeb Safari
[x] MacOS: Chrome / Safari
[x] MacOS: Desktop

[x] I verified there are no console errors (if there's a console error not related to the PR, report it or open an issue for it to be fixed)
[x] I followed proper code patterns (see Reviewing the code)

[x] I verified that any callback methods that were added or modified are named for what the method does and never what callback they handle (i.e. toggleReport and not onIconClick)
[x] I verified that the left part of a conditional rendering a React component is a boolean and NOT a string, e.g. myBool && <MyComponent />.
[x] I verified that comments were added to code that is not self explanatory
[x] I verified that any new or modified comments were clear, correct English, and explained "why" the code was doing something instead of only explaining "what" the code was doing.
[x] I verified any copy / text shown in the product is localized by adding it to src/languages/* files and using the translation method

[x] If any non-english text was added/modified, I verified the translation was requested/reviewed in #expensify-open-source and it was approved by an internal Expensify engineer. Link to Slack message:

[x] I verified all numbers, amounts, dates and phone numbers shown in the product are using the localization methods
[x] I verified any copy / text that was added to the app is grammatically correct in English. It adheres to proper capitalization guidelines (note: only the first word of header/labels should be capitalized), and is approved by marketing by adding the Waiting for Copy label for a copy review on the original GH to get the correct copy.
[x] I verified proper file naming conventions were followed for any new files or renamed files. All non-platform specific files are named after what they export and are not named "index.js". All platform-specific files are named for the platform the code supports as outlined in the README.
[x] I verified the JSDocs style guidelines (in STYLE.md) were followed

[x] If a new code pattern is added I verified it was agreed to be used by multiple Expensify engineers
[x] I followed the guidelines as stated in the Review Guidelines
[x] I tested other components that can be impacted by my changes (i.e. if the PR modifies a shared library or component like Avatar, I verified the components using Avatar are working as expected)
[x] I verified all code is DRY (the PR doesn't include any logic written more than once, with the exception of tests)
[x] I verified any variables that can be defined as constants (ie. in CONST.js or at the top of the file that uses the constant) are defined as such
[x] I verified that if a function's arguments changed that all usages have also been updated correctly
[x] If any new file was added I verified that:

[x] The file has a description of what it does and/or why is needed at the top of the file if the code is not self explanatory

[x] If a new CSS style is added I verified that:

[x] A similar style doesn't already exist
[x] The style can't be created with an existing StyleUtils function (i.e. StyleUtils.getBackgroundAndBorderStyle(theme.componentBG))

[x] If the PR modifies code that runs when editing or sending messages, I tested and verified there is no unexpected behavior for all supported markdown - URLs, single line code, code blocks, quotes, headings, bold, strikethrough, and italic.
[x] If the PR modifies a generic component, I tested and verified that those changes do not break usages of that component in the rest of the App (i.e. if a shared library or component like Avatar is modified, I verified that Avatar is working as expected in all cases)
[x] If the PR modifies a component related to any of the existing Storybook stories, I tested and verified all stories for that component are still working as expected.
[x] If the PR modifies a component or page that can be accessed by a direct deeplink, I verified that the code functions as expected when the deeplink is used - from a logged in and logged out account.
[x] If a new page is added, I verified it's using the ScrollView component to make it scrollable when more elements are added to the page.
[x] If the main branch was merged into this PR after a review, I tested again and verified the outcome was still expected according to the Test steps.

