Use connection pooling in SQLite server storage

As per discussion here https://github.com/taskchampion/taskchampion/pull/206#issuecomment-860851778
Currently (for 0.4.0 release) a new SQLite connection is made for each operation which isn't too efficient. Instead the r2d2 crate or similar can be used to have a pool of connections
It's a bit hard to guess how worthwhile this change is - the performance with creating lots of connections is likely perfectly adequate even with a vague "handful" of users; and anyone aiming to run a server for more than that probably wants to implement a more appropriate storage backend anyway (e.g Postgres/Redis/etc)

I think the main reason to do this would be to provide a better example for future use of other DB backends, using the same approach (r2d2 supports multiple backends).

Ah yeh that's a good point!
I guess the other option would be to use something more all-encompassing like https://github.com/diesel-rs/diesel which abstracts away the specific query DB library also (so same connection implementation would work for sqlite and postgres), and also gives things like schema migrations.. but seems like a much heavier option dependency-wise


[Bug]: No longer works in SD Forge after Forge upgraded to Gradio 4.

Is there an existing issue for this?

[X] I have searched the existing issues and checked the recent builds/commits of both this extension and the webui

Is EasyPhoto the latest version?

[X] I have updated EasyPhoto to the latest version and the bug still exists.

What happened?
https://github.com/lllyasviel/stable-diffusion-webui-forge upgraded to Gradio 4 + WebUI 1.10. This has broken a number of Extensions including EasyPhoto.
Steps to reproduce the problem

Update stable -diffusion-webui-forge

What should have happened?
No tab visible
Commit where the problem happens
webui:
EastPhoto:
What browsers do you use to access the UI ?
Brave
Command Line Arguments
Launching from Stability Matrix

List of enabled extensions

Console logs
Python 3.10.11 (tags/v3.10.11:7d4cc5a, Apr  5 2023, 00:38:17) [MSC v.1929 64 bit (AMD64)]
Version: f1.0.2v1.10.1-previous-37-g3bc9402d
Commit hash: 3bc9402de9f906dd5df504c1f24d88589c2d3074
Installing requirements
C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\extensions\sd-webui-inpaint-background\install.py:2: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
  import pkg_resources
C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\extensions\sd-webui-reactor\install.py:4: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
  import pkg_resources
CUDA 12.1
C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\extensions-builtin\forge_legacy_preprocessors\install.py:2: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
  import pkg_resources
C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\extensions-builtin\sd_forge_controlnet\install.py:2: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
  import pkg_resources
Launching Web UI with arguments: --gradio-allowed-path 'C:\StabilityMatrix\Data\Images'
Total VRAM 24564 MB, total RAM 64729 MB
Set vram state to: NORMAL_VRAM
Device: cuda:0 NVIDIA GeForce RTX 3090 Ti : native
Hint: your device supports --pin-shared-memory for potential speed improvements.
Hint: your device supports --cuda-malloc for potential speed improvements.
Hint: your device supports --cuda-stream for potential speed improvements.
VAE dtype: torch.bfloat16
CUDA Stream Activated:  False
2024-07-29 13:19:18.645936: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-07-29 13:19:19.581243: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
Using pytorch cross attention
ControlNet preprocessor location: C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\models\ControlNetPreprocessor
[-] ADetailer initialized. version: 24.6.0, num models: 19
[sdwi2iextender] Developper warning:
[sdwi2iextender] ./modules/img2img.py is being recompiled at run time with a patch. Your debugger will not work in this file.
[sdwi2iextender] If you need debug tools in this file, disable all extensions that use the sdwi2iextender library.
[sdwi2iextender] This patch is temporary and will be removed when v1.9 will be released.
sd-webui-prompt-all-in-one background API service started successfully.
13:19:30 - ReActor - STATUS - Running v0.7.1-a1 on Device: CUDA
Loading weights [8b886bbe13] from C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\models\Stable-diffusion\mklanRealisticPony_nextgenPonyv1.safetensors
*** Error calling: C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\extensions\sd-webui-ar_xhox\scripts\sd-webui-ar.py/ui
    Traceback (most recent call last):
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\modules\scripts.py", line 545, in wrap_call
        return func(*args, **kwargs)
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\extensions\sd-webui-ar_xhox\scripts\sd-webui-ar.py", line 348, in ui
        btns = [
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\extensions\sd-webui-ar_xhox\scripts\sd-webui-ar.py", line 349, in <listcomp>
        ARButton(ar=ar, value=label)
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\extensions\sd-webui-ar_xhox\scripts\sd-webui-ar.py", line 31, in __init__
        super().__init__(**kwargs)
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\modules\ui_components.py", line 23, in __init__
        super().__init__(*args, elem_classes=["tool", *elem_classes], value=value, **kwargs)
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\modules\gradio_extensions.py", line 126, in __repaired_init__
        original(self, *args, **fixed_kwargs)
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\venv\lib\site-packages\gradio\component_meta.py", line 163, in wrapper
        return fn(self, **kwargs)
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\venv\lib\site-packages\gradio\components\button.py", line 61, in __init__
        super().__init__(
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\modules\gradio_extensions.py", line 35, in IOComponent_init
        res = original_IOComponent_init(self, *args, **kwargs)
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\venv\lib\site-packages\gradio\component_meta.py", line 163, in wrapper
        return fn(self, **kwargs)
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\venv\lib\site-packages\gradio\components\base.py", line 224, in __init__
        self.component_class_id = self.__class__.get_component_class_id()
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\venv\lib\site-packages\gradio\components\base.py", line 118, in get_component_class_id
        module_path = sys.modules[module_name].__file__
    KeyError: 'sd-webui-ar.py'

---
model_type EPS
UNet ADM Dimension 2816
*** Error calling: C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\extensions\sd-webui-ar_xhox\scripts\sd-webui-ar.py/ui
    Traceback (most recent call last):
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\modules\scripts.py", line 545, in wrap_call
        return func(*args, **kwargs)
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\extensions\sd-webui-ar_xhox\scripts\sd-webui-ar.py", line 348, in ui
        btns = [
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\extensions\sd-webui-ar_xhox\scripts\sd-webui-ar.py", line 349, in <listcomp>
        ARButton(ar=ar, value=label)
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\extensions\sd-webui-ar_xhox\scripts\sd-webui-ar.py", line 31, in __init__
        super().__init__(**kwargs)
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\modules\ui_components.py", line 23, in __init__
        super().__init__(*args, elem_classes=["tool", *elem_classes], value=value, **kwargs)
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\modules\gradio_extensions.py", line 126, in __repaired_init__
        original(self, *args, **fixed_kwargs)
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\venv\lib\site-packages\gradio\component_meta.py", line 163, in wrapper
        return fn(self, **kwargs)
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\venv\lib\site-packages\gradio\components\button.py", line 61, in __init__
        super().__init__(
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\modules\gradio_extensions.py", line 35, in IOComponent_init
        res = original_IOComponent_init(self, *args, **kwargs)
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\venv\lib\site-packages\gradio\component_meta.py", line 163, in wrapper
        return fn(self, **kwargs)
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\venv\lib\site-packages\gradio\components\base.py", line 224, in __init__
        self.component_class_id = self.__class__.get_component_class_id()
      File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\venv\lib\site-packages\gradio\components\base.py", line 118, in get_component_class_id
        module_path = sys.modules[module_name].__file__
    KeyError: 'sd-webui-ar.py'

---
2024-07-29 13:19:33,765 - ControlNet - INFO - ControlNet UI callback registered.
Using pytorch attention in VAE
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
Using pytorch attention in VAE
Running on local URL:  http://<IP_ADDRESS>:7860
extra {'cond_stage_model.clip_l.logit_scale', 'cond_stage_model.clip_l.text_projection', 'cond_stage_model.clip_g.transformer.text_model.embeddings.position_ids'}

To create a public link, set `share=True` in `launch()`.
Startup time: 72.4s (prepare environment: 42.3s, launcher: 2.3s, import torch: 5.2s, setup paths: 4.2s, initialize shared: 0.3s, other imports: 0.8s, list SD models: 0.2s, load scripts: 6.9s, create ui: 5.0s, gradio launch: 3.3s, app_started_callback: 1.8s).
To load target model SDXLClipModel
Begin to load 1 model
[Memory Management] Current Free GPU Memory (MB) =  23291.7470703125
[Memory Management] Model Memory (MB) =  2144.3546981811523
[Memory Management] Minimal Inference Memory (MB) =  1024.0
[Memory Management] Estimated Remaining GPU Memory (MB) =  20123.392372131348
Moving model(s) has taken 0.45 seconds
Model loaded in 11.0s (calculate hash: 0.1s, forge instantiate config: 1.4s, forge load real models: 5.1s, forge finalize: 1.2s, load textual inversion embeddings: 2.4s, calculate empty prompt: 0.7s).

Additional information
No response

sd-webui-ar 这个插件  按钮继承 ToolButton>gr.Button
sys.modules[module_name] 中没有 sd-webui-ar 然后报错
gradio 类继承 修改了
我搞两天了也没搞明白

