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|---|---|
Title: Add `.quantile(foo=quantiles)` API?
Body: ### Is your feature request related to a problem?
Many of our methods allow the equivalent of `.shift(foo=5)`, where `foo` is a dimension name.
But not `.quantile`! That requires `.quantile(dim="foo", [0,0.5,1])`
### Describe the solution you'd like
I guess this is a missing thing rather than a deliberate choice, is that right?
### Describe alternatives you've considered
_No response_
### Additional context
_No response_ | 1medium
|
Title: System : Failed to execute 'open' on 'XMLHttpRequest': Invalid URL
Body: Hi @tricktreat , Everything works in `CLI` mode.
I tried to chat in web.
```
Human :
Please answer all the named entities in the sentence: Iron Man is a superhero appearing in American comic books published by Marvel Comics. The character was co-created by writer and editor Stan Lee, developed by scripter Larry Lieber, and designed by artists Don Heck and Jack Kirby.
System :
Failed to execute 'open' on 'XMLHttpRequest': Invalid URL
```
Here is my `index.ts` file
```
const HUGGINGGPT_BASE_URL = "http://{LAN_IP_of_the_server}:{port}/" //"http://localhost:8004"
// use ChatGPT: double click on the setting icon
const CHAT_GPT_URL = "https://api.openai.com"
const CHAT_GPT_LLM = "gpt-3.5-turbo" // gpt-3.5-turbo, gpt-4
// Dev: local endpoint
// const CHAT_GPT_URL = "http://localhost:8006"
export {HUGGINGGPT_BASE_URL, CHAT_GPT_URL, CHAT_GPT_LLM}
```
Everything works fine when I double click the setting icon (switch from huggingGPT to ChatGPT) and ask the same question in Web:
```
ChatGPT :
Hi there! I am OpenAI ChatGPT, an AI assistant for you. How can I help you?
Human :
Please answer all the named entities in the sentence: Iron Man is a superhero appearing in American comic books published by Marvel Comics. The character was co-created by writer and editor Stan Lee, developed by scripter Larry Lieber, and designed by artists Don Heck and Jack Kirby.
ChatGPT :
Named entities in the sentence are:
1. Iron Man (superhero)
2. American (related to the comic books being published)
3. Marvel Comics (publishing house)
4. Stan Lee (writer and editor)
5. Larry Lieber (scripter)
6. Don Heck (artist)
7. Jack Kirby (artist)
``` | 1medium
|
Title: Examples fail to run, aborting with a raised "Unknown GLSL error format"
Body: I tried a few examples, including https://github.com/glumpy/glumpy/blob/master/examples/gloo-antialias.py and they fail with the following error. I have just noticed that glxinfo is showing that direct rendering is not working. I am not sure if that is causing the problem.
python main.py
[i] Using GLFW (GL 4.4)
[i] Running at 60 frames/second
Traceback (most recent call last):
File "main.py", line 64, in <module>
app.run()
File "/usr/local/lib/python2.7/dist-packages/glumpy/app/**init**.py", line 301, in run
clock = **init**(clock=clock, framerate=framerate, backend=**backend**)
File "/usr/local/lib/python2.7/dist-packages/glumpy/app/**init**.py", line 261, in **init**
window.dispatch_event('on_resize', window._width, window._height)
File "/usr/local/lib/python2.7/dist-packages/glumpy/app/window/event.py", line 375, in dispatch_event
if getattr(self, event_type)(_args):
File "/usr/local/lib/python2.7/dist-packages/glumpy/app/window/window.py", line 197, in on_resize
self.dispatch_event('on_draw', 0.0)
File "/usr/local/lib/python2.7/dist-packages/glumpy/app/window/event.py", line 365, in dispatch_event
if handler(_args):
File "main.py", line 18, in on_draw
program.draw(gl.GL_POINTS)
File "/usr/local/lib/python2.7/dist-packages/glumpy/gloo/program.py", line 499, in draw
self.activate()
File "/usr/local/lib/python2.7/dist-packages/glumpy/gloo/globject.py", line 87, in activate
self._create()
File "/usr/local/lib/python2.7/dist-packages/glumpy/gloo/program.py", line 147, in _create
self._build_shaders(self._handle)
File "/usr/local/lib/python2.7/dist-packages/glumpy/gloo/program.py", line 195, in _build_shaders
shader.activate()
File "/usr/local/lib/python2.7/dist-packages/glumpy/gloo/globject.py", line 97, in activate
self._update()
File "/usr/local/lib/python2.7/dist-packages/glumpy/gloo/shader.py", line 205, in _update
lineno, mesg = self._parse_error(error)
File "/usr/local/lib/python2.7/dist-packages/glumpy/gloo/shader.py", line 242, in _parse_error
raise ValueError('Unknown GLSL error format')
ValueError: Unknown GLSL error format
Compilation exited abnormally with code 1 at Mon Aug 31 15:14:35
| 2hard
|
Title: Link to examples broken
Body: Hi @marcomusy ,
very minor issue: I found the link to the `surfIntersect.py` example broken (it's now called `surf_intersect.py`) - where could I fix this in the repo? If I stumble over these matters in the futures I can then simply send a small PR :)
Best, Johannes | 0easy
|
Title: Say() Function Producing Duplicate Messages in Channel
Body: (Filling out the following details about bugs will help us solve your issue sooner.)
### Reproducible in:
```bash
pip freeze | grep slack
python --version
sw_vers && uname -v # or `ver`
```
#### The `slack_bolt` version
slack-sdk==3.19.5
slack-bolt==1.15.5
(Paste the output of `pip freeze | grep slack`)
#### Python runtime version
3.9.12
(Paste the output of `python --version`)
#### OS info
ProductName: macOS
ProductVersion: 12.6.1
BuildVersion: 21G217
Darwin Kernel Version 21.6.0: Thu Sep 29 20:12:57 PDT 2022; root:xnu-8020.240.7~1/RELEASE_X86_64
(Paste the output of `sw_vers && uname -v` on macOS/Linux or `ver` on Windows OS)
#### Steps to reproduce:
(Share the commands to run, source code, and project settings (e.g., setup.py))
1. Set up venv with starter app.py code
```
import os
# Use the package we installed
from slack_bolt import App
from slack_bolt.adapter.socket_mode import SocketModeHandler
import re
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# Initializes your app with your bot token and signing secret
app = App(
token=os.environ["SLACK_BOT_TOKEN"]
)
#autoreply in hw function
@app.event("message")
def auto_reply(event, say):
try:
if 'files' in event: #need to handle events with attachments differently
if event['channel'] == "C019J946UPJ" and ('.pdf' in event['files'][0]['name'] or '.google' in event['text']) and (event['user'] != 'U038JV06ZMZ' and event['user'] != 'U03612ME0BY' and event['user'] != 'U029XD01PNF' and event['user'] != 'U038JV0HD19' and event['user'] != 'U03E7JSK3LG'): #if statement clarifying channel to watch for & to not reply to replies
say(text = "An IL scientist will connect with you shortly. Before we do, take this opportunity to double check your key behavior. Is it uncomfortably specific? See the examples in this document to help you refine your thinking. https://docs.google.com/document/d/1EVoyhVnX_jTzNbWBrN8C9Bz8-A7xHO8wN5dM6o-Oves/edit#",
thread_ts = event['ts'])
#print(event)
elif event['channel'] == 'C01972VPPJ5' and ('.pdf' in event['files'][0]['name'] or '.jpg' in event['files'][0]['name'] or '.png' in event['files'][0]['name'] or '.google' in event['text']) and (event['user'] != 'U038JV06ZMZ' and event['user'] != 'U03612ME0BY' and event['user'] != 'U029XD01PNF' and event['user'] != 'U038JV0HD19' and event['user'] != 'U03E7JSK3LG'):
say(text = "Great work - you have now uploaded your Behavior Diagnosis! An IL scientist will connect with you shortly but before they do it's worth doing a quick double check … Did you group your steps into steps and substeps? See an example HERE. https://docs.google.com/presentation/d/1vqK8EIEYQfr2ZzVIf3YVjHL-w29eh8TfM62UbZHKMfg/edit#slide=id.g10907c59ee3_0_465",
thread_ts = event['ts'])
#print(event)
else:
if event['channel'] == "C019J946UPJ" and ('.google' in event['text']) and (event['user'] != 'U038JV06ZMZ' and event['user'] != 'U03612ME0BY' and event['user'] != 'U029XD01PNF' and event['user'] != 'U038JV0HD19' and event['user'] != 'U03E7JSK3LG'): #if statement clarifying channel to watch for & to not reply to replies
say(text = "An IL scientist will connect with you shortly. Before we do, take this opportunity to double check your key behavior. Is it uncomfortably specific? See the examples in this document to help you refine your thinking. https://docs.google.com/document/d/1EVoyhVnX_jTzNbWBrN8C9Bz8-A7xHO8wN5dM6o-Oves/edit#",
thread_ts = event['ts'])
#print(event)
elif event['channel'] == 'C01972VPPJ5' and ('.google' in event['text'] or 'miro' in event['text']) and (event['user'] != 'U038JV06ZMZ' and event['user'] != 'U03612ME0BY' and event['user'] != 'U029XD01PNF' and event['user'] != 'U038JV0HD19' and event['user'] != 'U03E7JSK3LG'): #if statement clarifying channel to watch for & to not reply to replies
say(text = "Great work - you have now uploaded your Behavior Diagnosis! An IL scientist will connect with you shortly but before they do it's worth doing a quick double check … Did you group your steps into steps and substeps? See an example HERE. https://docs.google.com/presentation/d/1vqK8EIEYQfr2ZzVIf3YVjHL-w29eh8TfM62UbZHKMfg/edit#slide=id.g10907c59ee3_0_465",
thread_ts = event['ts'])
#print(event)
elif event['channel'] == 'C018UDKTULA' and 'parent_user_id' not in str(event): #auto reply to meet & greet post
say(text = "Sweet :lollipop: :chocolate_bar: :doughnut:! Thank you so much for sharing. \nIf you have a sec, take some time to find someone in the #meet_and_greet channel who works in the same industry or geographic area as you do. Say #hi and tell them what you have in common!",
thread_ts = event['ts'])
#print(event)
except Exception as e:
logger.error(f"Error publishing home tab: {e}")
```
3. Use test channel in place of last "elif " statement in the "auto_reply" function. (elif event['channel'] == 'C018UDKTULA' and 'parent_user_id' not in str(event):)
4. Run code with requirements specified above.
### Expected result:
The reply bot replies in the thread once with the automated message.
### Actual result:
The reply occurs successfully, but several hours later the bot posts a message in channel unprompted. I created a screenshot wireframe of what this looks like (I've been deleting them as they come along. If this doesn't convey the message well let me know and I can send over a real example).
(Tell what actually happened with logs, screenshots)
## Requirements
Please read the [Contributing guidelines](https://github.com/slackapi/bolt-python/blob/main/.github/contributing.md) and [Code of Conduct](https://slackhq.github.io/code-of-conduct) before creating this issue or pull request. By submitting, you are agreeing to those rules.
| 2hard
|
Title: [Bug] In 0.22.0 load_checkpoint speaker_file_path = speaker_file_path or os.path.join(checkpoint_dir, "speakers_xtts.pth")
Body: ### Describe the bug
When performing this after fine tuning a model:
def load_model(xtts_checkpoint, xtts_config, xtts_vocab):
global XTTS_MODEL
clear_gpu_cache()
if not xtts_checkpoint or not xtts_config or not xtts_vocab:
return "You need to run the previous steps or manually set the `XTTS checkpoint path`, `XTTS config path`, and `XTTS vocab path` fields !!"
config = XttsConfig()
config.load_json(xtts_config)
XTTS_MODEL = Xtts.init_from_config(config)
print("[FINETUNE] \033[94mStarting Step 3\033[0m Loading XTTS model!")
XTTS_MODEL.load_checkpoint(config, checkpoint_path=xtts_checkpoint, vocab_path=xtts_vocab, use_deepspeed=False)
if torch.cuda.is_available():
XTTS_MODEL.cuda()
print("[FINETUNE] Model Loaded!")
return "Model Loaded!"
TTS version 0.21.3 works absolutely perfectly.
TTS version 0.22.0 errors in the following way:
XTTS_MODEL.load_checkpoint(config, checkpoint_path=xtts_checkpoint, vocab_path=xtts_vocab, use_deepspeed=False)
File "C:\AI\text-generation-webui\installer_files\env\Lib\site-packages\TTS\tts\models\xtts.py", line 759, in load_checkpoint
speaker_file_path = speaker_file_path or os.path.join(checkpoint_dir, "speakers_xtts.pth")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<frozen ntpath>", line 108, in join
TypeError: expected str, bytes or os.PathLike object, not NoneType
### To Reproduce
I'm using finetuning and loading a model.
### Expected behavior
to load the model.
### Logs
```shell
XTTS_MODEL.load_checkpoint(config, checkpoint_path=xtts_checkpoint, vocab_path=xtts_vocab, use_deepspeed=False)
File "C:\AI\text-generation-webui\installer_files\env\Lib\site-packages\TTS\tts\models\xtts.py", line 759, in load_checkpoint
speaker_file_path = speaker_file_path or os.path.join(checkpoint_dir, "speakers_xtts.pth")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<frozen ntpath>", line 108, in join
TypeError: expected str, bytes or os.PathLike object, not NoneType
```
### Environment
```shell
INFO:root:OS Version: Windows 10.0.22621
INFO:root:Note: Windows 11 build is 10.x.22xxx
INFO:root:Python Version: 3.11.5
INFO:root:Torch Version: 2.1.2+cu121
INFO:root:System RAM: 22.37 GB available out of 31.34 GB total
INFO:root:CUDA_HOME: C:\AI\text-generation-webui\installer_files\env
INFO:root:Package Versions:
INFO:root:numpy Required: >= 1.22.0 Installed: 1.24.4
INFO:root:soundfile Required: >= 0.12.1 Installed: 0.12.1
INFO:root:uvicorn Required: >= 0.24.0.post1 Installed: 0.25.0
INFO:root:TTS Required: >= 0.22.0 Installed: 0.22.0
INFO:root:torch Required: >= 2.1.0+cu118 Installed: 2.1.2+cu121
INFO:root:torchaudio Required: >= 2.1.0+cu118 Installed: 2.1.2+cu121
INFO:root:fastapi Required: >= 0.104.1 Installed: 0.105.0
INFO:root:Jinja2 Required: >= 3.1.2 Installed: 3.1.2
INFO:root:requests Required: >= 2.31.0 Installed: 2.31.0
INFO:root:tqdm Required: >= 4.66.1 Installed: 4.66.1
INFO:root:importlib-metadata Required: >= 4.8.1 Installed: 7.0.1
INFO:root:packaging Required: >= 23.2 Installed: 23.2
INFO:root:GPU Information:
Sun Dec 24 11:21:21 2023
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 546.17 Driver Version: 546.17 CUDA Version: 12.3 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 4070 WDDM | 00000000:01:00.0 On | N/A |
| 0% 37C P8 11W / 200W | 597MiB / 12282MiB | 1% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 1308 C+G C:\Windows\explorer.exe N/A |
| 0 N/A N/A 1952 C+G ...crosoft\Edge\Application\msedge.exe N/A |
| 0 N/A N/A 2900 C+G ...__8wekyb3d8bbwe\Notepad\Notepad.exe N/A |
| 0 N/A N/A 7528 C+G ...siveControlPanel\SystemSettings.exe N/A |
| 0 N/A N/A 7624 C+G ...5n1h2txyewy\ShellExperienceHost.exe N/A |
| 0 N/A N/A 7628 C+G C:\Windows\explorer.exe N/A |
| 0 N/A N/A 8840 C+G ...nr4m\radeonsoftware\AMDRSSrcExt.exe N/A |
| 0 N/A N/A 9364 C+G ...nt.CBS_cw5n1h2txyewy\SearchHost.exe N/A |
| 0 N/A N/A 9412 C+G ...2txyewy\StartMenuExperienceHost.exe N/A |
| 0 N/A N/A 9964 C+G ...__8wekyb3d8bbwe\WindowsTerminal.exe N/A |
| 0 N/A N/A 12324 C+G ...m\radeonsoftware\RadeonSoftware.exe N/A |
| 0 N/A N/A 14164 C+G ...\cef\cef.win7x64\steamwebhelper.exe N/A |
| 0 N/A N/A 14640 C+G ...CBS_cw5n1h2txyewy\TextInputHost.exe N/A |
| 0 N/A N/A 17072 C+G ...ft Office\root\Office16\OUTLOOK.EXE N/A |
| 0 N/A N/A 17420 C+G ...s (x86)\Razer\Synapse\RzSynapse.exe N/A |
| 0 N/A N/A 19616 C+G ...2.0_x64__cv1g1gvanyjgm\WhatsApp.exe N/A |
| 0 N/A N/A 20624 C+G ...on\120.0.2210.91\msedgewebview2.exe N/A |
| 0 N/A N/A 23484 C+G ...Programs\Microsoft VS Code\Code.exe N/A |
+---------------------------------------------------------------------------------------+
INFO:root:Package Versions:
INFO:root:absl-py>= 2.0.0
INFO:root:accelerate>= 0.25.0
INFO:root:aiofiles>= 23.2.1
INFO:root:aiohttp>= 3.9.1
INFO:root:aiosignal>= 1.3.1
INFO:root:altair>= 5.2.0
INFO:root:annotated-types>= 0.6.0
INFO:root:antlr4-python3-runtime>= 4.9.3
INFO:root:anyascii>= 0.3.2
INFO:root:anyio>= 3.7.1
INFO:root:appdirs>= 1.4.4
INFO:root:asttokens>= 2.4.1
INFO:root:attributedict>= 0.3.0
INFO:root:attrs>= 23.1.0
INFO:root:audioread>= 3.0.1
INFO:root:autoawq>= 0.1.7
INFO:root:auto-gptq>= 0.6.0+cu121
INFO:root:av>= 10.0.0
INFO:root:Babel>= 2.14.0
INFO:root:bangla>= 0.0.2
INFO:root:beautifulsoup4>= 4.12.2
INFO:root:bitsandbytes>= 0.41.1
INFO:root:blessings>= 1.7
INFO:root:blinker>= 1.7.0
INFO:root:blis>= 0.7.11
INFO:root:bnnumerizer>= 0.0.2
INFO:root:bnunicodenormalizer>= 0.1.6
INFO:root:cachetools>= 5.3.2
INFO:root:catalogue>= 2.0.10
INFO:root:certifi>= 2022.12.7
INFO:root:cffi>= 1.16.0
INFO:root:chardet>= 5.2.0
INFO:root:charset-normalizer>= 2.1.1
INFO:root:click>= 8.1.7