Screenshots/Videos

Android: Native
![android native](https://github.com/Expensify/App/assets/91068263/9dd3d918-8f2a-488a-922f-7ad42265c841)

Android: mWeb Chrome
![android web](https://github.com/Expensify/App/assets/91068263/abdf0cfb-87be-4202-be88-3f3ddbc2a2a2)

iOS: Native
![ios native](https://github.com/Expensify/App/assets/91068263/4c58f769-1021-4c73-93b3-d6e499464503)

iOS: mWeb Safari
![ios web](https://github.com/Expensify/App/assets/91068263/63d2942a-e1b8-40f8-94aa-146dc922bba5)

MacOS: Chrome / Safari

MacOS: Desktop

Reviewer Checklist

[X] I have verified the author checklist is complete (all boxes are checked off).
[X] I verified the correct issue is linked in the ### Fixed Issues section above
[ ] I verified testing steps are clear and they cover the changes made in this PR

[X] I verified the steps for local testing are in the Tests section
[X] I verified the steps for Staging and/or Production testing are in the QA steps section
[X] I verified the steps cover any possible failure scenarios (i.e. verify an input displays the correct error message if the entered data is not correct)
[X] I turned off my network connection and tested it while offline to ensure it matches the expected behavior (i.e. verify the default avatar icon is displayed if app is offline)

[X] I checked that screenshots or videos are included for tests on all platforms
[X] I included screenshots or videos for tests on all platforms
[X] I verified tests pass on all platforms & I tested again on:

[X] Android: Native
[X] Android: mWeb Chrome
[X] iOS: Native
[X] iOS: mWeb Safari
[X] MacOS: Chrome / Safari
[X] MacOS: Desktop

[X] If there are any errors in the console that are unrelated to this PR, I either fixed them (preferred) or linked to where I reported them in Slack
[ ] I verified proper code patterns were followed (see Reviewing the code)

[X] I verified that any callback methods that were added or modified are named for what the method does and never what callback they handle (i.e. toggleReport and not onIconClick).
[X] I verified that the left part of a conditional rendering a React component is a boolean and NOT a string, e.g. myBool && <MyComponent />.
[X] I verified that comments were added to code that is not self explanatory
[X] I verified that any new or modified comments were clear, correct English, and explained "why" the code was doing something instead of only explaining "what" the code was doing.
[X] I verified any copy / text shown in the product is localized by adding it to src/languages/* files and using the translation method
[X] I verified all numbers, amounts, dates and phone numbers shown in the product are using the localization methods
[X] I verified any copy / text that was added to the app is grammatically correct in English. It adheres to proper capitalization guidelines (note: only the first word of header/labels should be capitalized), and is approved by marketing by adding the Waiting for Copy label for a copy review on the original GH to get the correct copy.
[X] I verified proper file naming conventions were followed for any new files or renamed files. All non-platform specific files are named after what they export and are not named "index.js". All platform-specific files are named for the platform the code supports as outlined in the README.
[X] I verified the JSDocs style guidelines (in STYLE.md) were followed

[X] If a new code pattern is added I verified it was agreed to be used by multiple Expensify engineers
[X] I verified that this PR follows the guidelines as stated in the Review Guidelines
[X] I verified other components that can be impacted by these changes have been tested, and I retested again (i.e. if the PR modifies a shared library or component like Avatar, I verified the components using Avatar have been tested & I retested again)
[X] I verified all code is DRY (the PR doesn't include any logic written more than once, with the exception of tests)
[X] I verified any variables that can be defined as constants (ie. in CONST.js or at the top of the file that uses the constant) are defined as such
[X] If a new component is created I verified that:

[X] A similar component doesn't exist in the codebase
[X] All props are defined accurately and each prop has a /** comment above it */
[X] The file is named correctly
[X] The component has a clear name that is non-ambiguous and the purpose of the component can be inferred from the name alone
[X] The only data being stored in the state is data necessary for rendering and nothing else
[X] For Class Components, any internal methods passed to components event handlers are bound to this properly so there are no scoping issues (i.e. for onClick={this.submit} the method this.submit should be bound to this in the constructor)
[X] Any internal methods bound to this are necessary to be bound (i.e. avoid this.submit = this.submit.bind(this); if this.submit is never passed to a component event handler like onClick)
[X] All JSX used for rendering exists in the render method
[X] The component has the minimum amount of code necessary for its purpose, and it is broken down into smaller components in order to separate concerns and functions

[X] If any new file was added I verified that:

[X] The file has a description of what it does and/or why is needed at the top of the file if the code is not self explanatory