Still fails without sd-webui-ar enabled. There is no EasyPhoto Tab but there is an accordion tab with "Dynamic Lora Weights (EasyPhoto builtin)"
There is an open issue at https://github.com/lllyasviel/stable-diffusion-webui-forge/issues
Python 3.10.11 (tags/v3.10.11:7d4cc5a, Apr  5 2023, 00:38:17) [MSC v.1929 64 bit (AMD64)]
Version: f1.0.2v1.10.1-previous-62-ge3522c89
Commit hash: e3522c89191a01e0dd5855abbfd15cb685be3634
Installing requirements
is_installed check for tensorflow-cpu failed as 'spec is None'
Installing requirements for easyphoto-webui
Installing requirements for tensorflow
C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\extensions\sd-webui-inpaint-background\install.py:2: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
import pkg_resources
C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\extensions\sd-webui-reactor\install.py:4: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
import pkg_resources
CUDA 12.1
C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\extensions-builtin\forge_legacy_preprocessors\install.py:2: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
import pkg_resources
C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\extensions-builtin\sd_forge_controlnet\install.py:2: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
import pkg_resources
Launching Web UI with arguments: --gradio-allowed-path 'C:\StabilityMatrix\Data\Images'
Total VRAM 24564 MB, total RAM 64729 MB
Set vram state to: NORMAL_VRAM
Device: cuda:0 NVIDIA GeForce RTX 3090 Ti : native
Hint: your device supports --pin-shared-memory for potential speed improvements.
Hint: your device supports --cuda-malloc for potential speed improvements.
Hint: your device supports --cuda-stream for potential speed improvements.
VAE dtype: torch.bfloat16
CUDA Stream Activated:  False
2024-08-01 16:37:19.132073: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
2024-08-01 16:37:20.164853: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
Using pytorch cross attention
Using pytorch cross attention
ControlNet preprocessor location: C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\models\ControlNetPreprocessor
[-] ADetailer initialized. version: 24.6.0, num models: 19
2024-08-01 16:37:30,146 - modelscope - INFO - PyTorch version 2.1.2+cu121 Found.
2024-08-01 16:37:30,162 - modelscope - INFO - TensorFlow version 2.16.1 Found.
2024-08-01 16:37:30,162 - modelscope - INFO - Loading ast index from C:\Users\rbrtw.cache\modelscope\ast_indexer
2024-08-01 16:37:30,630 - modelscope - INFO - Loading done! Current index file version is 1.9.3, with md5 e81d9fa8efecccbceabbbe9926052339 and a total number of 943 components indexed
[sdwi2iextender] Developper warning:
[sdwi2iextender] ./modules/img2img.py is being recompiled at run time with a patch. Your debugger will not work in this file.
[sdwi2iextender] If you need debug tools in this file, disable all extensions that use the sdwi2iextender library.
[sdwi2iextender] This patch is temporary and will be removed when v1.9 will be released.
sd-webui-prompt-all-in-one background API service started successfully.
16:37:34 - ReActor - STATUS - Running v0.7.1-a1 on Device: CUDA
Loading weights [8557e300af] from C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\models\Stable-diffusion\afroditexlNSFW_20.safetensors
AnimateDiffScript init
AnimateDiffScript init
Skipped: unet = diffusers.UNet2DConditionModel
Working with z of shape (1, 4, 32, 32) = 4096 dimensions.
model_type EPS
UNet ADM Dimension 2816
2024-08-01 16:37:38,075 - ControlNet - INFO - ControlNet UI callback registered.
*** Error executing callback ui_tabs_callback for C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\extensions\sd-webui-EasyPhoto\scripts\easyphoto_ui.py
Traceback (most recent call last):
File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\modules\script_callbacks.py", line 283, in ui_tabs_callback
res += c.callback() or []
File "C:\StabilityMatrix\Data\Packages\Stable Diffusion WebUI Forge\extensions\sd-webui-EasyPhoto\scripts\easyphoto_ui.py", line 254, in on_ui_tabs
instance_images = gr.Gallery().style(columns=[4], rows=[2], object_fit="contain", height="auto")
AttributeError: 'Gallery' object has no attribute 'style'

Running on local URL:  http://<IP_ADDRESS>:7860
To load target model JointCLIP
Begin to load 1 model
[Memory Management] Current Free GPU Memory (MB) =  23291.7470703125
[Memory Management] Model Memory (MB) =  1753.803913116455
[Memory Management] Minimal Inference Memory (MB) =  1024.0
[Memory Management] Estimated Remaining GPU Memory (MB) =  20513.943157196045
To create a public link, set share=True in launch().
Startup time: 63.0s (prepare environment: 28.1s, launcher: 2.4s, import torch: 5.7s, setup paths: 4.8s, initialize shared: 0.3s, other imports: 1.1s, opts onchange: 0.3s, list SD models: 0.2s, load scripts: 9.5s, create ui: 5.6s, gradio launch: 3.4s, app_started_callback: 1.4s).
Moving model(s) has taken 3.40 seconds
Model loaded in 10.8s (forge instantiate config: 1.6s, forge load real models: 4.5s, load textual inversion embeddings: 0.8s, calculate empty prompt: 3.7s).

The error in the log indicates an issue with the Gallery object in the EasyPhoto extension not having a style attribute. This is likely due to an API change or incompatibility in the Forge upgraded Gradio 4 library.

can some one fix this to get it work for webui forge

https://github.com/skyline9394/webui-forge-EasyPhoto please try got fix the code with chatgpt is only one file have change easyphoto_ui.py download it and replace it restart webui  only work on Gradio 4

I had the same issues. Nothing listed above works.
I installed (then uninstalled) both this original (https://github.com/aigc-apps/sd-webui-EasyPhoto) and then the one for Forge
(https://github.com/skyline9394/webui-forge-EasyPhoto) making sure to carefully following all instructions (although the line above "please try got fix the code with chatgpt is only one file have change" makes no grammatical sense. regardless what I do I still always get the following when it tries to start EasyPhoto (was same errors on both versions).
ControlNet preprocessor location: C:\AI\FORGE\webui_forge_cu121_torch231\webui\models\ControlNetPreprocessor
[-] ADetailer initialized. version: 24.11.0, num models: 10
16:20:36 - ReActor - STATUS - Running v0.7.1-b2 on Device: CUDA
2024-12-06 16:20:36,254 - modelscope - INFO - PyTorch version 2.3.1+cu121 Found.
2024-12-06 16:20:36,261 - modelscope - INFO - TensorFlow version 2.18.0 Found.
2024-12-06 16:20:36,262 - modelscope - INFO - Loading ast index from C:\Users\I345823.cache\modelscope\ast_indexer
2024-12-06 16:20:36,407 - modelscope - INFO - Loading done! Current index file version is 1.9.3, with md5 5e56b36732f547710a7e4a7aeca46404 and a total number of 943 components indexed
*** Error loading script: api.py
Traceback (most recent call last):
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\modules\scripts.py", line 525, in load_scripts
script_module = script_loading.load_module(scriptfile.path)
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\modules\script_loading.py", line 13, in load_module
module_spec.loader.exec_module(module)
File "", line 883, in exec_module
File "", line 241, in call_with_frames_removed
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\extensions\webui-forge-EasyPhoto\scripts\api.py", line 11, in
from scripts.easyphoto_infer import easyphoto_infer_forward, easyphoto_video_infer_forward
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\extensions\webui-forge-EasyPhoto\scripts\easyphoto_infer.py", line 35, in
from scripts.easyphoto_utils import (
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\extensions\webui-forge-EasyPhoto\scripts\easyphoto_utils_init.py", line 51, in
from .loractl_utils import check_loractl_conflict, LoraCtlScript
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\extensions\webui-forge-EasyPhoto\scripts\easyphoto_utils\loractl_utils.py", line 28, in
raise ImportError("Lora extension is not found.")
ImportError: Lora extension is not found.

*** Error loading script: easyphoto_infer.py
Traceback (most recent call last):
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\modules\scripts.py", line 525, in load_scripts
script_module = script_loading.load_module(scriptfile.path)
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\modules\script_loading.py", line 13, in load_module
module_spec.loader.exec_module(module)
File "", line 883, in exec_module
File "", line 241, in call_with_frames_removed
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\extensions\webui-forge-EasyPhoto\scripts\easyphoto_infer.py", line 35, in
from scripts.easyphoto_utils import (
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\extensions\webui-forge-EasyPhoto\scripts\easyphoto_utils_init.py", line 51, in
from .loractl_utils import check_loractl_conflict, LoraCtlScript
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\extensions\webui-forge-EasyPhoto\scripts\easyphoto_utils\loractl_utils.py", line 28, in
raise ImportError("Lora extension is not found.")
ImportError: Lora extension is not found.

*** Error loading script: easyphoto_train.py
Traceback (most recent call last):
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\modules\scripts.py", line 525, in load_scripts
script_module = script_loading.load_module(scriptfile.path)
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\modules\script_loading.py", line 13, in load_module
module_spec.loader.exec_module(module)
File "", line 883, in exec_module
File "", line 241, in _call_with_frames_removed
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\extensions\webui-forge-EasyPhoto\scripts\easyphoto_train.py", line 9, in
from modules.sd_models_config import config_default, config_sdxl
ImportError: cannot import name 'config_default' from 'modules.sd_models_config' (C:\AI\FORGE\webui_forge_cu121_torch231\webui\modules\sd_models_config.py)

*** Error loading script: easyphoto_tryon_infer.py
Traceback (most recent call last):
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\modules\scripts.py", line 525, in load_scripts
script_module = script_loading.load_module(scriptfile.path)
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\modules\script_loading.py", line 13, in load_module
module_spec.loader.exec_module(module)
File "", line 883, in exec_module
File "", line 241, in call_with_frames_removed
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\extensions\webui-forge-EasyPhoto\scripts\easyphoto_tryon_infer.py", line 26, in
from scripts.easyphoto_infer import inpaint
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\extensions\webui-forge-EasyPhoto\scripts\easyphoto_infer.py", line 35, in
from scripts.easyphoto_utils import (
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\extensions\webui-forge-EasyPhoto\scripts\easyphoto_utils_init.py", line 51, in
from .loractl_utils import check_loractl_conflict, LoraCtlScript
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\extensions\webui-forge-EasyPhoto\scripts\easyphoto_utils\loractl_utils.py", line 28, in
raise ImportError("Lora extension is not found.")
ImportError: Lora extension is not found.

*** Error loading script: easyphoto_ui.py
Traceback (most recent call last):
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\modules\scripts.py", line 525, in load_scripts
script_module = script_loading.load_module(scriptfile.path)
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\modules\script_loading.py", line 13, in load_module
module_spec.loader.exec_module(module)
File "", line 883, in exec_module
File "", line 241, in _call_with_frames_removed
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\extensions\webui-forge-EasyPhoto\scripts\easyphoto_ui.py", line 9, in
from modules.sd_models_config import config_default, config_sdxl
ImportError: cannot import name 'config_default' from 'modules.sd_models_config' (C:\AI\FORGE\webui_forge_cu121_torch231\webui\modules\sd_models_config.py)

*** Error loading script: sdwebui.py
Traceback (most recent call last):
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\modules\scripts.py", line 525, in load_scripts
script_module = script_loading.load_module(scriptfile.path)
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\modules\script_loading.py", line 13, in load_module
module_spec.loader.exec_module(module)
File "", line 883, in exec_module
File "", line 241, in call_with_frames_removed
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\extensions\webui-forge-EasyPhoto\scripts\sdwebui.py", line 21, in
from scripts.easyphoto_utils import (
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\extensions\webui-forge-EasyPhoto\scripts\easyphoto_utils_init.py", line 51, in
from .loractl_utils import check_loractl_conflict, LoraCtlScript
File "C:\AI\FORGE\webui_forge_cu121_torch231\webui\extensions\webui-forge-EasyPhoto\scripts\easyphoto_utils\loractl_utils.py", line 28, in
raise ImportError("Lora extension is not found.")
ImportError: Lora extension is not found.

Loading additional modules ... done.
2024-12-06 16:20:49,646 - ControlNet - INFO - ControlNet UI callback registered.
Model selected: {'checkpoint_info': {'filename': 'C:\AI\FORGE\webui_forge_cu121_torch231\webui\models\Stable-diffusion\PONY\sevenof9PonyRealMix_theBest.safetensors', 'hash': '17a76172'}, 'additional_modules': [], 'unet_storage_dtype': None}
Using online LoRAs in FP16: False
Running on local URL:  http://<IP_ADDRESS>:7860
To create a public link, set share=True in launch().