INFO:root:cloudpathlib>= 0.16.0
INFO:root:codecov>= 2.1.13
INFO:root:colorama>= 0.4.6
INFO:root:coloredlogs>= 15.0.1
INFO:root:colour-runner>= 0.1.1
INFO:root:confection>= 0.1.4
INFO:root:contourpy>= 1.2.0
INFO:root:coqpit>= 0.0.17
INFO:root:coverage>= 7.3.4
INFO:root:cramjam>= 2.7.0
INFO:root:ctransformers>= 0.2.27+cu121
INFO:root:ctranslate2>= 3.23.0
INFO:root:cycler>= 0.12.1
INFO:root:cymem>= 2.0.8
INFO:root:Cython>= 3.0.7
INFO:root:DataProperty>= 1.0.1
INFO:root:datasets>= 2.16.0
INFO:root:dateparser>= 1.1.8
INFO:root:decorator>= 5.1.1
INFO:root:deepdiff>= 6.7.1
INFO:root:deep-translator>= 1.9.2
INFO:root:dill>= 0.3.7
INFO:root:diskcache>= 5.6.3
INFO:root:distlib>= 0.3.8
INFO:root:docker-pycreds>= 0.4.0
INFO:root:docopt>= 0.6.2
INFO:root:einops>= 0.7.0
INFO:root:encodec>= 0.1.1
INFO:root:evaluate>= 0.4.1
INFO:root:executing>= 2.0.1
INFO:root:exllama>= 0.0.18+cu121
INFO:root:exllamav2>= 0.0.11+cu121
INFO:root:fastapi>= 0.105.0
INFO:root:faster-whisper>= 0.10.0
INFO:root:fastparquet>= 2023.10.1
INFO:root:ffmpeg>= 1.4
INFO:root:ffmpy>= 0.3.1
INFO:root:filelock>= 3.13.1
INFO:root:flash-attn>= 2.3.4
INFO:root:Flask>= 3.0.0
INFO:root:flask-cloudflared>= 0.0.14
INFO:root:flatbuffers>= 23.5.26
INFO:root:fonttools>= 4.47.0
INFO:root:frozenlist>= 1.4.1
INFO:root:fsspec>= 2023.10.0
INFO:root:g2pkk>= 0.1.2
INFO:root:gekko>= 1.0.6
INFO:root:gitdb>= 4.0.11
INFO:root:GitPython>= 3.1.40
INFO:root:google-auth>= 2.25.2
INFO:root:google-auth-oauthlib>= 1.2.0
INFO:root:gptq-for-llama>= 0.1.1+cu121
INFO:root:gradio>= 3.50.2
INFO:root:gradio_client>= 0.6.1
INFO:root:grpcio>= 1.60.0
INFO:root:gruut>= 2.2.3
INFO:root:gruut-ipa>= 0.13.0
INFO:root:gruut-lang-de>= 2.0.0
INFO:root:gruut-lang-en>= 2.0.0
INFO:root:gruut-lang-es>= 2.0.0
INFO:root:gruut-lang-fr>= 2.0.2
INFO:root:h11>= 0.14.0
INFO:root:hangul-romanize>= 0.1.0
INFO:root:hqq>= 0.1.1.post1
INFO:root:httpcore>= 1.0.2
INFO:root:httpx>= 0.26.0
INFO:root:huggingface-hub>= 0.20.1
INFO:root:humanfriendly>= 10.0
INFO:root:idna>= 3.4
INFO:root:importlib-metadata>= 7.0.1
INFO:root:importlib-resources>= 6.1.1
INFO:root:inflect>= 7.0.0
INFO:root:inspecta>= 0.1.3
INFO:root:ipython>= 8.19.0
INFO:root:itsdangerous>= 2.1.2
INFO:root:jamo>= 0.4.1
INFO:root:jedi>= 0.19.1
INFO:root:jieba>= 0.42.1
INFO:root:Jinja2>= 3.1.2
INFO:root:joblib>= 1.3.2
INFO:root:jsonlines>= 1.2.0
INFO:root:jsonschema>= 4.20.0
INFO:root:jsonschema-specifications>= 2023.11.2
INFO:root:kiwisolver>= 1.4.5
INFO:root:langcodes>= 3.3.0
INFO:root:lazy_loader>= 0.3
INFO:root:librosa>= 0.10.1
INFO:root:llama_cpp_python>= 0.2.24+cpuavx2
INFO:root:llama_cpp_python_cuda>= 0.2.24+cu121
INFO:root:llama_cpp_python_cuda_tensorcores>= 0.2.24+cu121
INFO:root:llvmlite>= 0.41.1
INFO:root:lm_eval>= 0.4.0
INFO:root:lxml>= 4.9.4
INFO:root:Markdown>= 3.5.1
INFO:root:markdown-it-py>= 3.0.0
INFO:root:MarkupSafe>= 2.1.3
INFO:root:matplotlib>= 3.8.2
INFO:root:matplotlib-inline>= 0.1.6
INFO:root:mbstrdecoder>= 1.1.3
INFO:root:mdurl>= 0.1.2
INFO:root:more-itertools>= 10.1.0
INFO:root:mpmath>= 1.3.0
INFO:root:msgpack>= 1.0.7
INFO:root:multidict>= 6.0.4
INFO:root:multiprocess>= 0.70.15
INFO:root:murmurhash>= 1.0.10
INFO:root:networkx>= 2.8.8
INFO:root:ngrok>= 0.12.1
INFO:root:ninja>= 1.11.1.1
INFO:root:nltk>= 3.8.1
INFO:root:num2words>= 0.5.13
INFO:root:numba>= 0.58.1
INFO:root:numexpr>= 2.8.8
INFO:root:numpy>= 1.24.4
INFO:root:oauthlib>= 3.2.2
INFO:root:omegaconf>= 2.3.0
INFO:root:onnxruntime>= 1.16.3
INFO:root:openai-whisper>= 20231117
INFO:root:optimum>= 1.16.1
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INFO:root:orjson>= 3.9.10
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INFO:root:pandas>= 1.5.3
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INFO:root:pathvalidate>= 3.2.0
INFO:root:peft>= 0.7.1
INFO:root:Pillow>= 10.1.0
INFO:root:pip>= 23.3.1
INFO:root:platformdirs>= 4.1.0
INFO:root:pluggy>= 1.3.0
INFO:root:pooch>= 1.8.0
INFO:root:portalocker>= 2.8.2
INFO:root:preshed>= 3.0.9
INFO:root:prompt-toolkit>= 3.0.43
INFO:root:protobuf>= 4.23.4
INFO:root:psutil>= 5.9.7
INFO:root:pure-eval>= 0.2.2
INFO:root:pyarrow>= 14.0.2
INFO:root:pyarrow-hotfix>= 0.6
INFO:root:pyasn1>= 0.5.1
INFO:root:pyasn1-modules>= 0.3.0
INFO:root:pybind11>= 2.11.1
INFO:root:pycparser>= 2.21
INFO:root:pydantic>= 2.5.3
INFO:root:pydantic_core>= 2.14.6
INFO:root:pydub>= 0.25.1
INFO:root:Pygments>= 2.17.2
INFO:root:pynndescent>= 0.5.11
INFO:root:pyparsing>= 3.1.1
INFO:root:pypinyin>= 0.50.0
INFO:root:pyproject-api>= 1.6.1
INFO:root:pyreadline3>= 3.4.1
INFO:root:pysbd>= 0.3.4
INFO:root:pytablewriter>= 1.2.0
INFO:root:python-crfsuite>= 0.9.10
INFO:root:python-dateutil>= 2.8.2
INFO:root:python-multipart>= 0.0.6
INFO:root:pytz>= 2023.3.post1
INFO:root:pywin32>= 306
INFO:root:PyYAML>= 6.0.1
INFO:root:py-cpuinfo>= 9.0.0
INFO:root:referencing>= 0.32.0
INFO:root:regex>= 2023.12.25
INFO:root:requests>= 2.31.0
INFO:root:requests-oauthlib>= 1.3.1
INFO:root:responses>= 0.18.0
INFO:root:rich>= 13.7.0
INFO:root:rootpath>= 0.1.1
INFO:root:rouge>= 1.0.1
INFO:root:rouge-score>= 0.1.2
INFO:root:rpds-py>= 0.15.2
INFO:root:rsa>= 4.9
INFO:root:sacrebleu>= 2.4.0
INFO:root:safetensors>= 0.4.1
INFO:root:scikit-learn>= 1.3.2
INFO:root:scipy>= 1.11.4
INFO:root:semantic-version>= 2.10.0
INFO:root:sentencepiece>= 0.1.99
INFO:root:sentry-sdk>= 1.39.1
INFO:root:setproctitle>= 1.3.3
INFO:root:setuptools>= 68.2.2
INFO:root:six>= 1.16.0
INFO:root:smart-open>= 6.4.0
INFO:root:smmap>= 5.0.1
INFO:root:sniffio>= 1.3.0
INFO:root:soundfile>= 0.12.1
INFO:root:soupsieve>= 2.5
INFO:root:soxr>= 0.3.7
INFO:root:spacy>= 3.7.2
INFO:root:spacy-legacy>= 3.0.12
INFO:root:spacy-loggers>= 1.0.5
INFO:root:SpeechRecognition>= 3.10.0
INFO:root:sqlitedict>= 2.1.0
INFO:root:srsly>= 2.4.8
INFO:root:sse-starlette>= 1.6.5
INFO:root:stack-data>= 0.6.3
INFO:root:starlette>= 0.27.0
INFO:root:SudachiDict-core>= 20230927
INFO:root:SudachiPy>= 0.6.8
INFO:root:sympy>= 1.12
INFO:root:tabledata>= 1.3.3
INFO:root:tabulate>= 0.9.0
INFO:root:tcolorpy>= 0.1.4
INFO:root:tensorboard>= 2.15.1
INFO:root:tensorboard-data-server>= 0.7.2
INFO:root:termcolor>= 2.4.0
INFO:root:texttable>= 1.7.0
INFO:root:thinc>= 8.2.2
INFO:root:threadpoolctl>= 3.2.0
INFO:root:tiktoken>= 0.5.2
INFO:root:timm>= 0.9.12
INFO:root:tokenizers>= 0.15.0
INFO:root:toml>= 0.10.2
INFO:root:toolz>= 0.12.0
INFO:root:torch>= 2.1.2+cu121
INFO:root:torchaudio>= 2.1.2+cu121
INFO:root:torchvision>= 0.16.2+cu121
INFO:root:tox>= 4.11.4
INFO:root:tqdm>= 4.66.1
INFO:root:tqdm-multiprocess>= 0.0.11
INFO:root:trainer>= 0.0.36
INFO:root:traitlets>= 5.14.0
INFO:root:transformers>= 4.36.2
INFO:root:TTS>= 0.22.0
INFO:root:typepy>= 1.3.2
INFO:root:typer>= 0.9.0
INFO:root:typing_extensions>= 4.9.0
INFO:root:tzdata>= 2023.3
INFO:root:tzlocal>= 5.2
INFO:root:umap-learn>= 0.5.5
INFO:root:Unidecode>= 1.3.7
INFO:root:urllib3>= 1.26.13
INFO:root:uvicorn>= 0.25.0
INFO:root:virtualenv>= 20.25.0
INFO:root:wandb>= 0.16.1
INFO:root:wasabi>= 1.1.2
INFO:root:wcwidth>= 0.2.12
INFO:root:weasel>= 0.3.4
INFO:root:websockets>= 11.0.3
INFO:root:Werkzeug>= 3.0.1
INFO:root:wheel>= 0.41.2
INFO:root:xxhash>= 3.4.1
INFO:root:yarl>= 1.9.4
INFO:root:zipp>= 3.17.0
INFO:root:zstandard>= 0.22.0
```
### Additional context
No problems on TTS 0.21.3 as mentioned. Only on TTS 0.22.0 | 1medium
|
Title: How to configure multiple folders in the same zip package
Body: How should I write "config" in readme when all the data, such as train test, is in a zip file
train floder and test floder in data.zip | 3misc
|
Title: Add support for user defined column aliases
Body: Currently, user defined aliases aren't correctly processed. Fix this and add full alias support. | 1medium
|
Title: Add parameter axis to tversky loss
Body: Add parameter axis to tversky loss similar to dice loss.
I can implement that if someone give me green light :) | 1medium
|
Title: Make Panel Extensions visible
Body: Now that Panel extensions start to pop up I believe we should maintain a list somewhere. I thought about `panel-extensions` org. But that is not visible enough.
I think we should have a page similar to our Panel Components page or integrated with the Panel Components page (like Streamlit does).
At first we could just list, describe and link to them. Later we could add thumbnails if we believe it creates value.
----
```markdown
## 📚 Discover Extensions
Here we maintain a list of Panel Extensions maintained inside and outside this organisation.
### Widgets
#### [`panel-material-ui`](https://github.com/panel-extensions/panel-material-ui)
An extension to bring MaterialUI components to Panel.
### Visualizations
#### [`panel-graphic-walker`](https://github.com/panel-extensions/panel-graphic-walker)
A Tableau like *Graphic Walker* pane for use with HoloViz Panel.
### Jupyter Extensions
#### [`jupyter_bokeh`](https://github.com/bokeh/jupyter_bokeh)
A Jupyter extension for rendering Bokeh and Panel content within Jupyter environments.
#### [`ipywidgets_bokeh`](https://github.com/bokeh/ipywidgets_bokeh)
Support for using Ipywidgets within Bokeh and Panel.
### Templates
``` | 0easy
|
Title: [BUG] WebUI login only for admin users
Body: ### Description
After new installation of paperless-ngx 2.10.2 (docker) and import of documents via document_importer WebUI user login is only possible for users with admin rights
### Steps to reproduce
1. Delete browsing data
2. Enter https://localhost:8000
### Webserver logs
```bash
[2024-07-12 15:05:00,207] [DEBUG] [paperless.classifier] Gathering data from database...
[2024-07-12 15:05:00,666] [INFO] [paperless.classifier] No updates since last training
[2024-07-12 15:05:00,668] [DEBUG] [paperless.tasks] Training data unchanged.
[2024-07-12 16:05:00,208] [DEBUG] [paperless.classifier] Gathering data from database...
[2024-07-12 16:05:00,666] [INFO] [paperless.classifier] No updates since last training
[2024-07-12 16:05:00,668] [DEBUG] [paperless.tasks] Training data unchanged.
[2024-07-12 17:05:00,206] [DEBUG] [paperless.classifier] Gathering data from database...
[2024-07-12 17:05:00,676] [INFO] [paperless.classifier] No updates since last training
[2024-07-12 17:05:00,678] [DEBUG] [paperless.tasks] Training data unchanged.
```
### Browser logs
_No response_
### Paperless-ngx version
2.10.2
### Host OS
Ubuntu 22.04
### Installation method
Docker - official image
### System status
_No response_
### Browser
Chrome
### Configuration changes
add in docker-compose.env PAPERLESS_FILENAME_FORMAT={owner_username}/{correspondent}/{created_year}/{created_month}/{title}
### Please confirm the following
- [X] I believe this issue is a bug that affects all users of Paperless-ngx, not something specific to my installation.
- [X] I have already searched for relevant existing issues and discussions before opening this report.
- [X] I have updated the title field above with a concise description. | 1medium
|
Title: On fields if `pattern` parameter is added swagger execute request button when clicked has no effect
Body: Here is the minimum code required to replay the error:
```python
from flask import Flask, request
from flask_restx import Resource, Api, fields
app = Flask(__name__)
api = Api(app)
simple_email_model = api.model("email_model", dict(
email=fields.String(required=True, pattern=".+@.+")
))
@api.route('/email')
class TestEmail(Resource):
@api.expect(simple_email_model, validate=True)
def post(self):
return request.json
if __name__ == '__main__':
app.run(debug=True)
```
I expect if pattern doesn't match the validation should fail.
But, when I go to swagger-ui, complete the field and click on `Execute` button - nothing happens (the request is not made, button is freezed).
### **Environment**
- Python version - 3.8
- Flask version - 2.0.3
- Flask-RESTX version - 0.5.1
| 1medium
|
Title: Exported quantized model wasn’t reduced in model size
Body: # Ask a Question
I have a model from sentence- transformers/all-mini-L6-v2 and I have applied dynamic quantization on it that reduces the model size by 75%
Initially 90 MB
After Quantization: 25 MB
But when I convert the model to onnx format using torch.onnx.export it is giving same size as the non quantized model in onnx format
### Question
<!-- Explain your question here. -->
Does onnx format effectively represents quantization
### Further information
- Relevant Area: <!--e.g., model usage, backend, best practices, converters, shape_inference, version_converter, training, test, operators, IR, ONNX Hub, data preprocessing, CI pipelines. -->
- Is this issue related to a specific model?
**Model name**: <!-- *e.g. mnist* --> sentence- transformers/ all-mini-L6-v2
**Model opset**: <!-- *e.g. 17* --> 14
### Notes
<!-- Any additional information, code snippets. -->
| 2hard
|
Title: Issue when execute on AMD
Body: **Crash reports MUST be included when reporting bugs.**
**Describe the bug**
Just executed on an AMD PC with python version higher than 3.8
**To Reproduce**
Run the cmd or gui version
**Expected behavior**
Show this message
The AMD version of Faceswap cannot run on versions of Python higher than 3.8
The solution is to cast the device.get() to int (line 109)
| 0easy
|
Title: Question: How to fix ImportError: DLL load failed: The specified procedure could not be found. Windows + Anaconda
Body: **Here is what I have done so far to setup LabelImg**
**Windows + Anaconda**
Install Python, PyQt5 and install lxml
git clone https://github.com/tzutalin/labelImg
I then had to moved to the **labelImg** directory using cd labelImg
Next, pyrcc5 -o libs/resources.py resources.qrc
**Here come the error response**
`(base) C:\Users\8470p\labelImg>pyrcc5 -o libs/resources.py resources.qrc
Traceback (most recent call last):
File "C:\Users\8470p\Anaconda3\lib\runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "C:\Users\8470p\Anaconda3\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\8470p\Anaconda3\lib\site-packages\PyQt5\pyrcc_main.py", line 23, in <module>
from .pyrcc import *
ImportError: DLL load failed: The specified procedure could not be found.
` | 1medium
|
Title: WidgetControl: display other widgets on the same map
Body: See https://github.com/jupyter-widgets/ipyleaflet/blob/1a8e57375ee79ee08a76049ca33a61211b827b83/python/jupyter_leaflet/src/controls/WidgetControl.ts#L10
 | 1medium
|
Title: Exporting yolov5 model as engine format never finishes
Body: ### Search before asking
- [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and found no similar bug report.
### YOLOv5 Component
Export
### Bug
This is what happens when exporting with onnx format:
everything works as normal.
And this is what happens when exporting with engine format:
It fails and just stops with no further information
I have seen another user @SeanSon2005 having the same issue in yolov8 but he wasn't clear in how he solved it and there are no similar issues on yolov5. Also neither yolov8's nor yolov5's export files have been updated to resolve the problem.
### Environment
- YOLOv5 2023-11-22 Python-3.11.6 torch-2.1.1+cu118 CUDA:0 (NVIDIA GeForce RTX 4070, 12282MiB)
- Windows 11
- Python 3.11.6
- tensorrt 8.6.1
### Minimal Reproducible Example
Just the export.py file from the yolov5 ultralitics repo
### Additional
@SeanSon2005 who was having the same problem on yolov8 says that he fixed the problem by going into the Yolo export.py and moving the import tensorrt as trt code segment after getting the onnx export but I wasnt able to replicate that so if anyone follow up on that or elaborate on where I need to move the import tensorrt as trt code segment I would appreciate it even if it doesnt solve the issue.
### Are you willing to submit a PR?
- [ ] Yes I'd like to help by submitting a PR! | 2hard
|
Title: Use codon write python module instead of C?
Body: Can I use codon write python module like C?
If it is, where can I find examples? | 3misc
|
Title: `None` RawKernel argument is turned into 0xffffffff instead of NULL pointer
Body: ### Description
I spent this evening chasing a memory corruption bug. As it turns out, the cause was that a `None` argument passed to a `cupy.RawKernel` turned into a pointer with the address `0xffffffff` (i.e. `-1`). I would have expected the address `0`, or at least an address equal to `NULL` or maybe `nullptr`, but `0xffffffff` caught me by surprise. What is going on there?