[X] If a new CSS style is added I verified that:

[X] A similar style doesn't already exist
[X] The style can't be created with an existing StyleUtils function (i.e. StyleUtils.getBackgroundAndBorderStyle(theme.componentBG)

[X] If the PR modifies code that runs when editing or sending messages, I tested and verified there is no unexpected behavior for all supported markdown - URLs, single line code, code blocks, quotes, headings, bold, strikethrough, and italic.
[X] If the PR modifies a generic component, I tested and verified that those changes do not break usages of that component in the rest of the App (i.e. if a shared library or component like Avatar is modified, I verified that Avatar is working as expected in all cases)
[X] If the PR modifies a component related to any of the existing Storybook stories, I tested and verified all stories for that component are still working as expected.
[X] If the PR modifies a component or page that can be accessed by a direct deeplink, I verified that the code functions as expected when the deeplink is used - from a logged in and logged out account.
[X] If a new page is added, I verified it's using the ScrollView component to make it scrollable when more elements are added to the page.
[X] If the main branch was merged into this PR after a review, I tested again and verified the outcome was still expected according to the Test steps.
[ ] I have checked off every checkbox in the PR reviewer checklist, including those that don't apply to this PR.

Screenshots/Videos

Android: Native

https://github.com/Expensify/App/assets/16493223/83d8ad64-ae3d-45d3-ad47-07b7001fa1e8

Android: mWeb Chrome

iOS: Native

https://github.com/Expensify/App/assets/16493223/f875e939-413e-43b5-b1e5-eacd156e86c9

iOS: mWeb Safari

https://github.com/Expensify/App/assets/16493223/87fa6c16-a21c-49b8-9e2b-3c2b8758d261

MacOS: Chrome / Safari

MacOS: Desktop

@kowczarz Please add a test case to verify that the draft is being saved correctly

I see one more issue with ref, but I will solve it tomorrow.

I think it's ready for the final review.

@kowczarz What about https://github.com/Expensify/App/pull/32815#discussion_r1425365157?

@s77rt I applied your suggestions earlier.

@s77rt I had to missed it.

:hand: This PR was not deployed to staging yet because QA is ongoing. It will be automatically deployed to staging after the next production release.

🚀 Deployed to staging by https://github.com/luacmartins in version: 1.4.14-0 🚀

platform
result

🤖 android 🤖
success ✅

🖥 desktop 🖥
success ✅

🍎 iOS 🍎
failure ❌

🕸 web 🕸
success ✅

🚀 Deployed to staging by https://github.com/luacmartins in version: 1.4.14-0 🚀

platform
result

🤖 android 🤖
success ✅

🖥 desktop 🖥
success ✅

🍎 iOS 🍎
success ✅

🕸 web 🕸
success ✅

🚀 Deployed to production by https://github.com/jasperhuangg in version: 1.4.14-6 🚀

platform
result

🤖 android 🤖
success ✅

🖥 desktop 🖥
success ✅

🍎 iOS 🍎
failure ❌

🕸 web 🕸
success ✅


Add clipboard module for copy/paste

https://github.com/rsp/deno-clipboard
Works on Linux, Mac and Windows

Nice :+1: I'm curious about Clipboard API, @rsp could you open an issue in main repo for that?

👍 @bartlomieju I created denoland/deno#3450 with all the info.


Task in apply from failed, but work from top level build.gradle file

Have code:
task detektProjectBaseline(type: io.gitlab.arturbosch.detekt.DetektCreateBaselineTask) {
    description = "Overrides current baseline."
    ignoreFailures.set(true)
    parallel.set(true)
    buildUponDefaultConfig.set(true)
    setSource(files(rootDir))
    config.setFrom(files("$rootDir/config/detekt/detekt.yml"))
    baseline.set(file("$rootDir/config/detekt/detekt-baseline.xml"))
    include("**/*.kt")
    include("**/*.kts")
    exclude("**/resources/**")
    exclude("**/build/**")
}

when this code is located in top level build.gradle file. Gradle sync and build work as expected. But when I just move it to external file and call apply from 'file.gradle' build failed on:

Caused by: groovy.lang.MissingPropertyException: Could not get unknown property 'io' for root project 'My Application' of type org.gradle.api.Project.