Environment vars changed: {'stream': False, 'inference_memory': 1024.0, 'pin_shared_memory': False}
[GPU Setting] You will use 93.75% GPU memory (15359.00 MB) to load weights, and use 6.25% GPU memory (1024.00 MB) to do matrix computation.


[Instrumentation] - Add samples

Once we have a package upgraded to use @azure/core-tracing >= preview.14 we can add a sample that demonstrates how to enable tracing using the @azure/opentelemetry-instrumentation-azure-sdk package. Until then, any sample we add won't be meaningful and won't be demonstrating an end-to-end scenario that works.

Make sure to remove the text matrix filter added here: https://github.com/Azure/azure-sdk-for-js/pull/19855/files#diff-1e748e5e9dfdc5f6f0d7123b19ac1c9a2909a257c55b19411056e845c75e4720R8-R9


fix(datastore-v2): InitializeSubscription race condition disconnected event

Issue #, if available:
Description of changes:
This PR is the V2 version of https://github.com/aws-amplify/amplify-ios/pull/2319
Check points: (check or cross out if not relevant)

[ ] Added new tests to cover change, if needed
[ ] Build succeeds with all target using Swift Package Manager
[ ] All unit tests pass
[ ] All integration tests pass
[ ] Security oriented best practices and standards are followed (e.g. using input sanitization, principle of least privilege, etc)
[ ] Documentation update for the change if required
[ ] PR title conforms to conventional commit style
[ ] If breaking change, documentation/changelog update with migration instructions

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.

Running through the unit / integration tests and these are the following that need to be fixed:

OutgoingMutationQueueNetworkTests/testLastMutationSentWhenNoNetwork() - has been hitting us a couple of times now

The macOS tests that are failing should not be blocking this PR since they were failing before and recently enabled in the pipeline.

Opening an issue to track this


Link address not found on Using with Spring Boot page

Page
/docs/solutions/spring-boot/
Details
link is not available
https://wiremock.org/3.x/docs/solutions/solutions/jvm
for the useful pages area
Suggested Edits
Provide a valid link
References
https://wiremock.org/3.x/docs/solutions/spring-boot/

I would like to take this up!


Questions about Eq. 3

Hi Yu,
Thank you for your detailed analysis and the commendable work. I have a question regarding Eq. 3 in the paper and how it relates to the implementation in the code.

Above is Eq. 3 in the paper.
The following is what I understood from the code:

The cur_adj before the while loop is assigned to the first_adj variable. It appears that first_adj represents
$$\lambda L^{(0)} + (1 - \lambda)f(A^{(1)})$$ where $A^{(1)}$ is calculated from the initial node features init_node_vec($X$). Refer to this line, where graph_skip_conn is $\lambda$, init_adj is $L^{(0)}$, and adj is $f(A^{(t)})$.

Similarly, the cur_adj in the 'while' loop represents $\lambda L^{(0)} + (1 - \lambda)f(A^{(t)})$.

Next, the above cur_adj is updated according to  $\eta$cur_adj$+ (1-\eta)$first_adj.

In conclusion, $\tilde{A}^{(t)}$ is generated according to $\tilde{A}^{(t)} = \eta[\lambda L^{(0)} + (1 - \lambda)f(A^{(t)})]+ (1-\eta)[\lambda L^{(0)} + (1 - \lambda)f(A^{(1)})]$.

If my interpretation is correct, this update strategy also accounts for the influence of $L^{(0)}$ and $A^{(1)}$, although it slightly deviates from Eq. 3 in the paper.  Could you please clarify if this deviation is intentional and why?

Apologies for the oversight. With just a bit of derivation, it becomes clear that these two are equivalent.


Reading too few bytes

I'm trying to read from a serial connection to a RS485 bus and the messages are 6 bytes in length. But for some reason I end up only getting 5 bytes out from the serial connection at a time. I tried using a Python library to quickly test the connection out and that seems to properly get me 6 bytes, but that also has a parameter for the Read function that can specify how many bytes should be read. Is this possible with this library? And how does it determine when it has read enough bytes from the serial connection and returns from Read?

Ah, actually it looks like I'm alternating between reading 5 bytes and 1 byte.  So I suppose I just need to do it myself and read until the 6 bytes have been read and then use the read values. It would be nice to get a Read(n, buff) function though, where you could say that read n bytes

Maybe try using this? https://golang.org/pkg/io/#ReadAtLeast

Ah, nice! I really need to start reading the docs for all the standard libraries more. Seems to work! Thanks.
I already did this to solve this issue earlier, but this is much cleaner
total := 0
for total < vxMsgLength {
      n, err := c.port.Read(buff[total:])
      if err != nil {
              log.Fatal().Err(err).Msg("Error while reading from serial connection")
      }
      total += n
}

Closing as not a bug.


Nodes render below links (Z axis)

Simple change: swap the order of nodes and links in Tree's render method. This causes links to appear below nodes.

Coverage remained the same at 92.935% when pulling baeb4aaf1a534db21f53e916f43381f1a0d663d4 on justinsisley:fix/26 into 7477fd0796527ba646b63c04c5bd368786c373bc on bkrem:master.


FIX: 'parser_trim_buffers' properly initializes word pointers

Summary
The pull request:

[x] closes #13703;
[x] passes all tests in test.sh except one (see the output);
[x] passes the code style test git diff upstream/master | flake8 --diff;

Details
Basically the function parse_trim_buffers did not properly move word pointers in parser->words and related fields. This pull request aims at fixing that.
Changes in parse_trim_buffers:

block L1239 -- L1256 (/* trim stream */) was augmented with parser->words update loop;
blocks L1224 -- L1237 (/* trim words, word_starts */) and L1239 -- L1256(/* trim stream */) were swapped to preserve pointer consistency;

Output of test.sh:
======================================================================
FAIL: test_round_trip_frame_sep (pandas.io.tests.test_clipboard.TestClipboard)
----------------------------------------------------------------------
Traceback (most recent call last):
  File "/Users/ivannz/Github/pd_fork/pandas/pandas/io/tests/test_clipboard.py", line 73, in test_round_trip_frame_sep
    self.check_round_trip_frame(dt, sep=',')
  File "/Users/ivannz/Github/pd_fork/pandas/pandas/io/tests/test_clipboard.py", line 69, in check_round_trip_frame
    tm.assert_frame_equal(data, result, check_dtype=False)
  File "/Users/ivannz/Github/pd_fork/pandas/pandas/util/testing.py", line 1276, in assert_frame_equal
    right.columns))
  File "/Users/ivannz/Github/pd_fork/pandas/pandas/util/testing.py", line 1022, in raise_assert_detail
    raise AssertionError(msg)
AssertionError: DataFrame are different

DataFrame shape (number of columns) are different
[left]:  2, Index([u'en', u'es'], dtype='object')
[right]: 0, Index([], dtype='object')

----------------------------------------------------------------------
Ran 10377 tests in 1197.728s

FAILED (SKIP=363, failures=1)

Apart from this, there were a couple of deprecation warnings in files other that tokenizer.c.

can you add a test that reproduces the segfault (and this PR fixes)

this is going to need a run of the asv suite for the csv benchmarks to see if anything changed. see here

[Current coverage][cc-pull] is 85.33% (diff: 100%)

Merging [#13788][cc-pull] into [master][cc-base-branch] will increase coverage by <.01%

@@             master     #13788   diff @@
==========================================
  Files           141        141
  Lines         50711      50703     -8
  Methods           0          0
  Messages          0          0
  Branches          0          0
==========================================
- Hits          43272      43267     -5
+ Misses         7439       7436     -3
  Partials          0          0

Powered by Codecov. Last update [4dd734c...66c6b5d][cc-compare]
[cc-base-branch]: https://codecov.io/gh/pydata/pandas/branch/master?src=pr
[cc-compare]: https://codecov.io/gh/pydata/pandas/compare/4dd734c9c3f38ddc19fa77fea71abfb6c1f3f2ba...66c6b5dad3eac1449ecfee017c00b89b7f2f602c?src=pr
[cc-pull]: https://codecov.io/gh/pydata/pandas/pull/13788?src=pr

I ran the ASV benchmarks. It says
[ 12.88%] ··· Running gil.nogil_read_csv.time_nogil_read_csv                                                                                327.76ms
[ 12.88%] ··· Running gil.nogil_read_csv.time_nogil_read_csv                                                                                327.76ms
[ 12.94%] ··· Running gil.nogil_read_csv.time_nogil_read_csv_datetime                                                                                326.62ms
[ 13.00%] ··· Running gil.nogil_read_csv.time_nogil_read_csv_object                                                                                18.14ms
[ 30.96%] ··· Running io_bench.read_csv_from_s3.time_read_nrows                                                                                n/a;...
[ 31.02%] ··· Running io_bench.read_csv_infer_datetime_format_custom.time_read_csv_infer_datetime_format_custom                                        11.53ms
[ 31.08%] ··· Running io_bench.read_csv_infer_datetime_format_iso8601.time_read_csv_infer_datetime_format_iso8601                                       2.07ms
[ 31.14%] ··· Running io_bench.read_csv_infer_datetime_format_ymd.time_read_csv_infer_datetime_format_ymd                                               2.13ms
[ 31.20%] ··· Running io_bench.read_csv_skiprows.time_read_csv_skiprows                                                                                12.84ms
[ 31.26%] ··· Running io_bench.read_csv_standard.time_read_csv_standard                                                                                11.19ms
[ 34.46%] ··· Running packers.packers_read_csv.time_packers_read_csv                                                                                180.63ms
[ 36.88%] ··· Running parser_vb.read_csv_comment2.time_read_csv_comment2                                                                               25.18ms
[ 36.94%] ··· Running parser_vb.read_csv_default_converter.time_read_csv_default_converter                                                              1.94ms
[ 37.00%] ··· Running parser_vb.read_csv_default_converter_python_engine.time_read_csv_default_converter                                                3.26ms
[ 37.06%] ··· Running parser_vb.read_csv_default_converter_with_decimal.time_read_csv_default_converter_with_decimal                                    1.98ms
[ 37.12%] ··· Running parser_vb.read_csv_default_converter_with_decimal_python_engine.time_read_csv_default_converter_with_decimal                      9.93ms
[ 37.18%] ··· Running parser_vb.read_csv_precise_converter.time_read_csv_precise_converter                                                              1.91ms
[ 37.24%] ··· Running parser_vb.read_csv_roundtrip_converter.time_read_csv_roundtrip_converter                                                          2.61ms
[ 37.30%] ··· Running parser_vb.read_csv_thou_vb.time_read_csv_thou_vb                                                                                25.74ms
[ 37.36%] ··· Running parser_vb.read_csv_vb.time_read_csv_vb                                                                                21.59ms
[ 62.88%] ··· Running gil.nogil_read_csv.time_nogil_read_csv                                                                                321.78ms
[ 62.94%] ··· Running gil.nogil_read_csv.time_nogil_read_csv_datetime                                                                                322.15ms
[ 63.00%] ··· Running gil.nogil_read_csv.time_nogil_read_csv_object                                                                                16.19ms
[ 80.96%] ··· Running io_bench.read_csv_from_s3.time_read_nrows                                                                                n/a;...
[ 81.02%] ··· Running io_bench.read_csv_infer_datetime_format_custom.time_read_csv_infer_datetime_format_custom                                        11.52ms
[ 81.08%] ··· Running io_bench.read_csv_infer_datetime_format_iso8601.time_read_csv_infer_datetime_format_iso8601                                       2.03ms
[ 81.14%] ··· Running io_bench.read_csv_infer_datetime_format_ymd.time_read_csv_infer_datetime_format_ymd                                               2.10ms
[ 81.20%] ··· Running io_bench.read_csv_skiprows.time_read_csv_skiprows                                                                                12.41ms
[ 81.26%] ··· Running io_bench.read_csv_standard.time_read_csv_standard                                                                                10.81ms
[ 84.46%] ··· Running packers.packers_read_csv.time_packers_read_csv                                                                                171.84ms
[ 86.88%] ··· Running parser_vb.read_csv_comment2.time_read_csv_comment2                                                                               25.49ms
[ 86.94%] ··· Running parser_vb.read_csv_default_converter.time_read_csv_default_converter                                                              1.87ms
[ 87.00%] ··· Running parser_vb.read_csv_default_converter_python_engine.time_read_csv_default_converter                                                3.26ms
[ 87.06%] ··· Running parser_vb.read_csv_default_converter_with_decimal.time_read_csv_default_converter_with_decimal                                    1.80ms
[ 87.12%] ··· Running parser_vb.read_csv_default_converter_with_decimal_python_engine.time_read_csv_default_converter_with_decimal                      9.94ms
[ 87.18%] ··· Running parser_vb.read_csv_precise_converter.time_read_csv_precise_converter                                                              1.74ms
[ 87.24%] ··· Running parser_vb.read_csv_roundtrip_converter.time_read_csv_roundtrip_converter                                                          2.42ms
[ 87.30%] ··· Running parser_vb.read_csv_thou_vb.time_read_csv_thou_vb                                                                                25.13ms
[ 87.36%] ··· Running parser_vb.read_csv_vb.time_read_csv_vb                                                                                20.23ms

...

BENCHMARKS NOT SIGNIFICANTLY CHANGED.

The full results are in the attached archive.
results.tar.gz

I added a test that check for either segfault, or memory corruption during parsing.

@ivannz You pulled in some changes from other PRs that have been merged recently. Can you rebase to fix this? Normally
git fetch upstream
git rebase upstream/master
git push -f origin parser_trim_fix

should do the trick

For the asv benchmarks, you will need to compare to current master to see if anything changed. In fact, we just did merge a pull request to clarify how to do this (https://github.com/pydata/pandas/pull/13794):
asv continuous upstream/master HEAD -b csv

tests are just like any other parser test
put in io/tests/parser/common.py

I had to update the stressfulness of the test, because sometimes safe_realloc just expands the parser->stream buffer, which does not corrupt the pointers in parser->words.

Here are the results of asv continuous -E virtualenv upstream/master HEAD -b csv:
Running 50 total benchmarks (2 commits * 1 environments * 25 benchmarks)
[  0.00%] · For pandas commit hash c092c2b0:
[  2.00%] ··· Running gil.nogil_read_csv.time_nogil_read_csv                                                                                330.53ms
[  4.00%] ··· Running gil.nogil_read_csv.time_nogil_read_csv_datetime                                                                                331.21ms
[  6.00%] ··· Running gil.nogil_read_csv.time_nogil_read_csv_object                                                                                16.88ms
[  8.00%] ··· Running io_bench.frame_to_csv.time_frame_to_csv                                                                                54.20ms
[ 10.00%] ··· Running io_bench.frame_to_csv2.time_frame_to_csv2                                                                                107.84ms
[ 12.00%] ··· Running io_bench.frame_to_csv_date_formatting.time_frame_to_csv_date_formatting                                                                                13.89ms
[ 14.00%] ··· Running io_bench.frame_to_csv_mixed.time_frame_to_csv_mixed                                                                                180.47ms
[ 16.00%] ··· Running io_bench.read_csv_from_s3.time_read_nrows                                                                                n/a;...
[ 18.00%] ··· Running io_bench.read_csv_infer_datetime_format_custom.time_read_csv_infer_datetime_format_custom                                                                              11.98ms
[ 20.00%] ··· Running io_bench.read_csv_infer_datetime_format_iso8601.time_read_csv_infer_datetime_format_iso8601                                                                             2.15ms
[ 22.00%] ··· Running io_bench.read_csv_infer_datetime_format_ymd.time_read_csv_infer_datetime_format_ymd                                                                                2.15ms
[ 24.00%] ··· Running io_bench.read_csv_skiprows.time_read_csv_skiprows                                                                                13.10ms
[ 26.00%] ··· Running io_bench.read_csv_standard.time_read_csv_standard                                                                                11.36ms
[ 28.00%] ··· Running io_bench.write_csv_standard.time_write_csv_standard                                                                                22.73ms
[ 30.00%] ··· Running packers.packers_read_csv.time_packers_read_csv                                                                                187.62ms
[ 32.00%] ··· Running packers.packers_write_csv.time_packers_write_csv                                                                                625.05ms
[ 34.00%] ··· Running parser_vb.read_csv_comment2.time_read_csv_comment2                                                                                26.11ms
[ 36.00%] ··· Running parser_vb.read_csv_default_converter.time_read_csv_default_converter                                                                                1.97ms
[ 38.00%] ··· Running parser_vb.read_csv_default_converter_python_engine.time_read_csv_default_converter                                                                                3.33ms
[ 40.00%] ··· Running parser_vb.read_csv_default_converter_with_decimal.time_read_csv_default_converter_with_decimal                                                                          1.98ms
[ 42.00%] ··· Running parser_vb.read_csv_default_converter_with_decimal_python_engine.time_read_csv_default_converter_with_decimal                                                           10.16ms
[ 44.00%] ··· Running parser_vb.read_csv_precise_converter.time_read_csv_precise_converter                                                                                1.91ms
[ 46.00%] ··· Running parser_vb.read_csv_roundtrip_converter.time_read_csv_roundtrip_converter                                                                                2.67ms
[ 48.00%] ··· Running parser_vb.read_csv_thou_vb.time_read_csv_thou_vb                                                                                23.23ms
[ 50.00%] ··· Running parser_vb.read_csv_vb.time_read_csv_vb                                                                                22.11ms

[ 50.00%] · For pandas commit hash 690d52cf:
[ 52.00%] ··· Running gil.nogil_read_csv.time_nogil_read_csv                                                                                321.53ms
[ 54.00%] ··· Running gil.nogil_read_csv.time_nogil_read_csv_datetime                                                                                323.61ms
[ 56.00%] ··· Running gil.nogil_read_csv.time_nogil_read_csv_object                                                                                20.18ms
[ 58.00%] ··· Running io_bench.frame_to_csv.time_frame_to_csv                                                                                53.05ms
[ 60.00%] ··· Running io_bench.frame_to_csv2.time_frame_to_csv2                                                                                110.65ms
[ 62.00%] ··· Running io_bench.frame_to_csv_date_formatting.time_frame_to_csv_date_formatting                                                                                13.39ms
[ 64.00%] ··· Running io_bench.frame_to_csv_mixed.time_frame_to_csv_mixed                                                                                182.09ms
[ 66.00%] ··· Running io_bench.read_csv_from_s3.time_read_nrows                                                                                n/a;...
[ 68.00%] ··· Running io_bench.read_csv_infer_datetime_format_custom.time_read_csv_infer_datetime_format_custom                                                                              12.03ms
[ 70.00%] ··· Running io_bench.read_csv_infer_datetime_format_iso8601.time_read_csv_infer_datetime_format_iso8601                                                                             2.11ms
[ 72.00%] ··· Running io_bench.read_csv_infer_datetime_format_ymd.time_read_csv_infer_datetime_format_ymd                                                                                2.19ms
[ 74.00%] ··· Running io_bench.read_csv_skiprows.time_read_csv_skiprows                                                                                13.24ms
[ 76.00%] ··· Running io_bench.read_csv_standard.time_read_csv_standard                                                                                11.19ms
[ 78.00%] ··· Running io_bench.write_csv_standard.time_write_csv_standard                                                                                22.07ms
[ 80.00%] ··· Running packers.packers_read_csv.time_packers_read_csv                                                                                175.88ms
[ 82.00%] ··· Running packers.packers_write_csv.time_packers_write_csv                                                                                638.85ms
[ 84.00%] ··· Running parser_vb.read_csv_comment2.time_read_csv_comment2                                                                                27.93ms
[ 86.00%] ··· Running parser_vb.read_csv_default_converter.time_read_csv_default_converter                                                                                1.85ms
[ 88.00%] ··· Running parser_vb.read_csv_default_converter_python_engine.time_read_csv_default_converter                                                                                3.38ms
[ 90.00%] ··· Running parser_vb.read_csv_default_converter_with_decimal.time_read_csv_default_converter_with_decimal                                                                          1.91ms
[ 92.00%] ··· Running parser_vb.read_csv_default_converter_with_decimal_python_engine.time_read_csv_default_converter_with_decimal                                                           10.13ms
[ 94.00%] ··· Running parser_vb.read_csv_precise_converter.time_read_csv_precise_converter                                                                                1.77ms
[ 96.00%] ··· Running parser_vb.read_csv_roundtrip_converter.time_read_csv_roundtrip_converter                                                                                2.52ms
[ 98.00%] ··· Running parser_vb.read_csv_thou_vb.time_read_csv_thou_vb                                                                                24.98ms
[100.00%] ··· Running parser_vb.read_csv_vb.time_read_csv_vb                                                                                24.66ms
BENCHMARKS NOT SIGNIFICANTLY CHANGED.

Complete results are in this archive:
results.tar.gz

pls add a note in whatsnew / bug fix section

@jreback , I rewrote the test as you suggested. It is a problem if nosetests does not recover from segfault when run on it?

@ivannz the whole point is for it NOT to recover. A segfault is as noticiable as any other error. If you have a test that segfaults, and it is fixed, it will pass.

Here are the latest results of asv continuous -E virtualenv upstream/master HEAD -b csv
· Running 50 total benchmarks (2 commits * 1 environments * 25 benchmarks)
[  0.00%] · For pandas commit hash c715ec76:
[  2.00%] ··· Running gil.nogil_read_csv.time_nogil_read_csv                                                                                332.52ms
[  4.00%] ··· Running gil.nogil_read_csv.time_nogil_read_csv_datetime                                                                                336.29ms
[  6.00%] ··· Running gil.nogil_read_csv.time_nogil_read_csv_object                                                                                17.21ms
[  8.00%] ··· Running io_bench.frame_to_csv.time_frame_to_csv                                                                                53.66ms
[ 10.00%] ··· Running io_bench.frame_to_csv2.time_frame_to_csv2                                                                                107.95ms
[ 12.00%] ··· Running io_bench.frame_to_csv_date_formatting.time_frame_to_csv_date_formatting                                                                                14.02ms
[ 14.00%] ··· Running io_bench.frame_to_csv_mixed.time_frame_to_csv_mixed                                                                                185.26ms
[ 16.00%] ··· Running io_bench.read_csv_from_s3.time_read_nrows                                                                                n/a;...
[ 18.00%] ··· Running io_bench.read_csv_infer_datetime_format_custom.time_read_csv_infer_datetime_format_custom                                                                              11.95ms
[ 20.00%] ··· Running io_bench.read_csv_infer_datetime_format_iso8601.time_read_csv_infer_datetime_format_iso8601                                                                             2.14ms
[ 22.00%] ··· Running io_bench.read_csv_infer_datetime_format_ymd.time_read_csv_infer_datetime_format_ymd                                                                                2.17ms
[ 24.00%] ··· Running io_bench.read_csv_skiprows.time_read_csv_skiprows                                                                                13.40ms
[ 26.00%] ··· Running io_bench.read_csv_standard.time_read_csv_standard                                                                                11.43ms
[ 28.00%] ··· Running io_bench.write_csv_standard.time_write_csv_standard                                                                                25.87ms
[ 30.00%] ··· Running packers.packers_read_csv.time_packers_read_csv                                                                                178.49ms
[ 32.00%] ··· Running packers.packers_write_csv.time_packers_write_csv                                                                                629.38ms
[ 34.00%] ··· Running parser_vb.read_csv_comment2.time_read_csv_comment2                                                                                26.11ms
[ 36.00%] ··· Running parser_vb.read_csv_default_converter.time_read_csv_default_converter                                                                                1.98ms
[ 38.00%] ··· Running parser_vb.read_csv_default_converter_python_engine.time_read_csv_default_converter                                                                                3.35ms
[ 40.00%] ··· Running parser_vb.read_csv_default_converter_with_decimal.time_read_csv_default_converter_with_decimal                                                                          2.01ms
[ 42.00%] ··· Running parser_vb.read_csv_default_converter_with_decimal_python_engine.time_read_csv_default_converter_with_decimal                                                           10.10ms
[ 44.00%] ··· Running parser_vb.read_csv_precise_converter.time_read_csv_precise_converter                                                                                1.88ms
[ 46.00%] ··· Running parser_vb.read_csv_roundtrip_converter.time_read_csv_roundtrip_converter                                                                                2.69ms
[ 48.00%] ··· Running parser_vb.read_csv_thou_vb.time_read_csv_thou_vb                                                                                22.01ms
[ 50.00%] ··· Running parser_vb.read_csv_vb.time_read_csv_vb                                                                                25.23ms

[ 50.00%] · For pandas commit hash 98c5b88d:
[ 52.00%] ··· Running gil.nogil_read_csv.time_nogil_read_csv                                                                                319.35ms
[ 54.00%] ··· Running gil.nogil_read_csv.time_nogil_read_csv_datetime                                                                                318.53ms
[ 56.00%] ··· Running gil.nogil_read_csv.time_nogil_read_csv_object                                                                                18.57ms
[ 58.00%] ··· Running io_bench.frame_to_csv.time_frame_to_csv                                                                                55.03ms
[ 60.00%] ··· Running io_bench.frame_to_csv2.time_frame_to_csv2                                                                                103.54ms
[ 62.00%] ··· Running io_bench.frame_to_csv_date_formatting.time_frame_to_csv_date_formatting                                                                                13.35ms
[ 64.00%] ··· Running io_bench.frame_to_csv_mixed.time_frame_to_csv_mixed                                                                                180.89ms
[ 66.00%] ··· Running io_bench.read_csv_from_s3.time_read_nrows                                                                                n/a;...
[ 68.00%] ··· Running io_bench.read_csv_infer_datetime_format_custom.time_read_csv_infer_datetime_format_custom                                                                              11.53ms
[ 70.00%] ··· Running io_bench.read_csv_infer_datetime_format_iso8601.time_read_csv_infer_datetime_format_iso8601                                                                             2.05ms
[ 72.00%] ··· Running io_bench.read_csv_infer_datetime_format_ymd.time_read_csv_infer_datetime_format_ymd                                                                                2.09ms
[ 74.00%] ··· Running io_bench.read_csv_skiprows.time_read_csv_skiprows                                                                                12.41ms
[ 76.00%] ··· Running io_bench.read_csv_standard.time_read_csv_standard                                                                                11.40ms
[ 78.00%] ··· Running io_bench.write_csv_standard.time_write_csv_standard                                                                                22.19ms
[ 80.00%] ··· Running packers.packers_read_csv.time_packers_read_csv                                                                                175.62ms
[ 82.00%] ··· Running packers.packers_write_csv.time_packers_write_csv                                                                                603.39ms
[ 84.00%] ··· Running parser_vb.read_csv_comment2.time_read_csv_comment2                                                                                25.62ms
[ 86.00%] ··· Running parser_vb.read_csv_default_converter.time_read_csv_default_converter                                                                                1.79ms
[ 88.00%] ··· Running parser_vb.read_csv_default_converter_python_engine.time_read_csv_default_converter                                                                                3.26ms
[ 90.00%] ··· Running parser_vb.read_csv_default_converter_with_decimal.time_read_csv_default_converter_with_decimal                                                                          1.83ms
[ 92.00%] ··· Running parser_vb.read_csv_default_converter_with_decimal_python_engine.time_read_csv_default_converter_with_decimal                                                           10.13ms
[ 94.00%] ··· Running parser_vb.read_csv_precise_converter.time_read_csv_precise_converter                                                                                1.74ms
[ 96.00%] ··· Running parser_vb.read_csv_roundtrip_converter.time_read_csv_roundtrip_converter                                                                                2.44ms
[ 98.00%] ··· Running parser_vb.read_csv_thou_vb.time_read_csv_thou_vb                                                                                21.64ms
[100.00%] ··· Running parser_vb.read_csv_vb.time_read_csv_vb                                                                                24.61ms

BENCHMARKS NOT SIGNIFICANTLY CHANGED.

I am not sure how to concisely put it for a general user, but whether the bug occurs, and its severity (a segfault or memory corruption) depends on the state of the system memory allocator. Specifically it is the result of the interplay between the inner logics of realloc implemetnation and the availability of memory regions adjacent to the realloced buffer:

sometimes realloc just expands the buffer, leaving its starting address intact -- in this case no bug takes place;
other times realloc moves a buffer to a new location, and in this case there are two alternatives:

the old block is invalidated, but is still accessible to the parser, in which case the tokenized in parser->words becomes corrupted;
the old block is invalidated and reclaimed be the OS, in which case the process crashes with a segfault.

The first alternative manifests itself in NaNs, or otherwise unacceptable ASCII characters (beyond 128) in fields where no such thing is possible by design (BCD or textual decimal fields).

ok @ivannz just say that on large chunking may segfault then.

Is there a way to cancel all but the most recent Travis job related to this PR (13788)?

@ivannz I canceled some builds (but there are some other PRs in the queue as well, so will take some time anyway). Only solution is to not push a minor commit yet if you know you are going to push another one.
BTW, you can set up travis for your own fork, which often gives you much quicker feedback, and there you yourself have control over the builds (eg to cancel them).

Thank you, @jorisvandenbossche !

I significantly improved the clarity and logic of the segfault/corruption test: now it is a bit more clear why and in what case the bug occurs.

@jorisvandenbossche , could you cancel Travis CI job #21105, please.

thanks!

I tried to compile master branch on Windows VS2015 and it failed with the errors:
pandas/src/parser/tokenizer.c(1260): error C2036: 'void *': unknown size
pandas/src/parser/tokenizer.c(1264): error C2036: 'void *': unknown size

So I guess newptr in these two lines should be cast to char *.

can u make a new issue? and fix if u can

OK. I'll later submit the fix.


fix invalid suggestions when parsing of argument fails

The current suggestion provider for dynamic arguments adds the suggestions of a failed parsed argument as well when stepping over it. This can for example be triggered when an integer argument is required, but a wrong value is provided. Consider the following case: some_command <number <enum>. When the user inputs something like some_command world  and tries to tab complete, the suggestions for the number argument show up. This is caused by a failed parse of the number argument, which doesn't step out of the current suggestion process for the argument, but rather continues to add all suggestions for it.
To fix this behaviour, we need to break the completion process if either the preprocessor or the parser isn't accepting the input and more arguments are provided.

One thing I just tough of is that with this change the suggestion processor now pretty much relies on the fact that people are using the api correctly. While there shouldn't be any changes made to the command input queue during the (pre-) process, the whole check & further argument tries will fail if users are changing the queue.
I will change that later, so that the queue gets reset in case of a (pre-) processing failure.

Valid argument:

Invalid argument:


Certain tables not loading data

I don't have many details on how to reproduce this, so this is more for logging purposes than anything else.
I'm having issues loading a specific table, regardless of the method.
Notes on the table:

Some fields (at different levels) have the same names
It is highly nested
It contains very few records (412 entries)
Running on full refresh did not fix this issue
Will update with details as they come in

Data from same source but different stream is functionnal

Update: Here is an error message using these configs:
      credentials_path: .secrets/gbq-client_secrets-staging.json
      project: target_bigquery_project_id
      dataset: hibob_z3z1ma
      denormalized: false
      method: streaming_insert
      # method: batch
      timeout: 300
      batch_size: 250000
      append_columns: true
      generate_view: true

2022-12-01T20:09:43.893979Z [info     ]   File "/project/.meltano/loaders/target-bigquery/venv/lib/python3.8/site-packages/target_bigquery/utils.py", line 109, in <listcomp> cmd_type=elb consumer=True name=target-bigquery producer=False stdio=stderr string_id=target-bigquery
2022-12-01T20:09:43.894101Z [info     ]     self._field_to_sql(f, f"{path}.{field.name}", array_depth) cmd_type=elb consumer=True name=target-bigquery producer=False stdio=stderr string_id=target-bigquery
2022-12-01T20:09:43.894224Z [info     ]   File "/project/.meltano/loaders/target-bigquery/venv/lib/python3.8/site-packages/target_bigquery/utils.py", line 108, in _field_to_sql cmd_type=elb consumer=True name=target-bigquery producer=False stdio=stderr string_id=target-bigquery
2022-12-01T20:09:43.894347Z [info     ]     [                          cmd_type=elb consumer=True name=target-bigquery producer=False stdio=stderr string_id=target-bigquery
2022-12-01T20:09:43.894468Z [info     ]   File "/project/.meltano/loaders/target-bigquery/venv/lib/python3.8/site-packages/target_bigquery/utils.py", line 111, in <listcomp> cmd_type=elb consumer=True name=target-bigquery producer=False stdio=stderr string_id=target-bigquery
2022-12-01T20:09:43.894590Z [info     ]     else self._wrap_repeat_json_extract(f, array_depth + 1) cmd_type=elb consumer=True name=target-bigquery producer=False stdio=stderr string_id=target-bigquery
2022-12-01T20:09:43.894712Z [info     ] TypeError: _wrap_repeat_json_extract() takes 2 positional arguments but 3 were given cmd_type=elb consumer=True name=target-bigquery producer=False stdio=stderr string_id=target-bigquery
2022-12-01T20:09:43.999884Z [error    ]

It seems we are having an array depth error in utils


Upgrade to a newer version of bitvec

Any prevision of when the crate will be updated to a newer version of bitvec?

I would need this too, because when I try to run "cargo tarpaulin" on my crate (using nom) with Rust 1.52.1, I have the following error:
error: Broken pipe (or error 32)
error: could not compile `bitvec`
ERROR: cargo_tapaulin: Failed to compile tests! Error: cannot find attribute `skip` in this scope

and I hope (let me hope) that it will fix this issue :)

Same issue with the tarpaulin here.

I would need this too, because when I try to run "cargo tarpaulin" on my crate (using nom) with Rust 1.52.1, I have the following error:
error: Broken pipe (or error 32)
error: could not compile `bitvec`
ERROR: cargo_tapaulin: Failed to compile tests! Error: cannot find attribute `skip` in this scope

and I hope (let me hope) that it will fix this issue :)

@orhun seems to be a Tarpaulin issue, in fact... https://github.com/xd009642/tarpaulin/issues/756

erf, that tarpaulin issue is caused by nom https://github.com/xd009642/tarpaulin/issues/756#issuecomment-834249299
and appears in nom's CI too https://github.com/Geal/nom/runs/2865070181?check_suite_focus=true

This doesn't happen for me on wsl2 but it's happening with actions-rs on github actions. Anyone know how to fix?

bitvec input types are now in the nom-bitvec crate, since the release of nom 7: https://crates.io/crates/nom/7.0.0


skill id does not exist

Just got this in the skills.log:
2021-12-07 22:42:46,007 ERROR mycroft.skills.intent_service:handle_converse_error:193 mycroft-sonos-controller-skill.smartgic: skill id does not exist

It never happened again and I'm not able to reproduce it.


Auto merge failed ,adding coingeckoid in extensions #17965

I'm submitting a ...
[* ] bug report
[ ] feature request
[ ] question about the decisions made in the repository
[ ] question about how to use this project

Summary

Other information (e.g. detailed explanation, stack traces, related issues, suggestions how to fix, links for us to have context, eg. StackOverflow, personal fork, etc.)

kindly merge the pull request #17965

merged


ENH: Make Parameters empty, do not warn in Transform default-constructor

Defaulted (= default) the Transform default-constructor, ensuring
that it will make both its Parameters and its FixedParameters empty.
Allowed calling this default-constructor from a derived transform class
without getting a itkWarningMacro.

This pull request does actually fix some runtime warnings (from itkWarningMacro), for example:

2632: Transform (000001C63A18C970): Using default transform constructor.  Should specify NOutputDims and NParameters as args to constructor.

At https://dev.azure.com/itkrobotwindow/ITK.Windows/_build/results?buildId=7248&view=logs&j=2d2b3007-3c5c-5840-9bb0-2b1ea49925f3&t=77aad734-2057-5694-9ae2-ee1f5f26eae8&l=294552

MacOS test exceeded the time limit.


681 bugs found patching in annots

Pull Request Overview

Couple of minor bugs in the annot patching

Review Notes

Annots could be added to an AGS multiple times, now only once
Adding Annots not associated with another encounter to an encounter caused an error

/rebase

/rebase

/rebase


How to access all of document contents in correct order?

Hi, I'm looking to use OfficeIMO to import Word documents to another format. I've fairly quickly been able to pull in the text content of a document with the correct structure but I'm now looking at importing tables and images (and any other content). I can see WordParagraph has an IsImage property which I think will give me what I want for images. For tables, I can see there is a Tables property on WordSection but nothing on WordParagraph. Is it possible to figure out where the table is within the document? I've looked at using the OpenXML API directly but that has just made me aware of why you developed this!

It's hard in its current state because of how it was implemented, where Tables and a few other types are stored totally separate from Paragraphs. As in Table exists in Document directly, similar to Paragraphs, but Images, HyperLinks and others are bound to Paragraph. So I basically bound Paragraphs to Section, same with Table and so on, but this causes issues when trying to establish the order.
I believe we need to create another Property which stores all Paragraphs, All Tables etc in single List in proper order as they are read from the word which should help with maintaining the current order without touching current properties.
It shouldn't be too hard to add. Most of the code is ready in:

https://github.com/EvotecIT/OfficeIMO/blob/master/OfficeIMO.Word/WordSection.PrivateMethods.cs

It just requires merging Lists/StBlocks/Paragraphs etc in one big ordered list of things.
Especially since I wanted to create a library DocX to PDF, but without order it's hard ;)

I think we should start somewhere, and improve over time. The methods in WordSection are simply converting OpenXml objects int officeimo objects for easier management so they convert Paragraph to WordParagraph, which then you can check if it has image, hyperlink or whatever assigned to them.

Solved by: https://github.com/EvotecIT/OfficeIMO/pull/240


Rc/apiexplorers

This PR updates Paymaster and Bundler API's and adds an API Explorer for both

will be merged in #88


[Diagnostic verifier] Support conditional verification

Resolves SR-703
In this change, you can specify optional '('  flag ')' right after expected-{error|warning|note}.
For instance, on // expected-error(FLAG_NAME) {{message}},
it's verified only if -D FLAG_NAME is passed as the command line option.
flag can be inverted by optional ! prefix.
Example usage:
// RUN: %target-typecheck-verify-swift -D SDK_%target-sdk-name

#if os(Linux)
var x = linuxAPI(0) // expected-error(SDK_linux) {{}}
#else
var x = otherAPI(0) // expected-error(!SDK_linux) {{}}
#endif

// Common
var n = commonAPI(0) // expected-error {{}}

Note that the -verify mode still doesn't take #if into account,
because non-active conditional compilation blocks might be diagnosed by the Parser,
and we want to verify that.
This is useful for verifying -target dependent diagnostics,
or -swift-version 3 -swift-version 4 comparison tests.

@swift-ci Please smoke test

Non-deterministic crasher?
https://ci.swift.org/job/swift-PR-Linux-smoke-test/4173/console
Failing Tests (1):
    Swift(linux-x86_64) :: compiler_crashers/28657-unreachable-executed-at-swift-lib-ast-type-cpp-1344.swift

@swift-ci Please smoke test Linux platform

@swift-ci Please smoke test Linux platform

@swift-ci Please smoke test Linux platform

Looks pretty good to me. @lattner might have opinions as implementor of the verifier fix-it mode.

Fixed a few cosmetic problems.
@swift-ci Please smoke test

Rebased on master
@swift-ci Please smoke test

@DougGregor looked at the other diagnostic verifier change recently, so tapping him for a second approval.

Resolved conflicts
@swift-ci Please smoke test

Resolved conflicts
@swift-ci Please smoke test

Rebased.
@swift-ci Please smoke test

@rintaro Do you still want us to take this?

I will update this PR when I have time. Let me keep it open for now.

Can one of the admins verify this patch?

@futurejones Are these expected messages?

Please update the base branch to main by Oct 5th otherwise the pull request will be closed automatically.

How to change the base branch: (Link)
More detail about the branch update: (Link)


perf: improved performance in various methods in Image and ImageList

Summary of Changes

Improved memory usage and runtime in:

Image

convert_to_grayscale
adjust_brightness
add_noise
adjust_contrast
adjust_color_balance
find_edges

ImageList

from_images
add_image
add_images
remove_image_by_index
remove_duplicate_images
convert_to_grayscale
resize
crop
adjust_brightness
add_noise
adjust_contrast
adjust_color_balance
find_edges

Changed blur algorithm in Image and ImageList from Gaussian blur to box blur
Fixed a bug in blur and sharpen, that they could not work with Tensors of size greater than 2**31

Details to the performance upgrades:
These details will explain the performance upgrades in the ImageList. All performance upgrades and changes in Image are made according to the changes in ImageList
Early stopping
convert_to_grayscale returns self when it has only one channel (in this case, the ImageList is already in grayscale)
remove_duplicate_images returns self if the unique image tensor has the same size as the original (in this case, there are no duplicates)
adjust_brightness returns ImageList with complete black images if factor is 0
adjust_color_balance returns ImageList.convert_to_grayscale if factor is 0
General changes
If a float Tensor is used during the computation, it will be float16 instead of float32
Improved the order of Tensor allocations, so that there will be fewer problems with tensors not being completely on the VRAM
Benchmark
Only the transformation methods have benchmarks for the runtime. Their benchmark includes only changes over/under a change factor of 0.25, rounded to one decimal point, as runtime depends on multiple factors and fluctuates heavily with changes of only a few milliseconds in most cases.
All differences are measured as a factor compared to the original results. That means a factor below 1 is worse, while a factor above 1 is a better result. For readability, all factors equal to 1 are not included.
Due to the bug fix mentioned above for blur and sharpen the performance of these methods decreased in most cases.
Benchmark with RGB images of size 250×250

method
result size difference
runtime difference
max memory allocation during runtime difference

from_images
8

7,95

remove_image_by_index

1,5

adjust_brightness

1,6
1,5

add_noise
4
42,5
2,25

adjust_contrast

1,4
1,69

adjust_color_balance
4
6,3
3

blur

0,7
0,9

sharpen

0,6
0,91

find_edges

1,3

Benchmark with RGBA images (RGB images with transparent layer) of size 256×256

method
result size difference
runtime difference
max memory allocation during runtime difference

from_images
8

6,29

remove_image_by_index

1,5

adjust_brightness

1,7
1,36

add_noise
4
24,5
2,25

adjust_contrast

1,53

adjust_color_balance
4
1,7
2,6

blur

1,6
0,9

sharpen

0,89

Benchmark with RGB images of multiple different sizes

method
result size difference
runtime difference
max memory allocation during runtime difference

from_images
7,7

6,3

add_images
8

2,38

remove_image_by_index

1,28

resize

1,3

crop

1,3
1,09

adjust_brightness

1,3
1,09

add_noise
4
44,3
1,47

adjust_contrast

2
2,17

adjust_color_balance
4
6,7
1,65

blur

2,87

sharpen

0,7
0,9

find_edges

1,9
0,91

Do you have benchmarks to quantify the performance improvement?

:tada: This PR is included in version 0.27.0 :tada:
The release is available on:

v0.27.0
GitHub release

Your semantic-release bot :package::rocket:


When sorting nodes # of tasks has little meaning

https://github.com/docker/swarmkit/blob/master/manager/scheduler/indexed_node_heap.go#L21
I'd expect some (at least portable way) to sort according to free memory or CPU.
Can't there be 1 task that consumes the memory like 5 other smaller tasks ?
IMHO for now even doing
return     (nh.heap[i].AvailableResources.NanoCPUs + nh.heap[i].AvailableResources.MeMemoryBytes) <         (nh.heap[j].AvailableResources.NanoCPUs + nh.heap[j].AvailableResources.MeMemoryBytes)
Will produce better outcome (with attention to overflow) WDYT?

Thanks for the suggestion. I think it a good idea. We will need metric collection to support this feature. It could be a scheduler option. User may use reserved resource as scheduler metric, which provides resource guarantee. Or use real time resource availability to achieve better resource utilization.

@doronp I'm going through old issues here.
There have been many improvements on this area after this issue have been created.
Is this one still valid or can we close it?


Refactor the simulation flow

Refactor the comparison scripts to be easily integrated to the new run flow.

Has the README been updated for this new flow? Is the run script now obsolete?

Not yet, there are still some work to be done before switching to new flow.

I noticed the SIM_SEED changes I made in run are not in the new flow.

Just added here:
https://github.com/google/riscv-dv/commit/e108691b8c61912068d9bdfbe52818f0eb66ad79#diff-d831ee482f0df083d0b5076369af4b43


Add GitHub Action configuration file

I've written a blog post and recorded a video regarding this PR: https://perlmaven.com/install-developer-dependencies-first-test-css

Thanks @szabgab


test failures -

Among other failures I get this:
    public void ConsumerShouldMoveToNextAvailableOffsetWhenQueryingForNextMessage()

Found address <IP_ADDRESS> for <IP_ADDRESS>
Using address <IP_ADDRESS> for <IP_ADDRESS>
BrokerRouter: Refreshing metadata for topics: IntegrationTopic
Awaiting message from: http://<IP_ADDRESS>:49161/
No connection to:http://<IP_ADDRESS>:49161/.  Attempting to re-connect...
Connection established to:http://<IP_ADDRESS>:49161/.
Received message of size: 131 From: http://<IP_ADDRESS>:49161/
Awaiting message from: http://<IP_ADDRESS>:49161/
Found address <IP_ADDRESS> for <IP_ADDRESS>
Using address <IP_ADDRESS> for <IP_ADDRESS>
Found address <IP_ADDRESS> for <IP_ADDRESS>
Using address <IP_ADDRESS> for <IP_ADDRESS>
Found address <IP_ADDRESS> for <IP_ADDRESS>
Using address <IP_ADDRESS> for <IP_ADDRESS>
Awaiting message from: http://<IP_ADDRESS>:49162/
No connection to:http://<IP_ADDRESS>:49162/.  Attempting to re-connect...
Awaiting message from: http://<IP_ADDRESS>:49163/
No connection to:http://<IP_ADDRESS>:49163/.  Attempting to re-connect...
Connection established to:http://<IP_ADDRESS>:49162/.
Connection established to:http://<IP_ADDRESS>:49163/.
Received message of size: 48 From: http://<IP_ADDRESS>:49163/
Awaiting message from: http://<IP_ADDRESS>:49163/
Closed down connection to: http://<IP_ADDRESS>:49161/
Closed down connection to: http://<IP_ADDRESS>:49162/
Closed down connection to: http://<IP_ADDRESS>:49163/
  This test requires there to be exactly two paritions.

  Expected: 2

  But was:  1

   at NUnit.Framework.Assert.That(Object actual, IResolveConstraint expression, String message, Object[] args)
   at kafka_tests.Integration.ProducerConsumerTests.ConsumerShouldMoveToNextAvailableOffsetWhenQueryingForNextMessage() in ProducerConsumerIntegrationTests.cs: line 188  This test requires there to be exactly two paritions.

  Expected: 2

  But was:  1

   at NUnit.Framework.Assert.That(Object actual, IResolveConstraint expression, String message, Object[] args)
   at kafka_tests.Integration.ProducerConsumerTests.ConsumerShouldMoveToNextAvailableOffsetWhenQueryingForNextMessage() in ProducerConsumerIntegrationTests.cs: line 188  This test requires there to be exactly two paritions.

  Expected: 2

  But was:  1

   at NUnit.Framework.Assert.That(Object actual, IResolveConstraint expression, String message, Object[] args)
   at kafka_tests.Integration.ProducerConsumerTests.ConsumerShouldMoveToNextAvailableOffsetWhenQueryingForNextMessage() in ProducerConsumerIntegrationTests.cs: line 188

Fresh cluster,  kafka-docker (https://github.com/wurstmeister/kafka-docker)
My own tests pass, but that doesn't mean all is good with the cluster.  I'm particularily confused over the test requiring 2 partitions, but presumably there should be a setup for that.

Those are the integration tests [Category("Integration")] which don't run on the build process.  I have been using those mainly to test against my own setup, which has only two kafka boxes.  So you can ignore failures from the integration tests as they are too specific for a generic environment.  This is definitely something that needs cleaning up.


When using the same Vue component in "popup" and "content UI", modifying the Vue component content will crash the popup after hot update.

Describe the bug
When using the same Vue component in "popup" and "content UI", modifying the Vue component content will crash the popup after hot update.
To Reproduce
wxt-demo.zip

Install package pnpm install
Run wxt pnpm run dev
Modify component content HelloWorld.vue
Inspect popups for error messages

Screenshots

Environment

  System:
    OS: Windows 11 10.0.23601
    CPU: (12) x64 AMD Ryzen 5 3600 6-Core Processor
    Memory: 17.65 GB / 31.93 GB
  Binaries:
    Node: 20.6.1 - C:\soft\nodejs\node.EXE
    npm: 9.8.1 - C:\soft\nodejs\npm.CMD
    pnpm: 8.12.0 - ~\AppData\Local\pnpm\pnpm.EXE
  Browsers:
    Edge: Chromium (120.0.2210.61)
    Internet Explorer: 11.0.23601.1000

I was able to reproduce, and this does only happen if re-using the component between entrypoints. Specifically, between a content script and HTML page. Doesn't happen between multiple UIs and doesn't happen between multiple content scripts.
Something weird must be going on between the HTML's HMR and the content script's bundling. Maybe they're getting combined somehow? They should be completely separate.

@aklinker1 I'm not sure if this helpful but it could be similar to this issue https://github.com/nuxt-modules/storybook/issues/428#issuecomment-1794448129

This looks to me like @vitejs/plugin-vue might be injected twice (and therefore transforming components twice).

@DaPotatoMan thanks for sharing. I've seen that before, but if I inspect the plugins, there's only 1 vue plugin per vite build or vite dev. I think instead, the problem is the mixing of dev and build modes.
I think the solution might be to use the SSR tools vite provides to render the other files rather than using vite build, but I don't know if vite can SSR a JS file, I think it's just for HTML.


Add Elasticseach sample publisher

Add job to the sample data loader which fill(publish) search data into Elasticsearch.

@jhettler It seems CLA (Common License Agreement) check is failing. Could you click the detail?

Same as in my other PR - I have signed CLA, but I have no idea how to rerun this check :) Thanks for hints.

@jhettler , let us work with our open source team to figure out. Will get back to you. Thanks a lot for the fast contribution :)

also cc @verdan , hey verdan, do you recall any extra steps to make the CLA check work for you? Thanks :)

No, it was pretty straight forward, sign in using Github and then agree with the license. Don’t remember any extra steps.
You sure you’re using the same github account for PR and license agreement?

@jhettler @verdan , talked with our open source team. The error is due to An author in one of the commits has no associated github name
I think you need to config your github user.name and user.email locally first(could confirm with git config --list), then push to the branch. Let us know if it works.

thanks @verdan for the input.

FYI, the failure comes from https://github.com/lyft/osscla/blob/master/osscla/services/gh.py#L118-L130

Thanks for hints, should be fine now.

@jhettler , great :) Thanks for the contribution. LGTM.


update image version to 2.0.0

Closing the pull request as changes are updated on target branch using cherry-pick

Closing the pull request as changes are updated on target branch using cherry-pick


Stringification customization

There are several problems in current implementation, e.g., not allow to override default stringification, customization on enum and pointer does not work.
#if 1
template<> struct libassert::stringifier<int> {
    std::string stringify(int x) {
        return "this is a int " + std::to_string(x);
    }
};

template<> struct libassert::stringifier<int*> {
    std::string stringify(int*) {
        return "this is a int* ";
    }
};

template<> struct libassert::stringifier<std::errc> {
    std::string stringify(std::errc ec) {
        return libassert::detail::stringification::stringify(std::error_condition(ec));
    }
};
#endif

int main() {
    int x = 0;
    ASSERT(x, &x, std::errc{});
}
#endif

The above code outputs
    Where:
        x => 0
    Extra diagnostics:
        &x          => <instance of int*>
        std::errc{} => <instance of std::errc>

Current stringification relies on complicated and verbose and error-prone SFINAE.
Since we are using C++17 now, if constexpr can reduce the use of SFINAE.

Thanks for opening this. The current system is definitely in need of work. It used to be implemented with a long if-constexpr chain and I think you’re right that it may be time to move back to that.

I'm going to close this, happy to discuss improvements further. I think there is likely still room for improvement.


Small fixes in yii\web\SessionIterator

check session variable
init $_key in constructor

Q
A

Is bugfix?
✔/❌

New feature?
❌

Breaks BC?
❌

Fixed issues

Thanks.


Partial Implementation of Testing Framework

Overview
This pull request introduces a foundational testing framework for the Polling App. It covers initial unit tests for both the backend API calls and some of the React components. While this is a work-in-progress, the setup established here will guide future test implementation.
Changes Included

Set up Jest and React Testing Library for unit testing.
Implemented initial unit tests for the fetchUsers and loginUser API calls.
Added basic tests for the Navbar component to ensure correct links are displayed based on user authentication status.
Configured jest.mock to simulate API calls, ensuring that our tests run in isolation without actual data fetching.
Created a structured testing plan to outline future testing phases, including both unit and integration tests.

Tests Implemented
API Calls:

fetchUsers: Validates correct fetching and error handling.
loginUser: Tests authentication success and failure scenarios.

Components:

Navbar: Checks if the appropriate links are displayed for authenticated and unauthenticated users.

Future Work

Complete the testing checklist as outlined in the Testing Plan.
Implement integration tests using Playwright.
Extend component tests to cover interactive behaviors and data integration.

:tada: This PR is included in version 1.0.0 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


electron not working

hello there!
is there any fix for this error?
http://prntscr.com/gpmkez
and is there any possibilities to make a multi steam account like to idle all games?
i've found this bot https://github.com/nyo/steam-idle but it cant run properly because i have 9 accounts and only 6 of them log in on others wont simply.

Instead of doing node index.js you can try to do node_modules/.bin/electron .
A blank cmd should appear for a few seconds before the window shows up.
I have not tested my program with 9 accounts, but try it and see how it works out. I'm going to say that it will work. If it doesn't work on my program either, then it's simply steam throttling your connected accounts and you should try to do the rest of your account from a different ip address.


Change matrix type

This change fixes Issue #352

Can one of the admins verify this patch?

Thanks @afabri for the PR and sorry for the huge delay...


Move from codes to magic links

Is your feature request related to a problem? Please describe.
Currently, the user has to get the link from their email and input with a code from that same email. This feels really useless
Describe the solution you'd like
Either, the email should just have a code and the existing page should redirect or:

Link in email has a query parameter
Which is parsed by Axum
Which gets fed into Liquid
Which makes the code a hidden input element

merg


The diagrams used to explain deadlocks in the Reentrancy section of the Microsoft Orleans docs are wrong

Issue description
In the Reentrancy section of the Microsoft Orleans documentation the same diagram is used for Case 1: the calls do not deadlock and Case 2: the calls deadlock:
https://learn.microsoft.com/en-us/dotnet/orleans/grains/reentrancy#case-1-the-calls-do-not-deadlock
https://learn.microsoft.com/en-us/dotnet/orleans/grains/reentrancy#case-2-the-calls-deadlock
Target framework

.NET 7

Document Details
⚠ Do not edit this section. It is required for learn.microsoft.com ➟ GitHub issue linking.

ID: 5d4ab7af-82bf-efd1-9742-ade3e84c0df4
Version Independent ID: 92bb7973-a8c4-e948-6aa7-f40eecfeaafd
Content: Reentrancy
Content Source: docs/orleans/grains/reentrancy.md
Product: dotnet-orleans
GitHub Login: @IEvangelist
Microsoft Alias: dapine

It seems that the "do not deadlock" one should be this: https://github.com/dotnet/orleans-docs/blob/main/src/images/scheduling_6.png

Fixed by #32830


No FINAL* files created.

Hi,
I used conda version of 3D-DNA (version 190716) to scaffold my genome.
However, I have not got FINAL files including FINAL.fasta, FINAL.hic  although no errors were not observed in log file.
I have obtained PREFIX_final.assembly, PREFIX_final.hic, PREFIX_final.fasta, PREFIX_final.asm, PREFIX_final.cprops, PREFIX_HiC.fasta, and PREFIX_HiC.assembly.
Is this result correct?
Any advice would be greatly appreciated.
Best,
Jung

Hi,
When I run the code 'run-asm-pipeline.sh -r 0 xx.fa merged_nodups.txt', I have not got FINAL files including FINAL.fasta, FINAL.hic. There are no erros in the logfile, but have the warning messages ':| WARNING: GNU Parallel version 20150322 or later not installed. We highly recommend to install it to increase performance. Starting pipeline without parallelization!
:| Warning: no explicit bundle size was listed. Will use the same one as listed for false positive size threshold: this is the most typical scenario.
:| WARNING: GNU Parallel version 20150322 or later not installed. We highly recommend to install it to increase performance. Starting pipeline without parallelization!
:| Warning: No input for label1 was provided. Default for label1 is ":::fragment_"
:| Warning: No input for label2 was provided. Default for label2 is ":::debris"
:| Warning: No input for label1 was provided. Default for label1 is ":::fragment_"
:| Warning: No input for label2 was provided. Default for label2 is ":::debris"'
Kind regards,
Aomei

Hi Aomei,
This is not enough for me to diagnose. Please attach the full output including stdout and stderr and the list of files that did get generated. Did the pipeline finish at all or it is still running?
Olga

On Nov 2, 2022, at 10:14 PM, liaomei1995 @.***> wrote:
Hi,
When I run the code 'run-asm-pipeline.sh -r 0 xx.fa merged_nodups.txt', I have not got FINAL files including FINAL.fasta, FINAL.hic. There are no erros in the logfile, but have the warning messages ':| WARNING: GNU Parallel version 20150322 or later not installed. We highly recommend to install it to increase performance. Starting pipeline without parallelization!
:| Warning: no explicit bundle size was listed. Will use the same one as listed for false positive size threshold: this is the most typical scenario.
:| WARNING: GNU Parallel version 20150322 or later not installed. We highly recommend to install it to increase performance. Starting pipeline without parallelization!
:| Warning: No input for label1 was provided. Default for label1 is ":::fragment_"
:| Warning: No input for label2 was provided. Default for label2 is ":::debris"
:| Warning: No input for label1 was provided. Default for label1 is ":::fragment_"
:| Warning: No input for label2 was provided. Default for label2 is ":::debris"'
Kind regards,
Aomei
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Dear sir / madam,
Please find the attached picture.
Kind regards,Aomei

李傲梅 助理研究员
Aomei Li, Assistant research fellow

广西南宁市大学东路174号 广西壮族自治区农业科学院甘蔗研究所 530007
Add: Daxue East Road NO. 174, Sugarcane Research Institute, Guangxi Academy of Agricultural Sciences, Nanning, 530007 China
Tel: 86-0771-3899390
E-Mail: @.***
----- 原始邮件 -----
发件人：dudcha @.>
收件人：aidenlab/3d-dna @.>
抄送人：liaomei1995 @.>,  Comment @.>
主题：Re: [aidenlab/3d-dna] No FINAL* files created.  (Issue #159)
日期：2022年11月03日 11点44分
Hi Aomei,
This is not enough for me to diagnose. Please attach the full output including stdout and stderr and the list of files that did get generated. Did the pipeline finish at all or it is still running?
Olga

On Nov 2, 2022, at 10:14 PM, liaomei1995 @.***> wrote:

Hi,

When I run the code 'run-asm-pipeline.sh -r 0 xx.fa merged_nodups.txt', I have not got FINAL files including FINAL.fasta, FINAL.hic. There are no erros in the logfile, but have the warning messages ':| WARNING: GNU Parallel version 20150322 or later not installed. We highly recommend to install it to increase performance. Starting pipeline without parallelization!

:| Warning: no explicit bundle size was listed. Will use the same one as listed for false positive size threshold: this is the most typical scenario.

:| WARNING: GNU Parallel version 20150322 or later not installed. We highly recommend to install it to increase performance. Starting pipeline without parallelization!

:| Warning: No input for label1 was provided. Default for label1 is ":::fragment_"

:| Warning: No input for label2 was provided. Default for label2 is ":::debris"

:| Warning: No input for label1 was provided. Default for label1 is ":::fragment_"

:| Warning: No input for label2 was provided. Default for label2 is ":::debris"'

Kind regards,

Aomei

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Hey Aomei,
There appears to be no attachment, sorry.
Olga

Hey,
I looked though the log: it looks fine to me. Something went wrong with the chrom boundary detection, but you should have no trouble checking via JBAT. If you are concerned about the warnings they are indeed just the warnings. I've removed them in the latest release since they understandably unnerved people. Thanks,
Olga

Thank you for the quick response.
Reviewing the 'GSE95797_Hs1.final.hic -> GSE95797_Hs1.rawchrom.hic' contact map, and 'GSE95797_Hs1.final.assembly -> GSE95797_Hs1.rawchrom.assembly' assembly files with JBAT look promising.
How do I extract that fasta corresponding to these scaffolds? The 'GSE95797_Hs1.rawchrom.fasta' seems to correspond to the contigs.

Please check the genome assembly cookbook for general guidance. https://www.dnazoo.org/methodsOn Mar 20, 2023, at 12:17 PM, acboulet @.***> wrote:
Thank you for the quick response.
Reviewing the 'GSE95797_Hs1.final.hic -> GSE95797_Hs1.rawchrom.hic' contact map, and 'GSE95797_Hs1.final.assembly -> GSE95797_Hs1.rawchrom.assembly' assembly files with JBAT look promising.
How do I extract that fasta corresponding to these scaffolds? The 'GSE95797_Hs1.rawchrom.fasta' seems to correspond to the contigs.
—Reply to this email directly, view it on GitHub, or unsubscribe.You are receiving this because you commented.Message ID: @.***>

Hi Aomei, This is not enough for me to diagnose. Please attach the full output including stdout and stderr and the list of files that did get generated. Did the pipeline finish at all or it is still running? Olga
…
On Nov 2, 2022, at 10:14 PM, liaomei1995 @.***> wrote: Hi, When I run the code 'run-asm-pipeline.sh -r 0 xx.fa merged_nodups.txt', I have not got FINAL files including FINAL.fasta, FINAL.hic. There are no erros in the logfile, but have the warning messages ':| WARNING: GNU Parallel version 20150322 or later not installed. We highly recommend to install it to increase performance. Starting pipeline without parallelization! :| Warning: no explicit bundle size was listed. Will use the same one as listed for false positive size threshold: this is the most typical scenario. :| WARNING: GNU Parallel version 20150322 or later not installed. We highly recommend to install it to increase performance. Starting pipeline without parallelization! :| Warning: No input for label1 was provided. Default for label1 is ":::fragment_" :| Warning: No input for label2 was provided. Default for label2 is ":::debris" :| Warning: No input for label1 was provided. Default for label1 is ":::fragment_" :| Warning: No input for label2 was provided. Default for label2 is ":::debris"' Kind regards, Aomei — Reply to this email directly, view it on GitHub <#159 (comment)>, or unsubscribe https://github.com/notifications/unsubscribe-auth/ACLAMG4SWRPNPBZ7BPE72QDWGMUZBANCNFSM6AAAAAARTZOC3Q. You are receiving this because you are subscribed to this thread.

HI Olga,
I also meet this error. When I run the code 'run-asm-pipeline.sh -r 0 xx.fa merged_nodups.txt', I have not got FINAL files including FINAL.fasta, FINAL.hic. There are no erros in the logfile, but have the warning messages ':| WARNING: GNU Parallel version 20150322 or later not installed. We highly recommend to install it to increase performance. Starting pipeline without parallelization!
:| Warning: no explicit bundle size was listed. Will use the same one as listed for false positive size threshold: this is the most typical scenario.
:| WARNING: GNU Parallel version 20150322 or later not installed. We highly recommend to install it to increase performance. Starting pipeline without parallelization!
:| Warning: No input for label1 was provided. Default for label1 is ":::fragment_"
:| Warning: No input for label2 was provided. Default for label2 is ":::debris"
:| Warning: No input for label1 was provided. Default for label1 is ":::fragment_"
:| Warning: No input for label2 was provided. Default for label2 is ":::debris"'
Can you help me with this, thank you very much
Kind regards,Yang