I found that I can pass `0` from Python to get a `NULL` pointer in the kernel, but it feels a bit dirty, because `0` is of type `int`, but the usual arguments is of type `cupy.ndarray`. Is it okay to just pass `0` when one wants `NULL`, or is there a "more proper" way to obtain a `NULL` value in Python?
### To Reproduce
```python
import cupy
# This prints
#0xffffffff (nil)
#0xffffffff (nil)
source = r"""
extern "C" __global__
void kernel(const float *ptr){
printf("%p %p\n", ptr, NULL);
}
"""
grid_dim = (1,)
block_dim = (2,)
args = (None,)
cupy.RawKernel(source, "kernel")(grid_dim, block_dim, args)
```
### Installation
Wheel (`pip install cupy-***`)
### Environment
```
OS : Linux-6.8.0-55-generic-x86_64-with-glibc2.39
Python Version : 3.12.4
CuPy Version : 13.3.0
CuPy Platform : NVIDIA CUDA
NumPy Version : 1.26.3
SciPy Version : 1.14.1
Cython Build Version : 0.29.36
Cython Runtime Version : None
CUDA Root : /usr/local/cuda
nvcc PATH : /usr/local/cuda/bin/nvcc
CUDA Build Version : 12060
CUDA Driver Version : 12060
CUDA Runtime Version : 12060 (linked to CuPy) / 12060 (locally installed)
CUDA Extra Include Dirs : []
cuBLAS Version : 120604
cuFFT Version : 11300
cuRAND Version : 10307
cuSOLVER Version : (11, 7, 1)
cuSPARSE Version : 12504
NVRTC Version : (12, 6)
Thrust Version : 200600
CUB Build Version : 200600
Jitify Build Version : <unknown>
cuDNN Build Version : None
cuDNN Version : None
NCCL Build Version : None
NCCL Runtime Version : None
cuTENSOR Version : None
cuSPARSELt Build Version : None
Device 0 Name : NVIDIA GeForce RTX 3060 Laptop GPU
Device 0 Compute Capability : 86
Device 0 PCI Bus ID : 0000:01:00.0
```
### Additional Information
_No response_ | 1medium
|
Title: [🕹️]Write a Article Comparing OpenBB and Other Financial Tools
Body: ### What side quest or challenge are you solving?
Write a Article Comparing OpenBB and Other Financial Tools
### Points
300
### Description
Wrote a article on medium comparing OpenBB and other financial tools
### Provide proof that you've completed the task
13-oct-2024 by suryadev pandey >> Link to Article : https://medium.com/@pandeysuryadev00/openbb-a-new-era-of-investment-research-dc81de518243 | 3misc
|
Title: Supporting inconsistent schemas in read_json
Body: If you have two (jsonl) files where one contains columns `{"id", "text"}` and the other contains `{"text", "id", "meta"}` and you wish to read the two files using `dd.read_json([file1.jsonl, file2.jsonl], lines=True)` we run into an error
```
Metadata mismatch found in `from_delayed`.
Partition type: `pandas.core.frame.DataFrame`
(or it is Partition type: `cudf.core.dataframe.DataFrame` when backend=='cudf')
+---------+-------+----------+
| Column | Found | Expected |
+---------+-------+----------+
| 'meta1' | - | object |
+---------+-------+----------+
```
For what it's worth this isn't an issue in read_parquet (cpu) and for gpu the fix is in the works https://github.com/rapidsai/cudf/pull/17554/files
## Guessing the rootcause
IIUC in both pandas and cudf, we call `read_json_file` ([here](https://github.com/dask/dask/blob/a9396a913c33de1d5966df9cc1901fd70107c99b/dask/dataframe/io/json.py#L315)).
In the pandas case, even if `dtype` is specified, pandas doesn't prune out the non-specified columns, while cudf does (assuming prune_columns=True). Therefore the pandas case continues to fail, while `cudf` case fails on a column order vs metadata column order mismatch error (since one file has `id, text`, while the other has `text, id`.
One possible hack could be supporting `columns` arg and then performing `engine(.....)[columns]`. Another could be
## MRE
```python
import dask.dataframe as dd
import dask
import tempfile
import pandas as pd
import os
records = [
{"id": 123, "text": "foo"},
{
"text": "bar",
"meta1": [{"field1": "cat"}],
"id": 456,
},
]
columns = ["text", "id"]
with tempfile.TemporaryDirectory() as tmpdir:
file1 = os.path.join(tmpdir, "part.0.jsonl")
file2 = os.path.join(tmpdir, "part.1.jsonl")
pd.DataFrame(records[:1]).to_json(file1, orient="records", lines=True)
pd.DataFrame(records[1:]).to_json(file2, orient="records", lines=True)
for backend in ["pandas", "cudf"]:
read_kwargs = dict()
if backend == "cudf":
read_kwargs["dtype"] = {"id": "str", "text": "str"}
read_kwargs["prune_columns"] = True
print("="*30)
print(f"==== {backend=} ====")
print("="*30)
try:
with dask.config.set({"dataframe.backend": backend}):
df = dd.read_json(
[file1, file2],
lines=True,
**read_kwargs,
)
print(f"{df.columns=}")
print(f"{df.compute().columns=}")
print(f"{type(df.compute())=}")
display((df.compute()))
except Exception as e:
print(f"{backend=} failed due to {e} \n")
```
cc @rjzamora
| 2hard
|
Title: Introducing a Version of the Enhanced Mycodo Integration for Home Assistant HACS
Body: Hello everyone,
I'm thrilled to announce the release of an enhanced Mycodo integration for Home Assistant, specifically designed for compatibility with HACS.
### Release Information
This is a **beta version** of the integration. I've thoroughly tested it on my own, but I am eager to hear your feedback and to identify any issues you may encounter. Here are the details of this release:
- **GitHub Link:** [Enhanced Mycodo Integration](https://github.com/zachi40/home-assistant-mycodo)
### Adherence to Community Guidelines
I am dedicated to ensuring that this integration complies with all community guidelines and standards. If you notice any issues or believe that my modifications inadvertently breach community rules, please contact me immediately. I am prepared to make necessary changes or remove the content if needed.
I am excited to see how this enhancement can serve the wider Home Assistant and Mycodo communities and am looking forward to your feedback.
Thank you for your support and interest!
| 3misc
|
Title: Superuser login fails after updating docker desktop
Body: ### Actions before raising this issue
- [X] I searched the existing issues and did not find anything similar.
- [X] I read/searched [the docs](https://docs.cvat.ai/docs/)
### Steps to Reproduce
_No response_
### Expected Behavior
_No response_
### Possible Solution
_No response_
### Context
I created a new superuser account with [docker exec -it cvat_server bash -ic 'python3 ~/manage.py createsuperuser'], but the error 'Unable to log in with provided credentials' occurred. If I create a regular account, I can log in with that account.
If there's any change since the last login, it's about [1. downgraded the numpy version 2. updated the version of docker desktop to the latest version], but after you reboot the pc after that update, you've seen pulling the container when you use the command [docker compose -f docker-compose.yml -f components/serverless/docker-compose.yml up -d]. Is there a way to log in as a superuser? I want to back up data such as project/task/annotation.
This is the error message on browser console.
server-proxy.ts:467
POST http://localhost:8080/api/auth/login?org= 400 (Bad Request)
(Anonymous) @ xhr.js:195
xhr @ xhr.js:15
Jo @ dispatchRequest.js:51
Promise.then
_request @ Axios.js:149
request @ Axios.js:40
(Anonymous) @ Axios.js:212
(Anonymous) @ bind.js:5
login @ server-proxy.ts:467
(Anonymous) @ api-implementation.ts:106
apiWrapper @ plugins.ts:43
await in apiWrapper
login @ api.ts:77
(Anonymous) @ auth-actions.ts:102
(Anonymous) @ index.js:16
t.<computed> @ bindActionCreators.js:8
onSubmit @ login-page.tsx:44
onFinish @ login-form.tsx:112
onFinish @ Form.js:73
(Anonymous) @ useForm.js:855
Promise.then
(Anonymous) @ useForm.js:851
onSubmit @ Form.js:130
Re @ react-dom.production.min.js:54
Be @ react-dom.production.min.js:54
(Anonymous) @ react-dom.production.min.js:55
zo @ react-dom.production.min.js:105
$o @ react-dom.production.min.js:106
(Anonymous) @ react-dom.production.min.js:117
uc @ react-dom.production.min.js:274
Pe @ react-dom.production.min.js:52
Vo @ react-dom.production.min.js:109
qn @ react-dom.production.min.js:74
Gn @ react-dom.production.min.js:73
cvat-app.tsx:421
Unable to log in with provided credentials.
o @ cvat-app.tsx:421
showErrors @ cvat-app.tsx:431
componentDidUpdate @ cvat-app.tsx:306
As @ react-dom.production.min.js:261
_s @ react-dom.production.min.js:260
ys @ react-dom.production.min.js:259
(Anonymous) @ react-dom.production.min.js:283
wc @ react-dom.production.min.js:281
cc @ react-dom.production.min.js:273
Wl @ react-dom.production.min.js:127
(Anonymous) @ react-dom.production.min.js:267
XMLHttpRequest.send
(Anonymous) @ xhr.js:195
xhr @ xhr.js:15
Jo @ dispatchRequest.js:51
Promise.then
_request @ Axios.js:149
request @ Axios.js:40
(Anonymous) @ Axios.js:212
(Anonymous) @ bind.js:5
login @ server-proxy.ts:467
(Anonymous) @ api-implementation.ts:106
apiWrapper @ plugins.ts:43
await in apiWrapper
login @ api.ts:77
(Anonymous) @ auth-actions.ts:102
(Anonymous) @ index.js:16
t.<computed> @ bindActionCreators.js:8
onSubmit @ login-page.tsx:44
onFinish @ login-form.tsx:112
onFinish @ Form.js:73
(Anonymous) @ useForm.js:855
Promise.then
(Anonymous) @ useForm.js:851
onSubmit @ Form.js:130
Re @ react-dom.production.min.js:54
Be @ react-dom.production.min.js:54
(Anonymous) @ react-dom.production.min.js:55
zo @ react-dom.production.min.js:105
$o @ react-dom.production.min.js:106
(Anonymous) @ react-dom.production.min.js:117
uc @ react-dom.production.min.js:274
Pe @ react-dom.production.min.js:52
Vo @ react-dom.production.min.js:109
qn @ react-dom.production.min.js:74
Gn @ react-dom.production.min.js:73
### Environment
```Markdown
WSL2 Ubuntu 22.04
Docker desktop 4.33.1>4.34.2(docker engine 27.1.1>27.2.0)
GPU RTX4090
```
| 1medium
|
Title: Why does Flask-SocketIO require sticky sessions? (and standard practices)
Body: Hi,
I was wondering why are sticky sessions required for hosting flask-socketio server. From my understanding, sticky sessions are basically used to route a request to the same server where the session originated.
1,) (this might be trivial since I am fairly new to backend development) -- why do you need sticky sessions for multiple workers in the same server. Isn't it possible for them to share session information?
2.) I wanted to confirm - the eventlet flask-socketio server doesn't use multiple workers by default right?
3.) To store the session id's I am using a dictionary in flask app with keys as a random unique id for each client and values as session-id as follows.
```
app = Flask(__name__)
app.clients = {}
# SocketIO
socketio = SocketIO(app)
@socketio.on('connect', namespace='/custom')
def events_connect():
userid = str(uuid.uuid4())
session['userid'] = userid
current_app.clients[userid] = request.sid
emit('userid', {'userid': userid})
```
Is there a suggested/standard approach to store the room information? I believe this should be thread-safe and should work even if I use multiple workers.
Thanks,
| 3misc
|
Title: [Ray Core] `ray.data.Dataset.repartition` not working consistently with doc/error message
Body: ### What happened + What you expected to happen
```
ds = ds.repartition(num_blocks=x, target_num_rows_per_block=y)
```
Obviously it's conflicting to setting both. Calling this gives the error `ValueError: Either num_blocks or target_num_rows_per_block must be set, but not both.`, as expected.
But this doesn't work `ds = ds.repartition(target_num_rows_per_block=y)`. I got `TypeError: Dataset.repartition() missing 1 required positional argument: 'num_blocks'`
This doesn't seem to make sense to me. According to [doc](https://docs.ray.io/en/latest/data/api/doc/ray.data.Dataset.repartition.html), it should be happy with one of the 2 arguments given.
### Versions / Dependencies
ray 2.43.0
Python 3.11.11
Ubuntu 22.04.3 LTS
### Reproduction script
```
import ray
ds = ray.data.range(100)
ds.repartition(target_num_rows_per_block=10)
```
### Issue Severity
None | 1medium
|
Title: SiamRPN vs SiamRPN++
Body: Hi! The models with the name "siamrpn" in MODEL_ZOO are the SiamRPN++ or the SiamRPN ones? Thank you! | 3misc
|
Title: 2d106track
Body: When will you release the pre-trained model 2d106track? | 3misc
|
Title: In search of Swag Designer!
Body: We continue to see the importance of building a high-quality dataset set for fine-tuning. To promote additional support, we want to offer people t-shirts, stickers, and other types of swag for hitting different internal milestones. If you would like to assist, feel free to post a few links to some design ideas you have. | 3misc
|
Title: OSError [Error 2] – Incorrect Path and Missing .DS_Store File in Site-Packages
Body: ### Checklist
- [x] I have searched the [existing issues](https://github.com/streamlit/streamlit/issues) for similar issues.
- [x] I added a very descriptive title to this issue.
- [x] I have provided sufficient information below to help reproduce this issue.
### Summary
There is an issue with installing the Python package Streamlit in a school computer lab environment running Windows 10. While the package can be successfully installed when logged in as a local administrator, it fails to install for network users (students). Even manual installation attempts do not resolve the problem. The installation process encounters errors, despite other Python packages being installed without any issues. This suggests that the problem is specific to Streamlit, and may be related to user permissions, path configurations, or network-related restrictions that are preventing the package from being installed for non-administrative users. This issue is hindering the ability for students to install the necessary package for their work in the lab.
`ERROR: Could not install packages due to an OSError: [Error 2] No such file or directory:
'I:\\Remote.V2\\AppData\\Python\\Python310\\site-packages\\.DS_Store‘`
### Reproducible Code Example
```Python
```
### Steps To Reproduce
1. Log in as a network user (student) on a Windows 10 machine in the computer lab.
2. Attempt to install Streamlit using pip install streamlit.
3. Observe the installation failure, with no errors for other Python packages.
4. Try manual installation of Streamlit; observe that it still doesn’t work for network users.
5. Log in as a local administrator and install Streamlit successfully.
### Expected Behavior
Streamlit should install successfully for all users (including network users) with the correct installation path configured. The package should be recognized and functional, allowing students to run streamlit run without errors. Other Python packages should also install and work seamlessly for network users, just as they do for local administrators.
### Current Behavior
`
ERROR: Could not install packages due to an OSError: [Error 2] No such file or directory:
'I:\\Remote.V2\\AppData\\Python\\Python310\\site-packages\\.DS_Store‘
`
### Is this a regression?
- [ ] Yes, this used to work in a previous version.
### Debug info
- Streamlit version: 1.41.1
- Python version: 3.10 (https://thonny.org/)
- Operating System: Windows 10
- Browser: Chrome / Edge
### Additional Information
_No response_ | 2hard
|
Title: DNS configuration is not displayed
Body: It is not possible to display DNS configuration help for a domain in the new UI. | 1medium
|
Title: ChatInterface strips unused tags on display
Body: ### Describe the bug
put
```
test
<test>
test
```
Into
```python
import gradio as gr
def fn(a, b):
print(a)
return a
with gr.Blocks() as demo:
gr.ChatInterface(fn)
demo.launch()
```
The displayed text only shows `test` and not the subsequent two lines
### Have you searched existing issues? 🔎
- [x] I have searched and found no existing issues
### Severity
I can work around it | 1medium
|
Title: Trax Import Error
Body: ### Description
Hello, I am having issues with the tensorflow imports while using trax, I've never experienced the issue before and not sure how to solve it.
### Environment information
---------------------------------------------------------------------------
NotFoundError Traceback (most recent call last)
<ipython-input-2-8b607b832815> in <module>
11 from nltk.tokenize import sent_tokenize, word_tokenize
12 from nltk.stem import WordNetLemmatizer
---> 13 import trax
14 import trax.fastmath.numpy as np
15 from trax import layers as tl
~/opt/anaconda3/lib/python3.7/site-packages/trax/__init__.py in <module>
16 """Trax top level import."""
17
---> 18 from trax import data
19 from trax import fastmath
20 from trax import layers
~/opt/anaconda3/lib/python3.7/site-packages/trax/data/__init__.py in <module>
67 from trax.data.inputs import UniformlySeek
68
---> 69 from trax.data.tf_inputs import add_eos_to_output_features
70 from trax.data.tf_inputs import BertGlueEvalStream
71 from trax.data.tf_inputs import BertGlueTrainStream
~/opt/anaconda3/lib/python3.7/site-packages/trax/data/tf_inputs.py in <module>
30 import numpy as np
31 import scipy
---> 32 import t5.data
33 import tensorflow as tf
34 import tensorflow_datasets as tfds
~/opt/anaconda3/lib/python3.7/site-packages/t5/__init__.py in <module>
15 """Import API modules."""
16
---> 17 import t5.data
18 import t5.evaluation
19 import t5.models
~/opt/anaconda3/lib/python3.7/site-packages/t5/data/__init__.py in <module>
15 """Import data modules."""
16
---> 17 from t5.data.dataset_providers import * # pylint:disable=wildcard-import
18 from t5.data.feature_converters import * # pylint:disable=wildcard-import
19 from t5.data.glue_utils import * # pylint:disable=wildcard-import
~/opt/anaconda3/lib/python3.7/site-packages/t5/data/dataset_providers.py in <module>
30 import dataclasses
31 import gin
---> 32 from t5.data import utils
33 from t5.data.preprocessors import tokenize as tokenize_preprocessor
34 from t5.data.vocabularies import Vocabulary
~/opt/anaconda3/lib/python3.7/site-packages/t5/data/utils.py in <module>
25 import gin
26 import numpy as np
---> 27 from t5.data import vocabularies
28 import tensorflow.compat.v2 as tf
29 import tensorflow_datasets as tfds
~/opt/anaconda3/lib/python3.7/site-packages/t5/data/vocabularies.py in <module>
21
22 import tensorflow.compat.v2 as tf
---> 23 import tensorflow_text as tf_text
24
25 import sentencepiece as sentencepiece_processor
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_text/__init__.py in <module>
19 # pylint: disable=wildcard-import
20 from tensorflow_text.python import keras
---> 21 from tensorflow_text.python import metrics
22 from tensorflow_text.python.ops import *
23
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_text/python/metrics/__init__.py in <module>
18
19 # pylint: disable=wildcard-import
---> 20 from tensorflow_text.python.metrics.text_similarity_metric_ops import *
21
22 # Public symbols in the "tensorflow_text.metrics" package.
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow_text/python/metrics/text_similarity_metric_ops.py in <module>
26 from tensorflow.python.framework import load_library
27 from tensorflow.python.platform import resource_loader
---> 28 gen_text_similarity_metric_ops = load_library.load_op_library(resource_loader.get_path_to_datafile('_text_similarity_metric_ops.dylib'))
29
30
~/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/load_library.py in load_op_library(library_filename)
56 RuntimeError: when unable to load the library or get the python wrappers.
57 """
---> 58 lib_handle = py_tf.TF_LoadLibrary(library_filename)
59 try:
60 wrappers = _pywrap_python_op_gen.GetPythonWrappers(
NotFoundError: dlopen(/Users/lexroverts/opt/anaconda3/lib/python3.7/site-packages/tensorflow_text/python/metrics/_text_similarity_metric_ops.dylib, 6): Symbol not found: __ZN10tensorflow15TensorShapeBaseINS_11TensorShapeEEC2EN4absl14lts_2020_02_254SpanIKxEE
Referenced from: /Users/lexroverts/opt/anaconda3/lib/python3.7/site-packages/tensorflow_text/python/metrics/_text_similarity_metric_ops.dylib
Expected in: /Users/lexroverts/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/../libtensorflow_framework.2.dylib
in /Users/lexroverts/opt/anaconda3/lib/python3.7/site-packages/tensorflow_text/python/metrics/_text_similarity_metric_ops.dylib
```
OS: 10.15.7
$ pip freeze | grep trax
# your output here
trax==1.3.9
$ pip freeze | grep tensor
# your output here
mesh-tensorflow==0.1.18
tensor2tensor==1.15.7
tensorboard==2.5.0
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.0
tensorflow==2.5.0
tensorflow-addons==0.12.1
tensorflow-datasets==4.2.0
tensorflow-estimator==2.5.0
tensorflow-gan==2.0.0
tensorflow-hub==0.11.0
tensorflow-metadata==0.27.0
tensorflow-probability==0.7.0
tensorflow-text==2.4.3
$ pip freeze | grep jax
# your output here
jax==0.2.9
jaxlib==0.1.59
$ python -V
# Python 3.7.6
```
| 1medium
|
Title: Integration with fusionauth?
Body: Hello,
I'm hoping to use [Inventree](https://inventree.readthedocs.io/en/latest/) for our makerspace and connect it to [FusionAuth](https://fusionauth.io/) via django-allauth (Inventree already uses this library), however I can't see FusionAuth listed as an Auth provider.
I know that FusionAuth will provide generic OAuth credentials, but I can't see how to configure a generic OAuth provider in the django-allauth config, and FusionAuth is not listed.
Have I missed a step in the documentation somewhere? | 1medium
|
Title: v2.8
Body: yarn install v1.22.10
[1/4] Resolving packages...
success Already up-to-date.
Done in 0.46s.
yarn run v1.22.10
$ tsc && jupyter labextension build .
Please supply at least one subcommand: check, disable, enable, install, link, list, uninstall, unlink
error Command failed with exit code 1.
info Visit https://yarnpkg.com/en/docs/cli/run for documentation about this command.
Traceback (most recent call last):
File "setup.py", line 300, in <module>
_setup()
File "setup.py", line 142, in _setup
'develop': Develop, | 1medium
|
Title: Codegen should be able to know the `__typename` for an object
Body: <!--- Provide a general summary of the changes you want in the title above. -->
<!--- This template is entirely optional and can be removed, but is here to help both you and us. -->
<!--- Anything on lines wrapped in comments like these will not show up in the final text. -->
## Feature Request Type
- [ ] Core functionality
- [x] Alteration (**backward compatible enhancement**/optimization) of existing feature(s)
- [ ] New behavior
## Description
The code generator doesn't (currently) know very much about the GraphQL types. e.g. a query like:
```
query OperationName {
__typename
union {
... on Animal {
age
}
... on Person {
name
}
}
}
```
Will generate classes like:
```py
class OperationNameResultUnionAnimal:
# typename: Animal
age: int
class OperationNameResultUnionPerson:
# typename: Person
name: str
OperationNameResultUnion = Union[OperationNameResultUnionAnimal, OperationNameResultUnionPerson]
class OperationNameResult:
union: OperationNameResultUnion
```
In principle, the `__typename` field of the response should tell us whether we are going to get an `Animal` or a `Person`, but it would be very handy for client generators to be able to easily construct the following mapping:
```py
{"Animal": OperationNameResultUnionAnimal, "Person": OperationNameResultUnionPerson}
```
Currently, this is very hard to do with the information exposed to the client generator. Let's plumb that information through so that clients can start to take advantage of it. | 1medium
|
Title: Pytest integration examples are broken in the documentation
Body: All terminal output examples are broken on the [pytest integration page](https://15r10nk.github.io/inline-snapshot/pytest/) in the documentation.
<img width="686" alt="image" src="https://github.com/user-attachments/assets/0fef22e5-5372-4e0f-85df-554f3870e427">
They all fail with the error: `NameError: name 'dirty_equals' is not defined`
Just letting you know in case you are not aware. Thanks for making a great package! 😃 | 1medium
|
Title: Form Methods
Body: BaseForm only allows POST, GOTO and GET methods, defaults to POST. Any intention to expand this to include DELETE and PATCH?
https://github.com/pydantic/FastUI/blob/cec25c61a7cc5a716d05d21039f95be3e8dac0e8/src/python-fastui/fastui/components/forms.py#L74 | 1medium
|
Title: 奇奇怪怪的报错
Body: Using model: Tacotron
Using device: cuda
Initialising Tacotron Model...
\Loading the json with %s
{'sample_rate': 16000, 'n_fft': 800, 'num_mels': 80, 'hop_size': 200, 'win_size': 800, 'fmin': 55, 'min_level_db': -100, 'ref_level_db': 20, 'max_abs_value': 4.0, 'preemphasis': 0.97, 'preemphasize': True, 'tts_embed_dims': 512, 'tts_encoder_dims': 256, 'tts_decoder_dims': 128, 'tts_postnet_dims': 512, 'tts_encoder_K': 5, 'tts_lstm_dims': 1024, 'tts_postnet_K': 5, 'tts_num_highways': 4, 'tts_dropout': 0.5, 'tts_cleaner_names': ['basic_cleaners'], 'tts_stop_threshold': -3.4, 'tts_schedule': [[2, 0.001, 10000, 12], [2, 0.0005, 15000, 12], [2, 0.0002, 20000, 12], [2, 0.0001, 30000, 12], [2, 5e-05, 40000, 12], [2, 1e-05, 60000, 12], [2, 5e-06, 160000, 12], [2, 3e-06, 320000, 12], [2, 1e-06, 640000, 12]], 'tts_clip_grad_norm': 1.0, 'tts_eval_interval': 500, 'tts_eval_num_samples': 1, 'tts_finetune_layers': [], 'max_mel_frames': 900, 'rescale': True, 'rescaling_max': 0.9, 'synthesis_batch_size': 16, 'signal_normalization': True, 'power': 1.5, 'griffin_lim_iters': 60, 'fmax': 7600, 'allow_clipping_in_normalization': True, 'clip_mels_length': True, 'use_lws': False, 'symmetric_mels': True, 'trim_silence': True, 'speaker_embedding_size': 256, 'silence_min_duration_split': 0.4, 'utterance_min_duration': 1.6, 'use_gst': True, 'use_ser_for_gst': True}
Trainable Parameters: 0.000M
Loading weights at synthesizer\saved_models\xb\xb.pt
Tacotron weights loaded from step 144
Using inputs from:
C:\Users\hyhhy\Desktop\AIless\vc\SV2TTS\synthesizer\train.txt
C:\Users\hyhhy\Desktop\AIless\vc\SV2TTS\synthesizer\mels
C:\Users\hyhhy\Desktop\AIless\vc\SV2TTS\synthesizer\embeds
Found 284 samples
+----------------+------------+---------------+------------------+
| Steps with r=2 | Batch Size | Learning Rate | Outputs/Step (r) |
+----------------+------------+---------------+------------------+
| 9k Steps | 12 | 0.001 | 2 |
+----------------+------------+---------------+------------------+
Could not load symbol cublasGetSmCountTarget from cublas64_11.dll. Error code 127
Traceback (most recent call last):
File "C:\Users\hyhhy\Desktop\AIless\MockingBird\synthesizer_train.py", line 37, in <module>
train(**vars(args))
File "C:\Users\hyhhy\Desktop\AIless\MockingBird\synthesizer\train.py", line 215, in train
optimizer.step()
File "C:\Users\hyhhy\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\optim\optimizer.py", line 109, in wrapper
return func(*args, **kwargs)
File "C:\Users\hyhhy\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\autograd\grad_mode.py", line 27, in decorate_context
return func(*args, **kwargs)
File "C:\Users\hyhhy\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\optim\adam.py", line 157, in step
adam(params_with_grad,
File "C:\Users\hyhhy\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\optim\adam.py", line 213, in adam
func(params,
File "C:\Users\hyhhy\AppData\Local\Programs\Python\Python39\lib\site-packages\torch\optim\adam.py", line 255, in _single_tensor_adam
assert not step_t.is_cuda, "If capturable=False, state_steps should not be CUDA tensors."
AssertionError: If capturable=False, state_steps should not be CUDA tensors. | 2hard
|
Title: How to do sentence_dedup
Body: ### Search before continuing 先搜索，再继续
- [x] I have searched the Data-Juicer issues and found no similar feature requests. 我已经搜索了 Data-Juicer 的 issue 列表但是没有发现类似的功能需求。
### Description 描述
请问怎么做句子级的去重，有没有推荐的开源实现？谢谢
### Use case 使用场景
_No response_
### Additional 额外信息
_No response_
### Are you willing to submit a PR for this feature? 您是否乐意为此功能提交一个 PR？
- [ ] Yes I'd like to help by submitting a PR! 是的！我愿意提供帮助并提交一个PR！ | 1medium
|
Title: How to add Bearer <JWT> in swagger-ui request
Body: Hello,
I have the same issue like : https://github.com/noirbizarre/flask-restplus/issues/315, but I can't find a solution. I a using the flask_restplus 0.11
I've been searching for a long time but I didn't found how to make query with jwt.
My authorisations looks like :
authorizations = {
'apikey': {
'type' : 'apiKey',
'in': 'header',
'name': 'Authorization'
}
}
the resulting query is :
curl -X GET "http://localhost:5000/api/request/" -H "accept: application/json" -H "Authorization: 12345"
The api answer :
{
"msg": "Bad Authorization header. Expected value 'Bearer <JWT>'"
}
If I add Bearer in 'authorizations like :
authorizations = {
'apikey': {
'type' : 'apiKey',
'in': 'header',
'name': 'Authorization Bearer'
}
}
the query is :
curl -X GET "http://localhost:5000/api/request/" -H "accept: application/json" -H "Authorization Bearer: 12345"
And I want a query like this :
curl -X GET "http://localhost:5000/api/request/" -H "accept: application/json" -H "Authorization: Bearer 12345"
Can somebody give me some help ?
Regards.
James
| 1medium
|
Title: Unable to upload a pdf file of size 2.92MB
Body: ### Describe the bug
`Error
HTTP 413:
413 Request Entity Too Large
nginx/1.18.0`
I am using google colab with to run gradio interface and I am trying to upload a file of size 2.92 MB. I am seeing this error now, never seen this error. I have been using the same code, same gradio interface and same file.
Why am I seeing this error today??
### Have you searched existing issues? 🔎
- [x] I have searched and found no existing issues
### Reproduction
```python
import gradio as gr
#Gradio interface
with gr.Blocks(title=" OCR") as demo:
gr.Markdown("# OCR")
gr.Markdown("Upload a PDF and its ground truth JSON file to convert content to structured JSON with lexicon correction and view performance metrics.")
with gr.Row():
pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"])
gt_json_input = gr.File(label="Upload Ground Truth JSON", file_types=[".json"])
submit_btn = gr.Button("Process")
with gr.Row():
json_output = gr.Textbox(label="Corrected JSON Output", lines=10)
cer_output = gr.Textbox(label="Character Error Rate (CER)", lines=1)
accuracy_output = gr.Textbox(label="Field-Level Accuracy", lines=1)
submit_btn.click(fn=process_pdf, inputs=[pdf_input, gt_json_input], outputs=[json_output, cer_output, accuracy_output])
logging.info("Launching Gradio interface")
demo.launch(share=True)
```
### Screenshot
### Logs
```shell
not getting any errors while debugging
```
### System Info
```shell
Gradio Environment Information:
------------------------------
Operating System: Linux
gradio version: 5.20.0
gradio_client version: 1.7.2
------------------------------------------------
gradio dependencies in your environment:
aiofiles: 23.2.1
anyio: 3.7.1
audioop-lts is not installed.
fastapi: 0.115.11
ffmpy: 0.5.0
gradio-client==1.7.2 is not installed.
groovy: 0.1.2
httpx: 0.28.1
huggingface-hub: 0.28.1
jinja2: 3.1.5
markupsafe: 2.1.5
numpy: 1.26.4
orjson: 3.10.15
packaging: 24.2
pandas: 2.2.2
pillow: 11.1.0
pydantic: 2.10.6
pydub: 0.25.1
python-multipart: 0.0.20
pyyaml: 6.0.2
ruff: 0.9.9
safehttpx: 0.1.6
semantic-version: 2.10.0
starlette: 0.46.0
tomlkit: 0.13.2
typer: 0.15.1
typing-extensions: 4.12.2
urllib3: 2.3.0
uvicorn: 0.34.0
authlib; extra == 'oauth' is not installed.
itsdangerous; extra == 'oauth' is not installed.
gradio_client dependencies in your environment:
fsspec: 2024.10.0
httpx: 0.28.1
huggingface-hub: 0.28.1
packaging: 24.2
typing-extensions: 4.12.2
websockets: 14.2
```
### Severity
Blocking usage of gradio | 1medium
|
Title: [Discussion] How to install to High Sierra 10.13.6?
Body: <!-- Please respect the title [Discussion] tag. -->
Hello.
Please help.
I cannot seem to be able to install spleeter to MacOs High Sierra.
Spleeter worked fine for me in Sierra but I now I cannot get it to work.
I have tried every installation method and advice I've search from google without luck.
I've tried to install with "conda" and "pip" without luck.
I'm now on version 2.1.0 (forcing with "==2.1.0 etc suffix) and this is the beginning of the error message atm :
Traceback (most recent call last):
File "/Users/finalcountdown/opt/anaconda3/lib/python3.7/site-packages/soundfile.py", line 143, in <module>
_snd = _ffi.dlopen(_libname)
OSError: cannot load library '/Users/finalcountdown/opt/anaconda3/bin/../lib/libsndfile.dylib': dlopen(/Users/finalcountdown/opt/anaconda3/bin/../lib/libsndfile.dylib, 2): Library not loaded: @rpath/libvorbis.0.4.9.dylib
Referenced from: /Users/finalcountdown/opt/anaconda3/lib/libsndfile.1.0.31.dylib
Reason: image not found
(I've tried other versions too. spleeter 1.5.3 is what "pip" and "conda" installs, but it apparently needs something from TF that TensorFlow 1.15 doesn't have, but downgrading to 1.14 doesn't help either.
This is obviously a compatibility issue, but I dunno what combination of versions I should use? Any version of spleeter is pretty much ok for me in this situation.
-Petri L.
If this request for help is not in right place can you please inform
me where to put my request(?). | 1medium
|
Title: Search without diacritics
Body: It is possible to implement search without diacritics. For example, to have the same search result when using the word Průhonice as Pruhonice? | 1medium
|
Title: The problem of accuracy of Dnn_regression model using tflearn
Body: Question 1.
I want to check the accuracy of the regression result window, not binary_acc. Because of this, only the binary_acc is displayed in the tensorboard graph, and it is displayed as 0 only.
Question 2.
I would like to apply the loss function to mean absolute percentage error instead of mse. I would like to ask for advice on the code that should be added to objectives.py.
Question 3.
If there is a theoretical error of deep running in my code, I would appreciate your advice.
Here is my code and the results:
# -*- coding: utf-8 -*-
from xlrd import open_workbook
from sklearn.model_selection import train_test_split
import tflearn
import numpy as np
import random
import tensorflow as tf
class EarlyStoppingCallback(tflearn.callbacks.Callback):
def __init__(self, loss_threshold):
self.loss_threshold = loss_threshold
def on_epoch_end(self, training_state):
if training_state.val_loss is None: return
if training_state.val_loss < self.loss_threshold and training_state.val_loss!=0.:
raise StopIteration
# Loss function
def mean_absolute_percentage_error(y_test, estimation_result):
y_test, estimation_result = np.array(y_test), np.array(estimation_result)
return np.mean(np.abs((y_test - estimation_result) / y_test)) * 100
# control variables
needed_col_numbers_for_data = [2,3,4,5,6,7,8,9,10,11]
num_feat = 9
needed_col_numbers_for_label = [11]
starting_row_index = 4 # this variable can be modified according to the data format in given excel file
# read data from excel file
filename = 'concrete_mix_data_6.xlsx'
workbook = open_workbook(filename)
worksheet = workbook.sheet_by_index(0)
nrows = worksheet.nrows
ncols = worksheet.ncols
# data preparation --> data & label
ending_row_index = nrows
data_and_label = []
for row_num in range(starting_row_index-1, ending_row_index):
data_and_label.append([worksheet.row_values(row_num)[i] for i in needed_col_numbers_for_data])
random.shuffle(data_and_label)
data_and_label = np.array(data_and_label)
X = data_and_label[:,:-1]
Y = np.array(data_and_label[:,-1])
Y = np.reshape(Y, (len(Y),1))
# split data into training and testing group
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.25)
# init
tflearn.init_graph(num_cores=4, gpu_memory_fraction=0.5)
print('======================= DNN regression model ==================')
# Build neural network
net = tflearn.input_data(shape=[None, num_feat])
net = tflearn.fully_connected(net, 1000, activation='relu', name='Layer1')
net = tflearn.dropout(net, 0.5, name='Dropout1')
net = tflearn.fully_connected(net, 1000, activation='relu', name='Layer2')
net = tflearn.dropout(net, 0.5, name='Dropout2')
net = tflearn.fully_connected(net, 1000, activation='relu', name='Layer3')
net = tflearn.dropout(net, 0.5, name='Dropout3')
net = tflearn.fully_connected(net, 1000, activation='relu', name='Layer4')
# net = tflearn.dropout(net, 0.5, name='Dropout4')
net = tflearn.fully_connected(net, 1000, activation='relu', name='Layer5')
# net = tflearn.dropout(net, 0.5, name='Dropout5')
net = tflearn.fully_connected(net, 1, activation='linear')
net = tflearn.regression(net, optimizer='Adam', loss='mean_square', metric='accuracy', learning_rate=0.001)
# Define model
model = tflearn.DNN(net, tensorboard_verbose=3, tensorboard_dir="/tmp/tflearn_logs/test")
# Start training (apply gradient descent algorithm)
# print X
# print Y
earlyStoppingCallback = EarlyStoppingCallback(loss_threshold = 60)
model.fit(X, Y, n_epoch=10000, batch_size=60, show_metric=True, snapshot_epoch=True, validation_set=0.25, callbacks=earlyStoppingCallback, run_id="tensorboard_test")
# Predictive Data('best')
estimation_result = model.predict(x_test)
# Results
result = np.concatenate((estimation_result,y_test),axis=1)
print('======================= DNN regression Results ==================')
print('Estimation Result / Target ')
print(result)
print('==================================================================')
mape = mean_absolute_percentage_error(y_test, estimation_result)
print('Mean absolute percentage error :',mape)
# mse = mean_squared_error(y_test, estimation_result)
# print(mse)
# # Save a model
model.save('tensorboard_test2.tflearn')
Training Step: 7377 | total loss: 133.95444 | time: 0.787s
| Adam | epoch: 1054 | loss: 133.95444 - binary_acc: 0.0000 -- iter: 360/390
Training Step: 7378 | total loss: 132.92976 | time: 1.935s
| Adam | epoch: 1054 | loss: 132.92976 - binary_acc: 0.0000 | val_loss: 76.89469 - val_acc: 0.0000 -- iter: 390/390 | 2hard
|
Title: TypeError: 'int' object is not subscriptable
Body: I try to do inference on your pretrained model following: https://github.com/deepinsight/insightface/blob/master/examples/demo_analysis.py
Here's my code:
```
import insightface
from cv2 import cv2
app = insightface.app.FaceAnalysis()
app.prepare(0, det_size=640)
image = cv2.imread("my-picture.jpg")
faces = app.get(image)
assert len(faces)==6
rimg = app.draw_on(image, faces)
cv2.imwrite("output.jpg", rimg)
```
Error:
```
Traceback (most recent call last):
File "E:\Work work\Python\Work\atin\FaceRecog\main.py", line 7, in <module>
faces = app.get(image)
File "E:\Anaconda\envs\insightface\lib\site-packages\insightface\app\face_analysis.py", line 59, in get
bboxes, kpss = self.det_model.detect(img,
File "E:\Anaconda\envs\insightface\lib\site-packages\insightface\model_zoo\retinaface.py", line 212, in detect
model_ratio = float(input_size[1]) / input_size[0]
TypeError: 'int' object is not subscriptable
find model: .insightface\models\buffalo_l\w600k_r50.onnx recognition ['None', 3, 112, 112] 127.5 127.5
set det-size: 640
```
| 1medium
|
Title: NNI install in development mode on macOS M1
Body: I install nni from soruce code in development mode on apple M1 by following instructions from https://nni.readthedocs.io/en/stable/notes/build_from_source.html
After install, I can import nni successfully.
However, when I try some simple NAS tutorial in here https://nni.readthedocs.io/en/stable/tutorials/hello_nas.html
I fail to import sublibrary retiarii
from nni.retiarii import model_wrapper
How should I solve the missing extra dependencies if I install from source code for M1?
thank you for your help!
| 1medium
|
Title: UniqueViolationError within a transaction (using get_or_create)
Body: I noticed that when multiple concurrent executions of my `add_list` coroutine overlap, occasionally I get `UniqueViolationError`. I initially thought this was protected against by using a transaction. But it seems that the transaction does not block other coroutines from starting another transaction (on same table) as well, is that correct?
For illustration, the `add_list` function looks something like the following, and is started multiple times (concurrently), with almost no break in between.
```
async def add_main(i):
m = await Main.objects().get_or_create(Main.key == f"Main-{i}")
a = await EntityA.objects().get_or_create(EntityA.key == f"EntityA-{i}")
m.foreign_1 = a
await m.save()
async def add_list():
async with Main._meta.db.transaction():
for i in range(100):
await add_main(i)
```
I don't get the `UniqueViolationError` on the "Main" table, but on a related one (e.g. EntityA), and while doing `get_or_create`. I think I will add an `with asyncio.Lock` before the transaction - Or are there better ways to prevent the `UniqueViolationError` from occurring? | 1medium
|
Title: Evaluation metrics
Body: Hi,
Great job!
For a fair comparison, is it possible to share the evaluation metrics code in the paper?
Thanks | 0easy
|
Title: Add file_name inside failed.json and success.json
Body: Adding the file name inside `.json` files can help to create a track of generated resume for each job application
| 0easy
|
Title: Server Error 500 after Bare Metal Update DB upgrade 1.4.2 -> 1.5.0
Body: I had a bare metal install of 1.4.2 running on buster.
Now after upgrade to bullseye I installed 1.5.0 and just copied the paperless.conf file.
Most of the stuff runs fine, but: While trying to access E-Mail accounts , E-Mail rules and planned tasks from the admin menu I get a Server Error 500. My guess is, that this could be due to some db structure incompatibilities, but tbh I do not have a glue.
I do not see anything in any log?
What could I do?
What is a clean upgrade path for bare metal? | 1medium
|
Title: 是否可以在批量下载视频的时候将获取到的链接保存到本地txt文件
Body: 获取到用户视频链接后，将这些链接和视频大小保存一份到本地txt | 0easy
|
Title: [BUG] Bad compatibility check by testing the existence of a CHANGELOG.md file which is not always available depending on the way of CUTLASS library installation
Body: In this code, if we have nvidia-cutlass package installed with pip, there will be no CHANGELOG.md file in the installed site-package directory, and hence the test result will be false negative.
https://github.com/microsoft/DeepSpeed/blob/4d4ff0edddb28a4d53d40dffad9ba16e59b83aec/op_builder/evoformer_attn.py#L55C41-L55C53
Please fix this by checking other files that are always available or maybe the best way to check is to see if the following python code return a valid result without throwing an exception:
```python
import os, cutlass
cutlass_path = os.path.dirname(cutlass.__file__)
cutlass_version = cutlass.__version__
```
| 1medium
|
Title: Comfy X Braile
Body: ### Feature Idea
[Cheesecake Johnson-]
Original post https://civitai.com/articles/11100
https://www.youtube.com/watch?v=_p1vo9uwu18
https://www.youtube.com/watch?v=0fIg4rI4cDw&t=15s
I thought of something uncommon!
As Comfy supports TTS, it could also support Braille! Would be so cool... not for me, but for people who are actually visually impaired.
I bet a lot of people would be happy if comfy implemented this and other visually-impaired tools.
Imagine a visually impaired person producing images of what he thinks the world looks like simply by using the interface of comfy, with a different CSS so it displays properly on their braille tablet.
I always been fascinated with adaptive technologies even thought I'm healthy and in a whole single piece.
http://braillespecs.github.io/braille-css/20210129/
The braille tablets that are developed in universities are supposed to be capable of drawing full interfaces and probably also have push buttons and drag and drop but I seen more modern devices for the broader public which have visual light screen behind too for non-blind people to see... This is a domain in perpetual development.
As AIs are mostly developed in renowned universities, I'm persuaded that developers will find colleague universitarian individuals who are impassioned by assistive technologies in their close relatives
While this is niche, this is also essential and while there mightn't be masses of budget in it, it's an important thing do do than to make computers have wide accessibility features, more than just a chore.
It's like the fact of teaching sign language... As there's not tons of money into that, hearing impaired or mute people are at risk of lacking competent teachers to teach them sign language. Sometimes things have to be done wherever the money's at. It's some kind of a chore and it's okay like that.
### Existing Solutions
_No response_
### Other
I propose developers would contact various professionals in the domain of sign language, braille and assistive technologies related institutions and organisms in Asia, America and Europe as the standards varies.
After, they can communicate with people in the blind programmer community which could be of a serious helping hand in order to implement Braille CSS and other TTS assistant cues in the HTML.
Blind programmer community is an actual real thing.
Personally, I won't benefit, but might enjoy seeing the result nonetheless. It's selfless.
seeing the world through the eyes of a visually impaired artist would be a huge move forward for their emancipation. As they don't know colours and are more about shapes, it would be very unique.
With this device, visually impaired could visually see the art they generate using marigold depth in comfy
https://www.hackster.io/news/stanford-university-unveils-pin-art-like-tactile-3d-display-for-visually-impaired-3d-modelers-fdadaf780deb
Visually impaired people making art would be so great!
Then people could describe what they see and it would make them create social bounds!
[-Cheesecake Johnson]
| 1medium
|
Title: [BUG]
Body: **Describe the bug**
ValueError: ALLOWED_GUILDS is not defined properly in the environment file!Please copy your server's guild ID and put it into ALLOWED_GUILDS in the .env file.For example a line should look like: `ALLOWED_GUILDS="971268468148166697"`
I have a 19 digit ID | 0easy
|
Title: Unclear usage of the PNA model
Body: ### 📚 Describe the documentation issue
https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.nn.models.PNA.html
It's listed here that the **non-optional** arguments are:
**in_channels, hidden_channels, num_layers, out_channels**
which is consistent with the other models (GIN-GCN) that create the layers under the hood.
However, PNA requires the presence of 'aggregators', 'scaler', 'degree' making them **non-optional** arguments.
```
**kwargs (optional): Additional arguments of
:class:`torch_geometric.nn.conv.PNAConv`.
```
### Suggest a potential alternative/fix
A simple fix would be:
```
def init_conv(self, in_channels: int, out_channels: int,
**kwargs) -> MessagePassing:
## For the deg argument ##
The init must recieve the deg argument
## Solution-1 for the aggregators/scalers ##
Defined here:
aggregators = ['mean', 'min', 'max', 'std']
scalers = ['identity', 'amplification', 'attenuation']
return PNAConv(in_channels, out_channels, aggregators = aggregators , scalers = scalers )
```
Alternatively, default the constructor of PNAConv to use these lists if nothing is defined.
| 1medium
|
Title: Center Align Table
Body: How do I center align the header and the rows within the table ? I have tried many things but nothing seems to be working. | 1medium
|
Title: List comprehension failing with an empty list
Body: The following very simple list comprehension works fine in the interactive Python interpreter, but fails if used in PyScript:
`[x for x in []]`
I would expect it to return an empty list (`[]`), as it does in the interactive interpreter:
```
>>> [x for x in []]
[]
```
but PyScript gives an error:
```
[x for x in []]
^
KeyError: 'x'
```
It's not a very interesting example, but I've had situations where an incoming iterable may be empty and it's easier to just execute the list comprehension without having to add an `if xxx` to the end to filter empty lists. I've worked around it with a `for` loop, but I didn't see a limitation explicitly called out in the docs and wanted to file an issue for it.
Weirdly, I have seen this construct work as expected sometimes, but I can get to to reliably fail if I write an app like this:
```
@time_trigger('startup')
def startup():
[x for x in []]
``` | 1medium
|
Title: SLZB-06M integration - connect through VPN
Body: ### The problem
I have two SLZB-06M: one connected in network directly, other - throught VPN
I try to use component to connect to SLZB-06M witch connected throught VPN. First stage was good - devace was added, but second stage (initializing with counter and etc) was failed.
When search in log i saw that component try to connect to internal IP, not vpn ip
### What version of Home Assistant Core has the issue?
core-2025.3.1
### What was the last working version of Home Assistant Core?
_No response_
### What type of installation are you running?
Home Assistant OS
### Integration causing the issue
_No response_
### Link to integration documentation on our website
_No response_
### Diagnostics information
_No response_
### Example YAML snippet
```yaml
```
### Anything in the logs that might be useful for us?
```txt
```
### Additional information
_No response_ | 1medium
|
Title: Using multi_joined with response object in object
Body: @igorbenav I have more a question about get multi_joined. When i joined 2 table many to one. I can't not create response object in object. Example: I only create like that
```json
"data": [
{
"id": 0,
"title": "string",
"template_id": 0,
"description": "string",
"created_at": "2024-04-14T16:28:01.403Z",
"created_by": "string",
"template_title": "Example text for encryption",
"template_description": "Example description for encryption template"
}
]
```
This is response that i want
```json
"data": [
{
"id": 0,
"title": "string",
"template_id": 0,
"description": "string",
"created_at": "2024-04-14T16:28:01.403Z",
"created_by": "string",
"created_at": "2024-04-14T16:28:01.403Z",
"created_by": "string",
"template":{
"title": "Example text for encryption",
"description": "Example description for encryption template"
}
}
]
```
Please help me :( | 1medium
|
Title: [DetectionDataset] - expand `from_yolo` to include support for OBB (Oriented Bounding Boxes)
Body: ### Description
In [supervision-0.18.0](https://github.com/roboflow/supervision/releases/tag/0.18.0), we added initial support for OBB; it's time to extend it to include dataset loading.
Make the necessary changes in [sv.DetectionDataset.from_yolo](https://supervision.roboflow.com/0.19.0/datasets/#supervision.dataset.core.DetectionDataset.from_yolo) to enable loading OBB datasets from disk in YOLO format. [Here](https://docs.ultralytics.com/datasets/obb/) you can read more about the YOLO OBB Format. In short, each line of the `.txt` file should have the following format.
```
class_index, x1, y1, x2, y2, x3, y3, x4, y4
```
The [sv.OrientedBoxAnnotator](https://supervision.roboflow.com/0.19.0/annotators/#supervision.annotators.core.OrientedBoxAnnotator.annotate) expects information about oriented bounding boxes to be stored in the `xyxyxyxy` field of [`sv.Detections.data`](https://github.com/roboflow/supervision/blob/4729e20a9408fbe08d342cc4dbae835d808686a5/supervision/detection/core.py#L88). Ensure that the information loaded from the dataset is stored there.
### API
Here's an example of how to use the new API. Roboflow allows for the export of segmentation datasets as OBB. Let's ensure that our support for OBB definitely works with datasets exported from Roboflow.
```python
import random
import roboflow
from roboflow import Roboflow
import supervision as sv
roboflow.login()
rf = Roboflow()
project = rf.workspace("roboflow-jvuqo").project("fashion-assistant")
version = project.version(3)
dataset = version.download("yolov8-obb")
train_ds = sv.DetectionDataset.from_yolo(
images_directory_path=f"{dataset.location}/train/images",
annotations_directory_path=f"{dataset.location}/train/labels",
data_yaml_path=f"{dataset.location}/data.yaml"
)
image_name = random.choice(list(train_ds.images))
image = train_data.images[image_name]
detections = train_data.annotations[image_name]
oriented_box_annotator = sv.OrientedBoxAnnotator()
annotated_frame = oriented_box_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
### Additional
- Note: Please share a Google Colab with minimal code to test the new feature. We know it's additional work, but it will speed up the review process. The reviewer must test each change. Setting up a local environment to do this is time-consuming. Please ensure that Google Colab can be accessed without any issues (make it public). Thank you! 🙏🏻 | 1medium
|
Title: cancel_order not working
Body: The cancel_order call in the REST api that calls Alpaca seem to execute fine but the order is still open. I tested this outside regular trading hours. Not sure if it works during normal trading hours. Regardless, it should throw an exception if the order was not cancelled. | 1medium
|
Title: python: error while loading shared libraries: libpython3.6m.so.1.0: cannot open shared object file: No such file or directory
Body: Tried to do a quick run of nbviewer but got this error.
Can't find any solution after some preliminary research.
My local python version is 3.6.5
Thank you! | 1medium
|
Title: 如何开启多卡训练？
Body: 【v1.2.2版本: 支持多卡训练】
我运行[examples/training_sup_text_matching_model.py]文件，发现是单卡训练，请问如何开启多卡训练？ | 1medium
|
Title: Kucoin - Unauthenticated subscription disconnects after 24 hours, fails to refresh token, and then cannot reconnect
Body: **Describe the bug**
Using an unauthenticated subscription to Kucoin's trade feed for a period of time (about 24 hours), Kucoin disconnects the websocket and then cryptofeed is unable to reconnect due to cryptofeed not refreshing the connection token.
**To Reproduce**
Steps to reproduce the behavior:
* Unauthenticated KuCoin feed on TRADE channel.
* Allow to run for 24+ hours.
```
2024-04-05 10:47:36,818 : WARNING : KUCOIN: error from exchange {'id': '660fd6c84d0eab582c237b51', 'type': 'error', 'code': 401, 'data': 'token is expired'}
2024-04-05 10:47:36,819 : WARNING : KUCOIN.ws.3: encountered connection issue no close frame received or sent - reconnecting in 1.0 seconds...
```
**Expected behavior**
Cryptofeed should renew the token, reconnect, and continue the channel subscriptions.
**Operating System:**
- Ubuntu 22.04
**Cryptofeed Version**
- cryptofeed 2.4.0 installed via pip / pypi
I see that the Binance exchange code has functions to refresh the auth token, I'll take a look and see if I can get something similar to work with KuCoin and submit a PR if successful.
| 1medium
|
Title: How do ON DUPLICATE KEY UPDATE?
Body: Table have a unique constrain on 2 fields.
When i try to `await MyModel.create(**dict_of_data)` with exist entry in unique fields, i got error:
`tortoise.exceptions.IntegrityError: (1062, "Duplicate entry...`
Try `await MyModel(**dict_of_data).save(force_update=True)` raise error:
`tortoise.exceptions.IntegrityError: Can't update object that doesn't exist. PK: None`
How can i do `ON DUPLICATE KEY UPDATE` for create new or update exist record without many requests and conditional structures? | 1medium
|
Title: Add docs about Configuration provider
Body: | 0easy
|
Title: sv changes bbox shape for object detection with YOLOv8??
Body: ### Search before asking
- [X] I have searched the Supervision [issues](https://github.com/roboflow/supervision/issues) and found no similar feature requests.
### Question
I've been using supervision, its tracker, annotators, ... Nice work!! However I've noticed that, doing object detection with yolov8, bboxe shape from ultralytics are changed by supervision even though it refers to the same detection. The following screenshot shows a detected object provided by YOLO, ultralytics.Result (before doing `supervision_tracker.update(results[0])` and after parsing it to `supervision_tracker`.
The bboxes are diferent. I expect they shouldn't...
Can this bbox shape change be removed? I would like to keep original bbox shape.
Thanks!!
### Additional
_No response_ | 1medium
|
Title: pull: "No file hash info found" and checkout fails when pulling frozen imported url that is a directory that has subsequently changed
Body: # Bug Report
## Description
I have used `dvc import-url` to track a GCS directory that is updated daily with incremental new data. [The documentation](https://dvc.org/doc/user-guide/data-management/importing-external-data#how-external-outputs-work) suggests that once the data is imported, it is frozen and DVC should not attempt to download changes from the tracked URL unless `dvc update` is run.
It also says that
> During [dvc push](https://dvc.org/doc/command-reference/push), DVC will upload the version of the data tracked by [the .dvc file] to the [DVC remote](https://dvc.org/doc/user-guide/data-management/remote-storage) so that it is backed up in case you need to recover it.
Instead, when I `dvc push`, the log messages suggest the files were uploaded, but nothing shows up in the remote. y imported data to remote, `dvc pull` from a fresh checkout downloads all the files from the remote url (including updated ones), and fails to check out the frozen version of the data as expected.
### Reproduce
I created a minimal example using a public GCS bucket in my account:
1. mkdir dvc-import-issue & cd dvc-import-issue
2. git init
3. dvc init
4. dvc import-url gs://hasha-ds-public/test_dir/ test_dir
5. dvc remote add origin gs://hasha-ds-public/test_remote/
6. dvc push -r origin
Output from `dvc push` is
```
Collecting |5.00 [00:00, 441entry/s]
Pushing |0.00 [00:01, ?file/s]
Collecting hasha-ds-public/test_remote/ on gs |5.00 [00:01, 3.29entry/s]
4 files pushed
```
However I later noticed that no files had been pushed to the remote at all despite the misleading log message. I was able to correct this by running `dvc import-url gs://hasha-ds-public/test_dir --to-remote`, but this feels like another bug.
8. git add .dvc/config test_dir.dvc .gitignore
9. git commit -m "Committed and pushed"
Now set up a fresh clone of the project
10. cd ..
11. git clone dvc-import-url-pull-issue dvc-import-url-pull-issue2
Add another file to the tracked URL
12. echo "more stuff" > blah.txt & gsutil cp blah.txt gs://hasha-ds-public/test_dir/
Finally, try pulling the frozen data associated with the imported URL from remote into the fresh clone
13. cd dvc-import-issue2
14. dvc pull -r origin
### Result
```
Collecting |5.00 [00:00, 11.3entry/s]
Fetching
Building workspace index |0.00 [00:00, ?entry/s]
Comparing indexes |6.00 [00:00, 14.9entry/s]
WARNING: No file hash info found for '/Users/alexhasha/repos/dvc-import-issue2/test_dir/baz.txt'. It won't be created.
WARNING: No file hash info found for '/Users/alexhasha/repos/dvc-import-issue2/test_dir/blah.txt'. It won't be created.
WARNING: No file hash info found for '/Users/alexhasha/repos/dvc-import-issue2/test_dir/bar.txt'. It won't be created.
WARNING: No file hash info found for '/Users/alexhasha/repos/dvc-import-issue2/test_dir/foo.txt'. It won't be created.
Applying changes |0.00 [00:00, ?file/s]
4 files fetched
ERROR: failed to pull data from the cloud - Checkout failed for following targets:
test_dir
Is your cache up to date?
<https://error.dvc.org/missing-files>
```
Note that it's trying to checkout `blah.txt` in the error message, though that file wasn't present when the directory was imported and test_dir.dvc suggests there should only be 3 files in the directory.*-
I have pushed this repository to https://github.com/ahasha/dvc-import-url-pull-issue . You should be able to reproduce the error message by cloning it and running `dvc pull -r origin`.
### Expected
1. After running `dvc import-url`, `dvc push` should upload the version of the data tracked by the created .dvc file to the remote
2. In a fresh clone of the repository, `dvc pull` should retrieve the version of the data tracked by the .dvc file from the remote, and not download anything new from the tracked URL.
### Environment information
**Output of `dvc doctor`:**
```console
DVC version: 3.36.0 (pip)
-------------------------
Platform: Python 3.11.4 on macOS-14.1.2-x86_64-i386-64bit
Subprojects:
dvc_data = 3.2.0
dvc_objects = 3.0.0
dvc_render = 1.0.0
dvc_task = 0.3.0
scmrepo = 2.0.2
Supports:
gs (gcsfs = 2023.12.2.post1),
http (aiohttp = 3.9.1, aiohttp-retry = 2.8.3),
https (aiohttp = 3.9.1, aiohttp-retry = 2.8.3)
Config:
Global: /Users/alexhasha/Library/Application Support/dvc
System: /Library/Application Support/dvc
Cache types: reflink, hardlink, symlink
Cache directory: apfs on /dev/disk1s3s1
Caches: local
Remotes: gs
Workspace directory: apfs on /dev/disk1s3s1
Repo: dvc, git
Repo.site_cache_dir: /Library/Caches/dvc/repo/a48422a1ed3a65e3efe064e5b18830a7
```
| 1medium
|
Title: New release?
Body: It seems there has not been a release since 0.22.0 in Feb 2021, but there have been several fixes.
Can you release a new version? | 0easy
|
Title: [Bug]: Alias recognition issue in stable-diffusion-webui
Body: ### Checklist
- [X] The issue exists after disabling all extensions
- [x] The issue exists on a clean installation of webui
- [ ] The issue is caused by an extension, but I believe it is caused by a bug in the webui
- [X] The issue exists in the current version of the webui
- [X] The issue has not been reported before recently
- [ ] The issue has been reported before but has not been fixed yet
### What happened?
**Description:**
The instructions on the [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Change-model-folder-location) indicate that models can be stored externally and aliases can be created for them. This used to work correctly but no longer does. Despite completely deleting files and reinstalling, the issue persists. The system fails to recognize aliases properly and treats them as incorrect files.
### Steps to reproduce the problem
**Steps to Reproduce:**
Follow the instructions on the wiki to store models externally and create aliases.
Attempt to use the alias in the stable-diffusion-webui.
Observe the errors in the terminal.
**Expected Behavior:**
The system should correctly recognize the alias and load the model without errors.
**Actual Behavior:**
The system fails to recognize the alias and produces the following errors:
```
Running on local URL: http://0.0.0.0:8777
To create a public link, set `share=True` in `launch()`.
Startup time: 0.4s (load scripts: 0.1s, create ui: 0.1s, gradio launch: 0.1s).
Checkpoint 06Cuteyukimixadorable_v10.safetensors [f944617859] not found; loading fallback 06Cuteyukimixadorable_v10.safetensors
Checkpoint 06Cuteyukimixadorable_v10.safetensors [f944617859] not found; loading fallback 06Cuteyukimixadorable_v10.safetensors
0%| | 0/20 [00:00<?, ?it/s]
*** Error completing request
*** Arguments: ('task(shidvi47vvgc19f)', <gradio.routes.Request object at 0x3234199c0>, 'one girl', '', [], 1, 1, 7, 512, 512, False, 0.7, 2, 'Latent', 0, 0, 0, 'Use same checkpoint', 'Use same sampler', 'Use same scheduler', '', '', [], 0, 20, 'DPM++ 2M', 'Automatic', False, '', 0.8, -1, False, -1, 0, 0, 0, False, False, 'positive', 'comma', 0, False, False, 'start', '', 1, '', [], 0, '', [], 0, '', [], True, False, False, False, False, False, False, 0, False) {}
Traceback (most recent call last):
File "/Users/user/stable-diffusion-webui/modules/call_queue.py", line 74, in f
res = list(func(*args, **kwargs))
File "/Users/user/stable-diffusion-webui/modules/call_queue.py", line 53, in f
res = func(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/call_queue.py", line 37, in f
res = func(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/txt2img.py", line 109, in txt2img
processed = processing.process_images(p)
File "/Users/user/stable-diffusion-webui/modules/processing.py", line 847, in process_images
res = process_images_inner(p)
File "/Users/user/stable-diffusion-webui/modules/processing.py", line 988, in process_images_inner
samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
File "/Users/user/stable-diffusion-webui/modules/processing.py", line 1346, in sample
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
File "/Users/user/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py", line 230, in sample
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
File "/Users/user/stable-diffusion-webui/modules/sd_samplers_common.py", line 272, in launch_sampling
return func()
File "/Users/user/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py", line 230, in <lambda>
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/k-diffusion/k_diffusion/sampling.py", line 594, in sample_dpmpp_2m
denoised = model(x, sigmas[i] * s_in, **extra_args)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_samplers_cfg_denoiser.py", line 249, in forward
x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/k-diffusion/k_diffusion/external.py", line 112, in forward
eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/k-diffusion/k_diffusion/external.py", line 138, in get_eps
return self.inner_model.apply_model(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_utils.py", line 22, in <lambda>
setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_utils.py", line 34, in __call__
return self.__sub_func(self.__orig_func, *args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_unet.py", line 50, in apply_model
result = orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_utils.py", line 22, in <lambda>
setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_utils.py", line 34, in __call__
return self.__sub_func(self.__orig_func, *args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_unet.py", line 50, in apply_model
result = orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/ddpm.py", line 858, in apply_model
x_recon = self.model(x_noisy, t, **cond)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/ddpm.py", line 1335, in forward
out = self.diffusion_model(x, t, context=cc)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_unet.py", line 91, in UNetModel_forward
return original_forward(self, x, timesteps, context, *args, **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/openaimodel.py", line 789, in forward
emb = self.time_embed(t_emb)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/container.py", line 217, in forward
input = module(input)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/devices.py", line 169, in forward_wrapper
result = self.org_forward(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/extensions-builtin/Lora/networks.py", line 584, in network_Linear_forward
return originals.Linear_forward(self, input)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/linear.py", line 116, in forward
return F.linear(input, self.weight, self.bias)
RuntimeError: Placeholder storage has not been allocated on MPS device!
```
### What should have happened?
The system should correctly recognize the alias and load the model without errors.
### What browsers do you use to access the UI ?
Apple Safari
### Sysinfo
[sysinfo-2024-07-28-18-46.json](https://github.com/user-attachments/files/16404890/sysinfo-2024-07-28-18-46.json)
### Console logs
```Shell
on local URL: http://0.0.0.0:8777
To create a public link, set `share=True` in `launch()`.
Startup time: 0.4s (load scripts: 0.1s, create ui: 0.1s, gradio launch: 0.1s).
Checkpoint 06Cuteyukimixadorable_v10.safetensors [f944617859] not found; loading fallback 06Cuteyukimixadorable_v10.safetensors
Checkpoint 06Cuteyukimixadorable_v10.safetensors [f944617859] not found; loading fallback 06Cuteyukimixadorable_v10.safetensors
0%| | 0/20 [00:00<?, ?it/s]
*** Error completing request
*** Arguments: ('task(shidvi47vvgc19f)', <gradio.routes.Request object at 0x3234199c0>, 'one girl', '', [], 1, 1, 7, 512, 512, False, 0.7, 2, 'Latent', 0, 0, 0, 'Use same checkpoint', 'Use same sampler', 'Use same scheduler', '', '', [], 0, 20, 'DPM++ 2M', 'Automatic', False, '', 0.8, -1, False, -1, 0, 0, 0, False, False, 'positive', 'comma', 0, False, False, 'start', '', 1, '', [], 0, '', [], 0, '', [], True, False, False, False, False, False, False, 0, False) {}
Traceback (most recent call last):
File "/Users/user/stable-diffusion-webui/modules/call_queue.py", line 74, in f
res = list(func(*args, **kwargs))
File "/Users/user/stable-diffusion-webui/modules/call_queue.py", line 53, in f
res = func(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/call_queue.py", line 37, in f
res = func(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/txt2img.py", line 109, in txt2img
processed = processing.process_images(p)
File "/Users/user/stable-diffusion-webui/modules/processing.py", line 847, in process_images
res = process_images_inner(p)
File "/Users/user/stable-diffusion-webui/modules/processing.py", line 988, in process_images_inner
samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
File "/Users/user/stable-diffusion-webui/modules/processing.py", line 1346, in sample
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
File "/Users/user/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py", line 230, in sample
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
File "/Users/user/stable-diffusion-webui/modules/sd_samplers_common.py", line 272, in launch_sampling
return func()
File "/Users/user/stable-diffusion-webui/modules/sd_samplers_kdiffusion.py", line 230, in <lambda>
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
return func(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/k-diffusion/k_diffusion/sampling.py", line 594, in sample_dpmpp_2m
denoised = model(x, sigmas[i] * s_in, **extra_args)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_samplers_cfg_denoiser.py", line 249, in forward
x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/k-diffusion/k_diffusion/external.py", line 112, in forward
eps = self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/k-diffusion/k_diffusion/external.py", line 138, in get_eps
return self.inner_model.apply_model(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_utils.py", line 22, in <lambda>
setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_utils.py", line 34, in __call__
return self.__sub_func(self.__orig_func, *args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_unet.py", line 50, in apply_model
result = orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_utils.py", line 22, in <lambda>
setattr(resolved_obj, func_path[-1], lambda *args, **kwargs: self(*args, **kwargs))
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_utils.py", line 34, in __call__
return self.__sub_func(self.__orig_func, *args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_hijack_unet.py", line 50, in apply_model
result = orig_func(self, x_noisy.to(devices.dtype_unet), t.to(devices.dtype_unet), cond, **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/ddpm.py", line 858, in apply_model
x_recon = self.model(x_noisy, t, **cond)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/stable-diffusion-stability-ai/ldm/models/diffusion/ddpm.py", line 1335, in forward
out = self.diffusion_model(x, t, context=cc)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/sd_unet.py", line 91, in UNetModel_forward
return original_forward(self, x, timesteps, context, *args, **kwargs)
File "/Users/user/stable-diffusion-webui/repositories/stable-diffusion-stability-ai/ldm/modules/diffusionmodules/openaimodel.py", line 789, in forward
emb = self.time_embed(t_emb)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1541, in _call_impl
return forward_call(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/container.py", line 217, in forward
input = module(input)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/modules/devices.py", line 169, in forward_wrapper
result = self.org_forward(*args, **kwargs)
File "/Users/user/stable-diffusion-webui/extensions-builtin/Lora/networks.py", line 584, in network_Linear_forward
return originals.Linear_forward(self, input)
File "/Users/user/stable-diffusion-webui/venv/lib/python3.10/site-packages/torch/nn/modules/linear.py", line 116, in forward
return F.linear(input, self.weight, self.bias)
RuntimeError: Placeholder storage has not been allocated on MPS device!
```
### Additional information
_No response_ | 1medium
|
Title: Navigation Button with tooltip error when clicking it
Body: **Describe your context**
Please provide us your environment, so we can easily reproduce the issue.
```
dash 2.7.1
dash-auth 1.4.1
dash-bootstrap-components 1.3.0
dash-core-components 2.0.0
dash-daq 0.5.0
dash-extensions 0.1.8
dash-html-components 2.0.0
dash-table 5.0.0
python-dash 0.0.1
```
- if frontend related, tell us your Browser, Version and OS
- OS: Mac OS 12.3.1
- Safari, Chrome
**Describe the bug**
MWE:
```
import dash
from dash import dcc
from dash import html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output
app = dash.Dash(__name__, suppress_callback_exceptions=True)
app.layout = html.Div([
dcc.Location(id='url', refresh=False),
html.Div(id='page-content'),
])
def get_overview_page():
page = html.Div([
dcc.Link(
html.Button('Neu', id='new_button',
style={'margin-left': '10px', 'width': '100px', 'height': '27px',
'fontSize': '16px'}),
href='/new-entry'
),
dbc.Tooltip(
"A.",
target="new_button", placement="top"
)
], style={'width': '30%', 'margin-top': '10px', 'display': 'inline-block', 'text-align': 'left'})
return page
# Update the index
@app.callback(Output('page-content', 'children'),
[Input('url', 'pathname')])
def display_page(pathname):
if pathname == '/new-entry':
return html.Div()
else:
return get_overview_page()
if __name__ == '__main__':
#app.run_server(debug=True, port=8080, host='0.0.0.0')
app.run_server(debug=True, port=8086, host='127.0.0.1')
```
When you press the button and move the mouse you receive:
`An object was provided as children instead of a component, string, or number (or list of those). Check the children property that looks something like: { "1": { "props": { "is_open": false } } }`
When you remove the tooltip. It is working, so it has to do something with it.
**Expected behavior**
No error.
| 1medium
|
Title: MultiSelectField returns null during query
Body: Hi
I am using the django-multiselectfield package and when I am making a query it always returns null. Am I missing some configuration because the other fields seems to working fine.
My enum for the Multiselect field
```
DAYS = (
('0', 'Sunday'),
('1', 'Monday'),
('2', 'Tuesday'),
('3', 'Wednesday'),
('4', 'Thursday'),
('5', 'Friday'),
('6', 'Saturday'))
```
My model
```
from multiselectfield import MultiSelectField
class Queue(models.Model):
days = MultiSelectField(choices=DAYS)
```
My schema
```
class QueueListType(DjangoListObjectType):
class Meta:
model = Queue
pagination = LimitOffsetGraphqlPagination(default_limit=25)
class Query(object):
all_queues = DjangoListObjectField(QueueListType)
```
When I am doing the query
```
query {
allQueues{
totalCount
results{
id
days
}
}
}
```
The result is
```
{
"data": {
"allQueues": {
"totalCount": 2,
"results": [
{
"id": "1",
"days": null
},
{
"id": "2",
"days": null
}
]
}
}
}
``` | 1medium
|
Title: Examples are written for Python 2
Body: At the site: http://www.dnspython.org/examples.html
The examples seem to be written for Python 2. They use print statements rather than print() commands. | 0easy
|
Title: [BUG] Spurious warning about `gluonts` data containers when none is used
Body:
**Describe the bug**
<!--
A clear and concise description of what the bug is.
-->
Getting warning to install 'orjson', 'ujson' while running a script that uses sktime and gluonts.
**To Reproduce**
<!--
Add a Minimal, Complete, and Verifiable example (for more details, see e.g. https://stackoverflow.com/help/mcve
If the code is too long, feel free to put it in a public gist and link it in the issue: https://gist.github.com
-->
```python
from sktime.clustering.k_means import TimeSeriesKMeansTslearn
from sklearn.datasets import make_blobs
X_train, _ = make_blobs(n_samples=100, centers=3, n_features=10, random_state=42)
model = TimeSeriesKMeansTslearn(n_clusters=3, metric="euclidean")
model.fit(X_train)
y_pred = model.predict(X_train)
print(y_pred)
```
**Expected behavior**
<!--
A clear and concise description of what you expected to happen.
-->
No warning message should be displayed, and the script should run without suggesting additional installations.
**Additional context**
<!--
Add any other context about the problem here.
-->
It says "/sktime/env/lib/python3.12/site-packages/gluonts/json.py:102: UserWarning: Using `json`-module for json-handling. Consider installing one of `orjson`, `ujson` to speed up serialization and deserialization.
warnings.warn(
"
**Versions**
<details>
<!--
Please run the following code snippet and paste the output here:
from sktime import show_versions; show_versions()
-->
System:
python: 3.12.3 (main, Jan 17 2025, 18:03:48) [GCC 13.3.0]
executable: /home/kartik/Desktop/sktime/env/bin/python
machine: Linux-6.8.0-52-generic-x86_64-with-glibc2.39
Python dependencies:
pip: 25.0
sktime: 0.35.0
sklearn: 1.5.2
skbase: 0.12.0
numpy: 1.26.4
scipy: 1.15.1
pandas: 2.2.3
matplotlib: 3.10.0
joblib: 1.4.2
numba: 0.60.0
statsmodels: 0.14.4
pmdarima: 2.0.4
statsforecast: 2.0.0
tsfresh: None
tslearn: 0.6.3
torch: None
tensorflow: 2.18.0
</details>
<!-- Thanks for contributing! -->
| 1medium
|
Title: [BUG] `Patch` does not seem to work correctly for data updates of figures created with `make_subplots`
Body: **Describe your context**
Hi, I am Jonas, one of the core developers of [`plotly-resampler`](https://github.com/predict-idlab/plotly-resampler/).
For our library, we created a custom component i.e., [`traceUpdater`](https://github.com/predict-idlab/trace-updater), to perform partial trace updates.
But as `Patch` was released, we think that this `traceUpdater` component can be omitted (since we also got some issues which were easily resolved by utilizing patch) - see https://github.com/predict-idlab/plotly-resampler/issues/271.
**Describe the bug**
However, when updating the `plotly-resampler` codebase to solely utilize `Patch` (PR: https://github.com/predict-idlab/plotly-resampler/pull/281) I remarked that `Patch` does not work when the figure consists of subplots (i.e. made with `make_subplots`). Is this expected behavior? see 📷 ⬇️
**Environment**
- clone the `refactor/remove-trace-updater` plotly-resampler branch and toy around with the example provided in - https://github.com/predict-idlab/plotly-resampler/pull/281
**Examples & Screenshots**
```python
from plotly_resampler import FigureResampler
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import numpy as np
x = np.arange(2_000_000)
noisy_sin = (3 + np.sin(x / 200) + np.random.randn(len(x)) / 10) * x / 1_000
fig = FigureResampler(
make_subplots(rows=2, cols=2, shared_xaxes="columns", horizontal_spacing=0.03)
)
log = noisy_sin * 0.9999995**x
exp = noisy_sin * 1.000002**x
fig.add_trace(go.Scattergl(name="log"), hf_x=x, hf_y=log)
fig.add_trace(go.Scattergl(name="exp"), hf_x=x, hf_y=exp)
fig.add_trace(go.Scattergl(name="-log"), hf_x=x, hf_y=-exp, row=1, col=2)
fig.add_trace(go.Scattergl(name="log"), hf_x=x, hf_y=-log, row=2, col=1)
fig.add_trace(go.Scattergl(name="3-exp"), hf_x=x, hf_y=3 - exp, row=2, col=1)
fig.add_trace(go.Scattergl(name="log"), hf_x=x, hf_y=log**2, row=2, col=2)
fig.show_dash(mode="inline")
```
| 1medium
|
Title: [BUG] Redundant fields required when using merge_builder in DeltaTableWriter
Body: When merging data into a Delta table using `DeltaTableWriter` with the `merge_builder` param one needs to provide the source DataFrame and the target table name as arguments to `DeltaTableWriter`, even though they are already passed to the merge builder. Otherwise Koheesio throws an error about missing required fields. So effectively we are passing the same values twice.
## Describe the bug
Take this example of merging data into a Delta table using _merge_builder_:
```
spark = get_active_session()
merge_condition = (
"target.COLUMN_A = source.COLUMN_A AND target.COLUMN_B = source.COLUMN_B"
)
DeltaTableWriter(
df=source_df,
table=target_table_name,
output_mode=BatchOutputMode.MERGE,
output_mode_params={
"merge_builder": (
DeltaTable.forName(sparkSession=spark, tableOrViewName=target_table_name)
.alias("target")
.merge(source=source_df.alias("source"), condition=merge_condition)
.whenNotMatchedInsertAll()
)
},
).write()
```
Even though in the `merge_builder` param we already pass `source_df` and `target_table_name`, we still need to pass the same values as the `df` and `table` arguments respectively to `DeltaTableWriter`. Otherwise Pydantic will throw a Validation Error.
## Steps to Reproduce
Use the above code snippet to merge data into a Delta table using merge builder to trigger the error.
## Expected behavior
When using a merge builder with appropriate params, one should not be forced to additionally pass the `df` and `table` params to `DeltaTableWriter`.
## Environment
Koheesio 0.9.0 | 1medium
|
Title: Can callback be set to take effect with the current page or globally
Body: Can callback be set to take effect with the current page or globally? Is this more reasonable? | 1medium
|
Title: github3.models.GitHubError: 401 Requires username/password authentication
Body: ##
| 1medium
|
Title: Call args/kwargs stored in a Spy can be overwritten without intention
Body: Suppose I have the following class definition and test code (in `test.py`):
```
class AClass:
def foo(self, x):
return None
def bar(self):
x = {"data": [1]}
self.foo(x)
x["data"].append(2)
self.foo(x)
def test_bar(mocker):
a = AClass()
spy = mocker.spy(a, "foo")
a.bar()
print(spy.call_args_list)
```
I want to check if `foo` has been called with the correct arguments. However, when I run `pytest -s test.py` I see the following output:
```
...
test.py::test_bar [call({'data': [1, 2]}), call({'data': [1, 2]})]
...
```
where I would've expected:
```
...
test.py::test_bar [call({'data': [1]}), call({'data': [1, 2]})]
...
```
I suspect the spy stores a reference to the calling args and kwargs, which allows for this behavior to happen. Creating a `deepcopy` would solve the issue, but I realize it can be quite costly to do so. Alternatively, having a flag to enable deep-copying if required would be useful. | 1medium
|
Title: can i run this in google colab without any problem
Body: and i tried too
and i got this
Running a test of your configuration...
Found 1 GPUs available. Using GPU 0 (Tesla T4) of compute capability 7.5 with 15.8Gb total memory.
Preparing the encoder, the synthesizer and the vocoder...
Loaded encoder "encoder.pt" trained to step 1564501
Synthesizer using device: cuda
Building Wave-RNN
Trainable Parameters: 4.481M
Loading model weights at saved_models/default/vocoder.pt
Testing your configuration with small inputs.
Testing the encoder...
Traceback (most recent call last):
File "/content/Real-Time-Voice-Cloning/demo_cli.py", line 80, in <module>
encoder.embed_utterance(np.zeros(encoder.sampling_rate))
File "/content/Real-Time-Voice-Cloning/encoder/inference.py", line 144, in embed_utterance
frames = audio.wav_to_mel_spectrogram(wav)
File "/content/Real-Time-Voice-Cloning/encoder/audio.py", line 58, in wav_to_mel_spectrogram
frames = librosa.feature.melspectrogram(
TypeError: melspectrogram() takes 0 positional arguments but 2 positional arguments (and 2 keyword-only arguments) were given
Colab paid products - Cancel contracts here
| 1medium
|
Title: Failed to generate or install certificate! :(
Body: Facing below issue when trying to run zappa certify
```
(myenv) root@ip-10-0-0-20:/home/ubuntu/zappa-s3-signature# zappa certify prod
Calling certify for stage prod..
Are you sure you want to certify? [y/n] y
Certifying domain cnd.doxbot.io..
Setting DNS challenge..
Waiting for DNS to propagate..
Domain challenge did not pass: {u'status': u'invalid', u'token': u'86P82OiXd8YvlSl5UK-7-NRLhIWYtHx_wyUfiLoSehs', u'type': u'dns-01', u'uri': u'https://acme-v01.api.letsencrypt.org/acme/challenge/Vv84wVPPZN6QaW4-oDO-koxR_RXNtoYRtIWdIc3THaE/18970297341', u'error': {u'status': 403, u'type': u'urn:acme:error:unauthorized', u'detail': u'No TXT record found at _acme-challenge.cnd.doxbot.io'}}
Failed to generate or install certificate! :(
==============
```
Route 53 entries
Note: The TXT record was created by zappa, it isn't done manually
| 1medium
|
Title: Django 2.1 support
Body: When will viewflow supporto Django 2.1? I'm mostly concerned about the view permission | 1medium
|
Title: Add an argument to ignore --cookies-from-browser errors
Body: ### DO NOT REMOVE OR SKIP THE ISSUE TEMPLATE
- [X] I understand that I will be **blocked** if I *intentionally* remove or skip any mandatory\* field
### Checklist
- [X] I'm requesting a feature unrelated to a specific site
- [X] I've looked through the [README](https://github.com/yt-dlp/yt-dlp#readme)
- [X] I've verified that I have **updated yt-dlp to nightly or master** ([update instructions](https://github.com/yt-dlp/yt-dlp#update-channels))
- [X] I've searched [known issues](https://github.com/yt-dlp/yt-dlp/issues/3766) and the [bugtracker](https://github.com/yt-dlp/yt-dlp/issues?q=) for similar issues **including closed ones**. DO NOT post duplicates
- [X] I've read the [guidelines for opening an issue](https://github.com/yt-dlp/yt-dlp/blob/master/CONTRIBUTING.md#opening-an-issue)
### Provide a description that is worded well enough to be understood
I would like to use YT-DLP with --cookies-from-browser argument.
However, in case it fails to extract cookies, I would like it to just proceed with no cookies.
I suggest to add an additional command line argument for this. Something like --cookies-from-browser-ignore-errors.
Thanks! :)
### Provide verbose output that clearly demonstrates the problem
- [X] Run **your** yt-dlp command with **-vU** flag added (`yt-dlp -vU <your command line>`)
- [X] If using API, add `'verbose': True` to `YoutubeDL` params instead
- [X] Copy the WHOLE output (starting with `[debug] Command-line config`) and insert it below
### Complete Verbose Output
```
[debug] Command-line config: ['-vU', '--cookies-from-browser', 'chrome', 'https://www.youtube.com/watch?v=p6ebfMzTgyY']
[debug] Encodings: locale cp1251, fs utf-8, pref cp1251, out utf-8, error utf-8, screen utf-8
[debug] yt-dlp version [email protected] from yt-dlp/yt-dlp [65cf46cdd] (source)
[debug] Lazy loading extractors is disabled
[debug] Git HEAD: 0b6b7742c
[debug] Python 3.10.11 (CPython AMD64 64bit) - Windows-10-10.0.19045-SP0 (OpenSSL 1.1.1t 7 Feb 2023)
[debug] exe versions: ffmpeg 4.3.1
[debug] Optional libraries: sqlite3-3.40.1
[debug] Proxy map: {}
Extracting cookies from chrome
[debug] Extracting cookies from: "C:\Users\user\AppData\Local\Google\Chrome\User Data\Default\Network\Cookies"
[debug] Found local state file at "C:\Users\user\AppData\Local\Google\Chrome\User Data\Local State"
[Cookies] Loading cookie 0/ 30ERROR: Failed to decrypt with DPAPI. See https://github.com/yt-dlp/yt-dlp/issues/10927 for more info
File "D:\Work\Source\yt-dlp\yt_dlp\__main__.py", line 17, in <module>
yt_dlp.main()
File "D:\Work\Source\yt-dlp\yt_dlp\__init__.py", line 1093, in main
_exit(*variadic(_real_main(argv)))
File "D:\Work\Source\yt-dlp\yt_dlp\__init__.py", line 991, in _real_main
with YoutubeDL(ydl_opts) as ydl:
File "D:\Work\Source\yt-dlp\yt_dlp\YoutubeDL.py", line 720, in __init__
self.print_debug_header()
File "D:\Work\Source\yt-dlp\yt_dlp\YoutubeDL.py", line 4078, in print_debug_header
write_debug(f'Request Handlers: {", ".join(rh.RH_NAME for rh in self._request_director.handlers.values())}')
File "C:\Users\user\AppData\Local\Programs\Python\Python310\lib\functools.py", line 981, in __get__
val = self.func(instance)
File "D:\Work\Source\yt-dlp\yt_dlp\YoutubeDL.py", line 4252, in _request_director
return self.build_request_director(_REQUEST_HANDLERS.values(), _RH_PREFERENCES)
File "D:\Work\Source\yt-dlp\yt_dlp\YoutubeDL.py", line 4227, in build_request_director
cookiejar=self.cookiejar,
File "C:\Users\user\AppData\Local\Programs\Python\Python310\lib\functools.py", line 981, in __get__
val = self.func(instance)
File "D:\Work\Source\yt-dlp\yt_dlp\YoutubeDL.py", line 4118, in cookiejar
return load_cookies(
File "D:\Work\Source\yt-dlp\yt_dlp\cookies.py", line 99, in load_cookies
extract_cookies_from_browser(browser_name, profile, YDLLogger(ydl), keyring=keyring, container=container))
File "D:\Work\Source\yt-dlp\yt_dlp\cookies.py", line 122, in extract_cookies_from_browser
return _extract_chrome_cookies(browser_name, profile, keyring, logger)
File "D:\Work\Source\yt-dlp\yt_dlp\cookies.py", line 331, in _extract_chrome_cookies
is_encrypted, cookie = _process_chrome_cookie(decryptor, *line)
File "D:\Work\Source\yt-dlp\yt_dlp\cookies.py", line 366, in _process_chrome_cookie
value = decryptor.decrypt(encrypted_value)
File "D:\Work\Source\yt-dlp\yt_dlp\cookies.py", line 551, in decrypt
return _decrypt_windows_dpapi(encrypted_value, self._logger).decode()
File "D:\Work\Source\yt-dlp\yt_dlp\cookies.py", line 1087, in _decrypt_windows_dpapi
logger.error(message)
File "D:\Work\Source\yt-dlp\yt_dlp\utils\_utils.py", line 5650, in error
self._ydl.report_error(message, is_error=is_error)
File "D:\Work\Source\yt-dlp\yt_dlp\YoutubeDL.py", line 1092, in report_error
self.trouble(f'{self._format_err("ERROR:", self.Styles.ERROR)} {message}', *args, **kwargs)
File "D:\Work\Source\yt-dlp\yt_dlp\YoutubeDL.py", line 1020, in trouble
tb_data = traceback.format_list(traceback.extract_stack())
ERROR: Failed to decrypt with DPAPI. See https://github.com/yt-dlp/yt-dlp/issues/10927 for more info
Traceback (most recent call last):
File "D:\Work\Source\yt-dlp\yt_dlp\cookies.py", line 99, in load_cookies
extract_cookies_from_browser(browser_name, profile, YDLLogger(ydl), keyring=keyring, container=container))
File "D:\Work\Source\yt-dlp\yt_dlp\cookies.py", line 122, in extract_cookies_from_browser
return _extract_chrome_cookies(browser_name, profile, keyring, logger)
File "D:\Work\Source\yt-dlp\yt_dlp\cookies.py", line 331, in _extract_chrome_cookies
is_encrypted, cookie = _process_chrome_cookie(decryptor, *line)
File "D:\Work\Source\yt-dlp\yt_dlp\cookies.py", line 366, in _process_chrome_cookie
value = decryptor.decrypt(encrypted_value)
File "D:\Work\Source\yt-dlp\yt_dlp\cookies.py", line 551, in decrypt
return _decrypt_windows_dpapi(encrypted_value, self._logger).decode()
File "D:\Work\Source\yt-dlp\yt_dlp\cookies.py", line 1088, in _decrypt_windows_dpapi
raise DownloadError(message) # force exit
yt_dlp.utils.DownloadError: Failed to decrypt with DPAPI. See https://github.com/yt-dlp/yt-dlp/issues/10927 for more info
``` | 1medium
|
Title: [WavLM] Finetuning for speaker diarization
Body: I am intending on using WavLM and finetuning it for speaker diarization. My aim is to obviously get the DER's that is in Readme for WavLM. I tried to find some resources on how to even begin this, and found some things on speaker recognition and verification. However, that isn't speaker diarization, so I was wondering if anyone had any pointers? (At the very least a starting place.)
| 2hard
|
Title: [BUG] Merging PDF + PNG omits PNG
Body: ### Description
When attempting to merge a PDF-based document with another document that resulted from a PNG upload the merge process finishes without reporting any obvious error, but the resulting "merged" PDF is missing the page from the PNG-based document. No error is visible from the web frontend.
Working around the issue by downloading and re-uploading the PDF generated for the PNG (and using that for the merge) unfortunately does not work as it is rejected as a duplicate.
### Steps to reproduce
1. Upload PDF
2. Upload PNG
3. Select both documents for merging (generate new metadata, do not delete original files).
### Webserver logs
```bash
[2024-08-27 10:44:43,663] [INFO] [paperless.bulk_edit] Attempting to merge 2 documents into a single document.
[2024-08-27 10:44:43,820] [ERROR] [paperless.bulk_edit] Error merging document 592, it will not be included in the merge: /usr/src/paperless/media/documents/originals/0000592.png: unable to find trailer dictionary while recovering damaged file
Traceback (most recent call last):
File "/usr/src/paperless/src/documents/bulk_edit.py", line 262, in merge
with pikepdf.open(str(doc.source_path)) as pdf:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/pikepdf/_methods.py", line 398, in open
pdf = Pdf._open(
^^^^^^^^^^
pikepdf._core.PdfError: /usr/src/paperless/media/documents/originals/0000592.png: unable to find trailer dictionary while recovering damaged file
[2024-08-27 10:44:43,822] [INFO] [paperless.bulk_edit] Adding merged document to the task queue.
[2024-08-27 10:44:43,857] [INFO] [celery.worker.strategy] Task documents.tasks.consume_file[fa0ac878-d0ad-472f-9f8b-b1a6f934d58a] received
[2024-08-27 10:44:43,905] [INFO] [paperless.tasks] WorkflowTriggerPlugin completed with:
[2024-08-27 10:44:43,913] [INFO] [paperless.consumer] Consuming 618_592_merged.pdf
[2024-08-27 10:44:43,931] [INFO] [paperless.parsing.tesseract] pdftotext exited 0
[2024-08-27 10:44:45,693] [INFO] [ocrmypdf._pipeline] page is facing ⇧, confidence 5.52 - no change
[2024-08-27 10:44:55,983] [INFO] [ocrmypdf._pipelines.ocr] Postprocessing...
[2024-08-27 10:44:56,807] [INFO] [ocrmypdf._pipeline] Image optimization ratio: 1.22 savings: 18.3%
[2024-08-27 10:44:56,808] [INFO] [ocrmypdf._pipeline] Total file size ratio: 1.24 savings: 19.6%
[2024-08-27 10:44:56,810] [INFO] [ocrmypdf._pipelines._common] Output file is a PDF/A-2B (as expected)
[2024-08-27 10:44:58,394] [INFO] [paperless.parsing] convert exited 0
[2024-08-27 10:45:01,713] [INFO] [paperless.handlers] Assigning correspondent Thilo-Alexander Ginkel to 2024-08-26 618_592_merged
[2024-08-27 10:45:01,722] [INFO] [paperless.handlers] Assigning document type Abrechnung to 2024-08-26 Thilo-Alexander Ginkel 618_592_merged
[2024-08-27 10:45:01,732] [INFO] [paperless.handlers] Tagging "2024-08-26 Thilo-Alexander Ginkel 618_592_merged" with "Veranstaltung, Abrechnung"
[2024-08-27 10:45:01,799] [INFO] [paperless.consumer] Document 2024-08-26 Thilo-Alexander Ginkel 618_592_merged consumption finished
[2024-08-27 10:45:01,803] [INFO] [paperless.tasks] ConsumeTaskPlugin completed with: Success. New document id 619 created
```
### Browser logs
_No response_
### Paperless-ngx version
2.11.6
### Host OS
Ubuntu 22.04
### Installation method
Docker - official image
### System status
```json
{
"pngx_version": "2.11.6",
"server_os": "Linux-5.15.0-117-generic-x86_64-with-glibc2.36",
"install_type": "docker",
"storage": {
"total": 473533612032,
"available": 359678787584
},
"database": {
"type": "postgresql",
"url": "paperless",
"status": "OK",
"error": null,
"migration_status": {
"latest_migration": "documents.1052_document_transaction_id",
"unapplied_migrations": []
}
},
"tasks": {
"redis_url": "redis://broker:6379",
"redis_status": "OK",
"redis_error": null,
"celery_status": "OK",
"index_status": "OK",
"index_last_modified": "2024-08-27T10:47:27.021629+02:00",
"index_error": null,
"classifier_status": "OK",
"classifier_last_trained": "2024-08-27T08:05:00.617726Z",
"classifier_error": null
}
}
```
### Browser
_No response_
### Configuration changes
_No response_
### Please confirm the following
- [X] I believe this issue is a bug that affects all users of Paperless-ngx, not something specific to my installation.
- [X] I have already searched for relevant existing issues and discussions before opening this report.
- [X] I have updated the title field above with a concise description. | 1medium
|
Title: fsspec.exceptions.FSTimeoutError when downloading dataset
Body: ### Describe the bug
I am trying to download the `librispeech_asr` `clean` dataset, which results in a `FSTimeoutError` exception after downloading around 61% of the data.
### Steps to reproduce the bug
```
import datasets
datasets.load_dataset("librispeech_asr", "clean")
```
The output is as follows:
> Downloading data: 61%|██████████████▋ | 3.92G/6.39G [05:00<03:06, 13.2MB/s]Traceback (most recent call last):
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 56, in _runner
> result[0] = await coro
> ^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/implementations/http.py", line 262, in _get_file
> chunk = await r.content.read(chunk_size)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/streams.py", line 393, in read
> await self._wait("read")
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/streams.py", line 311, in _wait
> with self._timer:
> ^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/aiohttp/helpers.py", line 713, in __exit__
> raise asyncio.TimeoutError from None
> TimeoutError
>
> The above exception was the direct cause of the following exception:
>
> Traceback (most recent call last):
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/load_dataset.py", line 3, in <module>
> datasets.load_dataset("librispeech_asr", "clean")
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/load.py", line 2096, in load_dataset
> builder_instance.download_and_prepare(
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 924, in download_and_prepare
> self._download_and_prepare(
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 1647, in _download_and_prepare
> super()._download_and_prepare(
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/builder.py", line 977, in _download_and_prepare
> split_generators = self._split_generators(dl_manager, **split_generators_kwargs)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/.cache/huggingface/modules/datasets_modules/datasets/librispeech_asr/2712a8f82f0d20807a56faadcd08734f9bdd24c850bb118ba21ff33ebff0432f/librispeech_asr.py", line 115, in _split_generators
> archive_path = dl_manager.download(_DL_URLS[self.config.name])
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 159, in download
> downloaded_path_or_paths = map_nested(
> ^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py", line 512, in map_nested
> _single_map_nested((function, obj, batched, batch_size, types, None, True, None))
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py", line 380, in _single_map_nested
> return [mapped_item for batch in iter_batched(data_struct, batch_size) for mapped_item in function(batch)]
> ^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 216, in _download_batched
> self._download_single(url_or_filename, download_config=download_config)
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/download/download_manager.py", line 225, in _download_single
> out = cached_path(url_or_filename, download_config=download_config)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 205, in cached_path
> output_path = get_from_cache(
> ^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 415, in get_from_cache
> fsspec_get(url, temp_file, storage_options=storage_options, desc=download_desc, disable_tqdm=disable_tqdm)
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 334, in fsspec_get
> fs.get_file(path, temp_file.name, callback=callback)
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 118, in wrapper
> return sync(self.loop, func, *args, **kwargs)
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "/Users/Timon/Documents/iEEG_deeplearning/wav2vec_pretrain/.venv/lib/python3.12/site-packages/fsspec/asyn.py", line 101, in sync
> raise FSTimeoutError from return_result
> fsspec.exceptions.FSTimeoutError
> Downloading data: 61%|██████████████▋ | 3.92G/6.39G [05:00<03:09, 13.0MB/s]
### Expected behavior
Complete the download
### Environment info
Python version 3.12.6
Dependencies:
> dependencies = [
> "accelerate>=0.34.2",
> "datasets[audio]>=3.0.0",
> "ipython>=8.18.1",
> "librosa>=0.10.2.post1",
> "torch>=2.4.1",
> "torchaudio>=2.4.1",
> "transformers>=4.44.2",
> ]
MacOS 14.6.1 (23G93) | 1medium
|
Title: problem about the preprocess of face image before feed it into face recognition onnx model
Body: I download the resnet 18 pretrained model(R18 | Glint360K | 72.07) for face encoding or face embedding or anything we call it, and it is onnx format. I do not know how to preprocess the aligned face image before feed it into this onnx model. I use the another face detect model and the alignment of dlib library. in the face embedding, I'd like use insightface pretrained model. I'd appeciated if any one can help me. | 1medium
|
Title: ES Autocomplete search queries should properly use boosted fields
Body: ### Is your proposal related to a problem?
The fixes to Elasticsearch boosting from #10653 never properly made it into autocomplete queries. This means that if you're using the autocomplete method to search pages, it will often give a higher rank to pages with titles that don't match the search results. I've been able to make these changes in the site that I discovered this issue in, but the existing code is written in such a way that it's a challenge to make small changes to search backends without having to duplicate a lot of base Wagtail code, which I'd really like to not do.
### Describe the solution you'd like
Autocomplete queries should also have boosted fields that are copied to using the same method as the base query compiler and then queried with a boost at runtime.
### Describe alternatives you've considered
If the Wagtail team doesn't want to fully support this, it would still be appreciated to be able to break out some of the existing methods in ways that make it easier to extend.
### Additional context
I know that the Wagtail team is generally critical about using partial matching/autocompletion for normal searches, but this came up because I've developed a relatively large Wagtail instance that uses autocomplete queries for their base site search. The client sells a large amount of products, and these products are often referred to by shortened versions of the page title. For example, 299 is a very common search term on our site, and users (and our client) expect to be able to find all of the products sold on the site in that product line, all of which will have titles like 299E4STD3G. In our cases, it makes sense then to use edgengrams for our main search, as that's one of the primary ways that users are browsing the site. I wouldn't be surprised if other Wagtail instances have similar requirements, so I think this is a reasonable usecase to support.
### Working on this
I've already developed a solution for my site, so I know what places in the code need to be changed. I would likely need to do some additional work (tests, etc) to get it ready for the main repo, but I would be happy to work on it.
Anyone can contribute to this. View our [contributing guidelines](https://docs.wagtail.org/en/latest/contributing/index.html), add a comment to the issue once you’re ready to start.
| 1medium
|
Title: Heatmaps
Body: Hello, Is it possible to get heatmaps from EasyOCR?
Thank you in advance. | 1medium
|
Title: Remove Direct Arm Compute Libray (ACL) Integration for Quantized Matmuls: `qlinear`/`qlinear_dynamic`
Body: PR https://github.com/pytorch/pytorch/pull/148585 (temporarily) introduced a direct ACL implementation for `qlinear` and `qlinear_dynamic` for AArch64 when `USE_MKLDNN_ACL` is set.
This direct ACL implementation is a lot faster than the existing implementations that utilized ACL through oneDNN (MKLDNN) due to the (current) API friction between the stateful ACL API and the stateless oneDNN API (see benchmarks and numbers on https://github.com/pytorch/pytorch/pull/148585).
I'm creating this issue to make sure that we end up removing this direct ACL path for `qlinear` and `qlinear_dynamic` once we're done enabling a fast implementation for quantized matmuls through oneDNN+ACL.
cc @jerryzh168 @jianyuh @raghuramank100 @jamesr66a @vkuzo @jgong5 @Xia-Weiwen @leslie-fang-intel @msaroufim @malfet @snadampal @milpuz01 | 2hard
|
Title: [BUG] Settings keep resetting
Body: **Describe the bug**
Seemingly by random, my settings keep getting reset
**To Reproduce**
Steps to reproduce the behavior:
1. Search a couple times
2. Settings get reset
**Deployment Method**
- [ ] Heroku (one-click deploy)
- [ ] Docker
- [ ] `run` executable
- [x] pip/pipx
- [ ] Other: [describe setup]
**Version of Whoogle Search**
- [ ] Latest build from [source] (i.e. GitHub, Docker Hub, pip, etc)
- [x] Version [v0.7.1]
- [ ] Not sure
**Instance:**
https://gowogle.voring.me
**Desktop (please complete the following information):**
- OS: Linux
- Browser: Mozilla Firefox 97.0
| 1medium
|
Title: train meachine translation OOM
Body: ### Description
Can you tell me why even I set batch_size to 4, also occur OOM problem ？
I know maybe the OOM problem because of model save and eval, but I don't know the OOM problem more specific.
### Environment information
python /root/anaconda3/lib/python3.6/site-packages/tensor2tensor/bin/t2t_trainer.py --data_dir=./data_dir \
--problem=translate_enzh_bpe50k \
--model=transformer \
--hparams="batch_size=4" \
--hparams_set=transformer_base_single_gpu \
--output_dir=./en_zh_model \
--schedule=continuous_train_and_eval \
--train_steps=900000 \
--t2t_usr_dir=user_dir
process the english data with bpe.
python 3.7
tensor2tensor == 1.9.0
tensorflow-gpu == 1.12.0
```
OS: <your answer here>
$ pip freeze | grep tensor
# your output here
$ python -V
# your output here
```
### For bugs: reproduction and error logs
```
# Steps to reproduce:
...
```
```
# Error logs:
...
```
| 2hard
|
Title: Alternative solution for 21
Body: You can also use:
```python
np.tile(np.identity(2),(4,4))
``` | 3misc
|
Title: Bug: For external Executors, passing entire address to `host` does not work
Body: The following syntax, which is supported for the gateway, does not work for external executors:
```python
f.add(host='grpc://localhost:1234', external=True)
```
Instead, host and port have to be precised separately:
```python
f.add(host='localhost', port=1234, external=True)
```
Not that this bug also applies to replicated external Executors:
```python
f.add(host='grpc://localhost:1234,grpc://localhost:1235', external=True)
```
| 1medium
|
Title: test_messages, maybe a mistake, maybe my error
Body: Current block is:
```
def test_messages(client):
"""Ensure that user can post messages"""
login(client, app.config["USERNAME"], app.config["PASSWORD"])
rv = client.post(
"/add",
data=dict(title="<Hello>", text="<strong>HTML</strong> allowed here"),
follow_redirects=True,
)
assert b"No entries here so far" not in rv.data
assert b"<Hello>" in rv.data
assert b"<strong>HTML</strong> allowed here" in rv.data
```
But this never passes the final test.
I replaced the first of the 3 asserts with
`assert b"New entry was successfully posted" in rv.data`
Which then passes | 1medium
|
Title: Importing words throws numpy deprecation warning
Body: Warning:
DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps | 0easy
|
Title: Error reported when NNI was used to prune Albert-tiny
Body: **Describe the issue**:
I'm just pruning the linear layer. Everything seemed fine, but the following problem occurred when I was about to use the ModelSpeedup module to speed up the model.
```
device = torch.device("cpu")
inputs =(torch.LongTensor([tokenizer_out['input_ids']]).to(device),torch.LongTensor([tokenizer_out['attention_mask']]).to(device))
ModelSpeedup(S_model, inputs, masks).speedup_model()
```
```
config_list = [{
'sparsity_per_layer': 0.5,
'op_types': ['Linear']
}, {
'exclude': True,
'op_names': ['dense2']
}]
```
```
bert_model.transformer.layer.0.attention.q_lin sparsity : 0.5
bert_model.transformer.layer.0.attention.k_lin sparsity : 0.5
bert_model.transformer.layer.0.attention.v_lin sparsity : 0.5
bert_model.transformer.layer.0.attention.out_lin sparsity : 0.5
bert_model.transformer.layer.0.ffn.lin1 sparsity : 0.5
bert_model.transformer.layer.0.ffn.lin2 sparsity : 0.5
bert_model.transformer.layer.1.attention.q_lin sparsity : 0.5
bert_model.transformer.layer.1.attention.k_lin sparsity : 0.5
bert_model.transformer.layer.1.attention.v_lin sparsity : 0.5
bert_model.transformer.layer.1.attention.out_lin sparsity : 0.5
bert_model.transformer.layer.1.ffn.lin1 sparsity : 0.5
bert_model.transformer.layer.1.ffn.lin2 sparsity : 0.5
bert_model.transformer.layer.2.attention.q_lin sparsity : 0.5
bert_model.transformer.layer.2.attention.k_lin sparsity : 0.5
bert_model.transformer.layer.2.attention.v_lin sparsity : 0.5
bert_model.transformer.layer.2.attention.out_lin sparsity : 0.5
bert_model.transformer.layer.2.ffn.lin1 sparsity : 0.5
bert_model.transformer.layer.2.ffn.lin2 sparsity : 0.5
bert_model.transformer.layer.3.attention.q_lin sparsity : 0.5
bert_model.transformer.layer.3.attention.k_lin sparsity : 0.5
bert_model.transformer.layer.3.attention.v_lin sparsity : 0.5
bert_model.transformer.layer.3.attention.out_lin sparsity : 0.5
bert_model.transformer.layer.3.ffn.lin1 sparsity : 0.5
bert_model.transformer.layer.3.ffn.lin2 sparsity : 0.5
cpu
[2022-10-26 08:50:04] start to speedup the model
no multi-dimension masks found.
[2022-10-26 08:50:05] infer module masks...
[2022-10-26 08:50:05] Update mask for bert_model.embeddings.word_embeddings
[2022-10-26 08:50:06] Update mask for bert_model.embeddings.aten::size.46
[2022-10-26 08:50:06] Update mask for bert_model.embeddings.aten::slice.47
[2022-10-26 08:50:06] Slice dim:0, Slice obj:slice(0, 9223372036854775807, 1)
[2022-10-26 08:50:06] Get attribute: bert_model
[2022-10-26 08:50:06] Get attribute: embeddings
[2022-10-26 08:50:06] Get attribute: position_ids
[2022-10-26 08:50:06] Update mask for bert_model.transformer.layer.0.attention.aten::eq.61
[2022-10-26 08:50:06] Update mask for bert_model.transformer.layer.1.attention.aten::eq.84
[2022-10-26 08:50:06] Update mask for bert_model.transformer.layer.2.attention.aten::eq.107
[2022-10-26 08:50:06] Update mask for bert_model.transformer.layer.3.attention.aten::eq.130
[2022-10-26 08:50:06] Update mask for bert_model.embeddings.aten::slice.48
[2022-10-26 08:50:06] Slice dim:1, Slice obj:slice(0, tensor([300]), 1)
[2022-10-26 08:50:06] Model has Slice operation, and the operand size=torch.Size([1, 512]), Slice object:(slice(None, None, None), slice(0, tensor([300]), 1))
[2022-10-26 08:50:06] Model has Slice operation, and the operand size=torch.Size([1, 512]), Slice object:(slice(None, None, None), slice(0, tensor([300]), 1))
[2022-10-26 08:50:06] Update mask for bert_model.embeddings.position_embeddings
[2022-10-26 08:50:06] Update mask for bert_model.embeddings.aten::add.49
[2022-10-26 08:50:06] Update mask for bert_model.embeddings.LayerNorm
[2022-10-26 08:50:06] Update mask for bert_model.embeddings.dropout
[2022-10-26 08:50:06] Update mask for bert_model.transformer.layer.0.attention.q_lin
[2022-10-26 08:50:06] Update mask for bert_model.transformer.layer.0.attention.k_lin
[2022-10-26 08:50:06] Update mask for bert_model.transformer.layer.0.attention.v_lin
[2022-10-26 08:50:06] Update mask for bert_model.transformer.layer.0.attention.aten::size.50
[2022-10-26 08:50:06] Update mask for bert_model.transformer.layer.0.attention.aten::size.51
[2022-10-26 08:50:06] Update mask for bert_model.transformer.layer.0.attention.aten::view.52
[2022-10-26 08:50:06] WARNING: throw some args away when calling the function "view"
[2022-10-26 08:50:06] WARNING: throw some args away when calling the function "view"
[2022-10-26 08:50:06] Update mask for bert_model.transformer.layer.0.attention.aten::view.54
[2022-10-26 08:50:06] WARNING: throw some args away when calling the function "view"
[2022-10-26 08:50:06] WARNING: throw some args away when calling the function "view"
[2022-10-26 08:50:06] Update mask for bert_model.transformer.layer.0.attention.aten::view.56
[2022-10-26 08:50:06] WARNING: throw some args away when calling the function "view"
[2022-10-26 08:50:06] WARNING: throw some args away when calling the function "view"
[2022-10-26 08:50:06] Update mask for bert_model.transformer.layer.0.attention.aten::view.62
[2022-10-26 08:50:06] WARNING: throw some args away when calling the function "view"
Traceback (most recent call last):
File "/workspace/bert2distill_albert/prune_s2_main.py", line 67, in <module>
prune_student_model(configs, dataManager, logger)
File "/workspace/bert2distill_albert/engines/prune_distill_model.py", line 112, in prune_student_model
ModelSpeedup(S_model, inputs, masks).speedup_model()
File "/root/miniconda3/envs/tf_nlp/lib/python3.8/site-packages/nni/compression/pytorch/speedup/compressor.py", line 543, in speedup_model
self.infer_modules_masks()
File "/root/miniconda3/envs/tf_nlp/lib/python3.8/site-packages/nni/compression/pytorch/speedup/compressor.py", line 380, in infer_modules_masks
self.update_direct_sparsity(curnode)
File "/root/miniconda3/envs/tf_nlp/lib/python3.8/site-packages/nni/compression/pytorch/speedup/compressor.py", line 247, in update_direct_sparsity
_auto_infer.update_direct_sparsity()
File "/root/miniconda3/envs/tf_nlp/lib/python3.8/site-packages/nni/compression/pytorch/speedup/infer_mask.py", line 328, in update_direct_sparsity
self.random_init()
File "/root/miniconda3/envs/tf_nlp/lib/python3.8/site-packages/nni/compression/pytorch/speedup/infer_mask.py", line 138, in random_init
randomize_tensor(tensor, start, end)
File "/root/miniconda3/envs/tf_nlp/lib/python3.8/site-packages/nni/compression/pytorch/utils/utils.py", line 72, in randomize_tensor
torch.randint(int(start), int(end), tensor.size(),
RuntimeError: to - 1 is out of bounds for bool
```
**Environment**:
- NNI version:2.9
- Training service (local|remote|pai|aml|etc):
- Client OS:
- Server OS (for remote mode only):
- Python version:3.8
- PyTorch/TensorFlow version:1.12.1
- Is conda/virtualenv/venv used?:
- Is running in Docker?:
**Configuration**:
- Experiment config (remember to remove secrets!):
- Search space:
**Log message**:
- nnimanager.log:
- dispatcher.log:
- nnictl stdout and stderr:
<!--
Where can you find the log files:
LOG: https://github.com/microsoft/nni/blob/master/docs/en_US/Tutorial/HowToDebug.md#experiment-root-director
STDOUT/STDERR: https://nni.readthedocs.io/en/stable/reference/nnictl.html#nnictl-log-stdout
-->
**How to reproduce it?**: | 2hard
|
Title: Don't use deprecated `is_int64_dtype` and `is_period_dtype` functions
Body: | 1medium
|
Title: Extending connections using relay on same level as edges.
Body: I have been pulling my hair out tying to solve this one. I would appreciate some help with this. Graphene Relay specs limits me to having my api calls received in the following format:
```
QUERY:
{
allSpecies {
edges {
node {
id
name
}
}
}
}
RECEIVED DATA
"data": {
"allSpecies": {
"edges": [
{
"node": {
"id": "U3BlY2llOjE=",
"name": "Human"
}
},
{
"node": {
"id": "U3BlY2llOjI=",
"name": "Alien"
}
}
]
}
}
}
```
I want to be able to create a same level property to access the api without going through edges and node first but still keep relay integrated for the purposes of pagination in case i need the added functionality.
```
NEW QUERY:
{
allSpecies {
newProperty{
id
name
}
}
}
```
In my current setup I am trying to point my newly created property "new property" to the edges node. How can I easily point to the same edges connection from the connection class and receive the same list of data? Is there a better way to do this?
```
class Custom(graphene.Connection):
class Meta:
abstract = True
new_property = graphene.List(graphene.String)
def resolve_new_property(self, info):
return self.edges
class Specie(DjangoObjectType):
eye_colors = graphene.List(graphene.String)
hair_colors = graphene.List(graphene.String)
skin_colors = graphene.List(graphene.String)
def resolve_eye_colors(self, info):
return [c.strip() for c in self.eye_colors.split(',')]
def resolve_hair_colors(self, info):
return [c.strip() for c in self.hair_colors.split(',')]
def resolve_skin_colors(self, info):
return [c.strip() for c in self.skin_colors.split(',')]
class Meta:
model = models.Species
interfaces = (Node, )
exclude_fields = ('created', 'edited', 'eye_colors', 'hair_colors',
'skin_colors')
filter_fields = {'name': {'startswith', 'contains'}}
connection_class = Custom
class Query(graphene.ObjectType):
all_species = DjangoFilterConnectionField(Specie)
schema = graphene.Schema(
query=Query,
mutation=Mutation
)
```
After running the query, I get the following error
```
{
"data": {
"allSpecies": {
"newProperty": "[<graphene.relay.connection.SpecieEdge object at 0x04BB2750>, <graphene.relay.connection.SpecieEdge object at 0x04BB23F0>]"
}
}
}
``` | 2hard
|
Title: Preset config should take priority over include
Body: We need to write a test that makes sure a preset's `config` section overrides the config from any other preset specified in the `include` section. | 1medium
|
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