Expected Behavior
Build work as expected
Current Behavior
Build failed with error Caused by: groovy.lang.MissingPropertyException: Could not get unknown property 'io' for root project 'My Application' of type org.gradle.api.Project.
Steps to Reproduce
https://github.com/mtrakal/gradle-detekt-issue
https://github.com/mtrakal/gradle-detekt-issue/blob/master/config/detekt.gradle
https://github.com/mtrakal/gradle-detekt-issue/blob/master/build.gradle
I reported same issue to Detekt, because have no idea where is the root of cause: https://github.com/detekt/detekt/issues/3594

file.gradle has a different classpath to the main build.gradle file, so the task types visible in build.gradle are not visible in file.gradle.
Take a look at precompiled script plugins as one way to break up your configuration into reusable chunks.


Implement types in accordance with database tables

Issue #6 describes data types used for task interaction, as well as IRepository trait.

Add types from the Issue #6
Add IRepository interface.

This issue has been closed by multiple other pull requests, namely #18 and #11 .


deploy new ember image to TEST (20181115-37cab27)

image: oapass/ember:20181115-37cab27@sha256:18bbff8f018df480af12a0abcfed3cde9afb9dcd6f5a9779bb57d9bd70ea7b70

This has been deployed.
Should I wait until this is tested as working before we deploy to demo?

We're focusing our testing effort in demo, so I would deploy this to demo right away so folks can continue to testing in demo.
AFter the launch we should figure out how to effectively make user of the 3 envs set up. :)

Done


Error starting application

Hi team,
I'm getting the following when trying to start the application:
┌──(kali㉿kali)-[~/RemoteIDReceiver/Receiver]
└─$ python ./backend/dronesniffer/main.py -p 80
2023-12-11 08:01:37,316 main INFO Setting up database...
2023-12-11 08:01:37,317 info_handler INFO setting up database and tables
2023-12-11 08:01:37,319 main INFO Starting sniff manager...
Traceback (most recent call last):
  File "/home/kali/RemoteIDReceiver/Receiver/./backend/dronesniffer/main.py", line 66, in <module>
    main()
  File "/home/kali/RemoteIDReceiver/Receiver/./backend/dronesniffer/main.py", line 56, in main
    settings = get_settings()
               ^^^^^^^^^^^^^^
  File "/home/kali/RemoteIDReceiver/Receiver/backend/dronesniffer/settings.py", line 15, in get_settings
    settings = Settings()
               ^^^^^^^^^^
  File "/usr/local/lib/python3.11/dist-packages/pydantic/main.py", line 164, in __init__
    __pydantic_self__.__pydantic_validator__.validate_python(data, self_instance=__pydantic_self__)
pydantic_core._pydantic_core.ValidationError: 1 validation error for Settings
google_maps_api_key
  Field required [type=missing, input_value={}, input_type=dict]
    For further information visit https://errors.pydantic.dev/2.5/v/missing
2023-12-11 08:01:37,320 sniffers INFO Stopping all sniffers...
2023-12-11 08:01:37,321 sniffers INFO All sniffers were stopped successfully.

I tested it on Kali 2023.3 and Kali 2023.4. Looks like it wants the google maps API key before I've had a chance to set it.

could you try now? I added the None value explicitly by default into the variable that stores the google maps api key.
google_maps_api_key: Optional[str] = None
Apparently pydantics requires an initialization value

That was fast! And yes, it fixed the issue. 👍


Fix infinite loop on empty first key section (#360).

See #360.
The solution is to distinguish between root calls to ConfigurationRoot.GetChildrenImplementation() and subsequent recursive calls. So now passing null to that method signals this is a root call, whereas passing string.Empty means we're looking at a key section that has no name.

More tests, and :shipit: