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|---|---|
Title: Migrate from "yield from" to await (TypeError: object Engine can't be used in 'await' expression)
Body: Hi, i replaced in my code "yield from" to "await", and received Traceback:
"TypeError: object Engine can't be used in 'await' expression"
``` python
async def db_psql_middleware(app, handler):
async def middleware(request):
db = app.get('db_psql')
if not db:
app['db_psql'] = db = await create_engine(app['psql_dsn'], minsize=1, maxsize=5)
request.app['db_psql'] = db
return (await handler(request))
return middleware
async def psql_select(request):
with (await request.app['db_psql']) as conn:
result = await conn.execute(models.select())
```
Traceback
``` python
[2015-09-17 14:50:29 +0300] [26045] [ERROR] Error handling request
Traceback (most recent call last):
File "/Users/vvv/src/backend-tools/python/asyncio/venv35/lib/python3.5/site-packages/aiohttp/server.py", line 272, in start
yield from self.handle_request(message, payload)
File "/Users/vvv/src/backend-tools/python/asyncio/venv35/lib/python3.5/site-packages/aiohttp/web.py", line 85, in handle_request
resp = yield from handler(request)
File "/Users/vvv/src/backend-tools/python/asyncio/app.py", line 39, in middleware
return (await handler(request))
File "/Users/vvv/src/backend-tools/python/asyncio/app.py", line 46, in psql_select
with (await request.app['db_psql']) as conn:
TypeError: object Engine can't be used in 'await' expression
```
| 1medium
|
Title: Proposal: Support for Failure-topic forwarding
Body: Failure handling is an area where there is limited consensus within the Kafka community. One option for Faust would be adding support for failure forwarding in the same pattern as sinks. The API might look like:
```python
# in faust.models.record
# new Faust Record type specifically for error handling
class FailureEventRecord(Record):
errormsg: str
exception: AgentException
failed_record: Record
# in app.py
topic = app.topic('my.event', value_type=MyRecord)
failure_topic = app.topic('my.event.failed', value_type=faust.FailureEventRecord)
@app.agent(topic, sinks=[sink1, sink2], failures=[failed_record_operation])
async def my_exception_agent(records):
with record in records:
raising_operation(record)
```
## Checklist
- [x] I have included information about relevant versions
- [x] I have verified that the issue persists when using the `master` branch of Faust.
## Steps to reproduce
Documentations says:
> "Crashing the instance to require human intervention is certainly a choice, but far from ideal considering how common mistakes in code or unexpected exceptions are. It may be better to log the error and have ops replay and reprocess the stream on notification."
## Expected behavior
Any events which fail stream processing in an agent will be wrapped in a `FailureEventRecord` and delivered to the topic or topics specified in the `failures` parameter to `app.agent()`.
# Versions
* Python 3.6
* Faust 1.7.0
* MacOS 10.14.5
* Kafka ?
* RocksDB version (if applicable)
| 2hard
|
Title: faster-rcnn performance
Body: hi, wuyuxin
Thank you for sharing this wonderful project! Here I have some performance problems of faster-rcnn.
In coco dataset, experiments results show GN has better performance.
R50-FPN | 38.9;35.4
R50-FPN-GN | 40.4;36.3
However, in my dataset(ocr icdar2017), GN performance is poor than FreezeBN. Scratch performance is also poor than pre-trainend which is contrary to your conclusion. Here are the results on my dataset.
pre-train, resnet101, FreezeBN, freeze_AT=2, f-score:0.7742
pre-train, resnet101, GN, freeze_AT=2, f-score:0.7605
no pre-train(From Scratch), resnet101, GN, freeze_AT=0, f-score:0.7106
Hope for your suggestion.
| 1medium
|
Title: Docs breadcrumbs usually start with 'Intro'
Body: e.g. 'Intro > Legal'. This doesn't make sense.
| 0easy
|
Title: linux agent disconnect after seconds
Body: **Server Info :**
- OS: Ubuntu 20.04
- Browser: Chrome
- RMM Version (as shown in top left of web UI): 0.15.3
**Installation Method:**
- [x] Standard
**Agent Info (please complete the following information):**
- Agent version (as shown in the 'Summary' tab of the agent from web UI): 2.4.2
- Agent OS: Ubuntu 20.04
**Describe the bug**
when i install linux agent app on my linux vps, it connect to RMM panel and show in this panel but after some seconds the vps will disconnect and i cant do anything with RMM on that VPS
how can i get some logs for debug the problem? or what shuld i do for fix the issue?
| 1medium
|
Title: Prediction widget shows no probabilities
Body: **Describe the bug**
The Prediction widget shows no probabilities for the predicted classes.
**To Reproduce**
**Expected behavior**
See probabilities for predicted classes in prediction Widget.
**Orange version:**
3.32
**Screenshots**
**Operating system:**
Windows 11
**Additional context**
| 1medium
|
Title: Wagtail choosers ignore Django's ForeignKey.limit_choices_to
Body: ### Issue Summary
If you set up a callable for a `ForeignKey.limit_choices_to`, It will get called by Django during form construction, but Wagtail's chooser system will never call that function, and thus not limit the choices presented in the chooser. This presumably also affects the other forms of `limit_choices_to` (see [here](https://docs.djangoproject.com/en/5.1/ref/models/fields/#django.db.models.ForeignKey.limit_choices_to)), but a callable is the easiest one to use as a robust example.
### Steps to Reproduce
1. Build a basic bakerydemo, then apply the changes from here: https://github.com/wagtail/bakerydemo/compare/main...coredumperror:bakerydemo:limit_choices_to
2. Open a BreadPage editor (which will cause `"limit_choices_to_bread"` to be printed to the console).
3. Choose a BreadType (which will _not_ cause that print statement to fire).
4. Every bread type is displayed in the chooser, instead of just ones with "bread" in their title.
### Technical details
Python version: 3.12
Django version: 5.1
Wagtail version: 6.4.0
### Working on this
Shouldn't this Django feature be supported by Wagtail's chooser system? It's quite surprising that my project's existing code, which worked fine when I wrote it years ago, has now stopped limiting the available options because Wagtail's new(ish) chooser system apparently just ignores it. | 1medium
|
Title: add benchmark vs mypyc ?
Body: | 1medium
|
Title: Question about code of `vit_for_small_dataset.py`
Body: Hi,
Thanks for the outstanding efforts to bring ViT to the framework of PyTorch, which is meaningful for me to learn about it!
However, when I review the code in `vit_for_small_dataset.py`, in the `SPT`, I find code in the line `86` to be weird.
```python
patch_dim = patch_size * patch_size * 5 * channels
```
I fail to understand the meaning of `5` here. Is it a typo? Based on the context here, it seems that `patch_size * patch_size * channels` is more appropriate.
Thanks for your attention! | 1medium
|
Title: Bug in functional model
Body: I think there seems a bug in functional model.
Case-1: When inputs=outputs for the model construction but training different output shape: Training success
```
import keras
from keras import layers
import numpy as np
input_1 = layers.Input(shape=(3,))
input_2 = layers.Input(shape=(5,))
model_1 = keras.models.Model([input_1, input_2], [input_1, input_2])
print(model_1.summary())
model_1.compile(optimizer='adam',metrics=['accuracy','accuracy'],loss=['mse'])
#Notice I am passing different output size for training but still training happens
model_1.fit([np.random.normal(size=(10,3)),np.random.normal(size=(10,5))],
[np.random.normal(size=(10,1)),np.random.normal(size=(10,2))])
print('Training completed')
```
Case 2: Same as Case-1 but different behavior with different mismatched output shapes(than case-1) for training: Error during loss calculation. But I expect Error during graph execution itself.
```
#With diffrent output shapes than model constructed its raising error while calculating the loss.
#Instead it should have raised shape mismatch error during graph execution.
model_1.fit([np.random.normal(size=(10,3)),np.random.normal(size=(10,5))],
[np.random.normal(size=(10,2)),np.random.normal(size=(10,4))])
```
Case 3: With Unconnected inputs and outputs
```
input_1 = layers.Input(shape=(3,))
input_2 = layers.Input(shape=(5,))
input_3 = layers.Input(shape=(1,))
input_4 = layers.Input(shape=(2,))
model_2 = keras.models.Model([input_1, input_2], [input_3, input_4])
model_2.compile(optimizer='adam',metrics=['accuracy','accuracy'],loss=['mse'])
#Passing correct input and ouputs fails because these are not connected.
model_2.fit([np.random.normal(size=(10,3)),np.random.normal(size=(10,5))], [np.random.normal(size=(10,1)),np.random.normal(size=(10,2))])
```
Got error below which is correct but it is not useful for end users. Instead it should have raised error during graph construction.
```
177 output_tensors = []
178 for x in self.outputs:
--> 179 output_tensors.append(tensor_dict[id(x)])
180
181 return tree.pack_sequence_as(self._outputs_struct, output_tensors)
KeyError: "Exception encountered when calling Functional.call().\n\n\x1b[1m139941182292272\x1b[0m\n\nArguments received by Functional.call():\n • inputs=('tf.Tensor(shape=(None, 3), dtype=float32)', 'tf.Tensor(shape=(None, 5), dtype=float32)')\n • training=True\n • mask=('None', 'None')"
```
I tried to fix an issue similar to case-3 by raising Error during graph build itself in PR #20705 where I noticed this issue related to case1(From failed Test case). Please refer the [gist](https://colab.research.google.com/gist/Surya2k1/88040ebc171b2154627fe54a3560d4b1/functional_model_bug.ipynb).
| 2hard
|
Title: ERNIE英文预训练模型
Body: ERNIE有公开的英文预训练模型吗 | 3misc
|
Title: Adding file.peek()
Body: Hello!
I noticed that in your README.md there is no mention of your aiofiles supporting [peek](https://docs.python.org/3/library/io.html#io.BufferedReader.peek). However in your code there are some tests for it.
When attempting to use peek in my code, it did not seem to work as desired. Could you please add this functionality?
Many Thanks | 2hard
|
Title: TGI on NVIDIA GH200 (Arm64)
Body: ### Feature request
Would it be possible to build/publish an arm64 container image for the text-generation-inference? I would like to be able to run it on a NVIDIA GH200 which is an arm64-based system.
Thanks,
Jonathan
### Motivation
Current images won't run on arm64.
### Your contribution
I've tried to build the image myself, but I haven't been able to get it to build successfully. | 2hard
|
Title: Dataset on Hub re-downloads every time?
Body: ### Describe the bug
Hi, I have a dataset on the hub [here](https://huggingface.co/datasets/manestay/borderlines). It has 1k+ downloads, which I sure is mostly just me and my colleagues working with it. It should have far fewer, since I'm using the same machine with a properly set up HF_HOME variable. However, whenever I run the below function `load_borderlines_hf`, it downloads the entire dataset from the hub and then does the other logic:
https://github.com/manestay/borderlines/blob/4e161f444661e2ebfe643f3fe149d9258d63a57d/run_gpt/lib.py#L80
Let me know what I'm doing wrong here, or if it's a bug with the `datasets` library itself. On the hub I have my data stored in CSVs, but several columns are lists, so that's why I have the code to map splitting on `;`. I looked into dataset loading scripts, but it seemed difficult to set up. I have verified that other `datasets` and `models` on my system are using the cache properly (e.g. I have a 13B parameter model and large datasets, but those are cached and don't redownload).
__EDIT: __ as pointed out in the discussion below, it may be the `map()` calls that aren't being cached properly. Supposing the `load_dataset()` retrieve from the cache, then it should be the case that the `map()` calls also retrieve from the cached output. But the `map()` commands re-execute sometimes.
### Steps to reproduce the bug
1. Copy and paste the function from [here](https://github.com/manestay/borderlines/blob/4e161f444661e2ebfe643f3fe149d9258d63a57d/run_gpt/lib.py#L80) (lines 80-100)
2. Run it in Python `load_borderlines_hf(None)`
3. It completes successfully, downloading from HF hub, then doing the mapping logic etc.
4. If you run it again after some time, it will re-download, ignoring the cache
### Expected behavior
Re-running the code, which calls `datasets.load_dataset('manestay/borderlines', 'territories')`, should use the cached version
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-5.14.21-150500.55.7-default-x86_64-with-glibc2.31
- Python version: 3.10.13
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 1.5.3
- `fsspec` version: 2023.10.0 | 1medium
|
Title: OSError: When resolving region for bucket 'ursa-labs-taxi-data': AWS Error [code 99]: curlCode: 35, SSL connect error
Body: ### Describe the bug
When trying to read data from the example bucket (and any other bucket) the error "OSError: When resolving region for bucket 'ursa-labs-taxi-data': AWS Error [code 99]: curlCode: 35, SSL connect error" is displayed although aws environment variables are set and configured as well as the proxy
### How to Reproduce
```
import awswranger as wr
df = wr.s3.read_parquet(path="s3://ursa-labs-taxi-data/2017/")
```
### Expected behavior
Read data from s3 into the dataframe
### Your project
_No response_
### Screenshots
_No response_
### OS
Red Hat Enterprise Linux release 8.8 (Ootpa)
### Python version
3.8.16
### AWS SDK for pandas version
3.4.0
### Additional context
Through the aws cli services are reachable | 1medium
|
Title: pmtiles tooltip + layers control is broken with folium > 0.14.0
Body: <!-- Please search existing issues to avoid creating duplicates. -->
### Environment Information
- leafmap version: 0.29.5
- Python version: 3.9
- Operating System: Windows/Linux
### Description
When PMTilesLayer tooltip is enabled, it leads to console error and prevents layers control from loading. Layers control is able to load when tooltip=False
This can be seen on the live site as well: https://leafmap.org/notebooks/82_pmtiles/
### What I Did
Followed the example in 82_pmtiles.ipynb and found out that it works properly on one notebook env but not the other. Realized that layers control is missing when I tried to add other layers to the map.
Error in browser console suggests that it may be related to folium/jinja templates:
```
// below line is not present when using folium 0.14.0
macro_element_51d29068fa44734e98b3aa01b7f1db45.addTo(pm_tiles_vector_ff278fae2ca890eb72792f1dc892823e);
```
So the current workaround is to downgrade to folium==0.14.0
folium-pmtiles is affected as well (using this example: https://github.com/jtmiclat/folium-pmtiles/blob/master/example/pmtiles_vector_maplibre.ipynb)
Confirmed to be related to this change in folium: https://github.com/python-visualization/folium/pull/1690#issuecomment-1377180410 | 1medium
|
Title: Replace ApolloTracing with simple tracing extension
Body: Instead of `ApolloTracing` (which is deprecated) we could implement a simple extension that adds `extensions: {traces: []}` object where each trace would be a separate `{path: time: }`.
I don't know how useful this would be to folk, but maybe it would have some utility as simple example extension? | 1medium
|
Title: bug：main、dev code run main.py has error：lzma.LZMAError: Corrupt input data
Body: I use the latest code, in main,dev；i find use api in api/main.py, the web result is error, no voice and server has：lzma.LZMAError: Corrupt input data
1.i try check model,code and pip package,it's run,but generate has no voice and is noise
[audio_files (19).zip](https://github.com/user-attachments/files/17505105/audio_files.19.zip)
2. i set use_vllm, it's work, but generate has noise and other voice,like this:
[audio_files (20).zip](https://github.com/user-attachments/files/17505111/audio_files.20.zip)
| 1medium
|
Title: CUDA
Body: ### Your question
CUDA error: no kernel image is available for execution on the device CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1 Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
### Logs
```powershell
```
### Other
_No response_ | 2hard
|
Title: get
Body: ## 🐛 Bug Description
<!-- A clear and concise description of what the bug is. -->
## To Reproduce
Steps to reproduce the behavior:
1.
1.
1.
## Expected Behavior
<!-- A clear and concise description of what you expected to happen. -->
## Screenshot
<!-- A screenshot of the error message or anything shouldn't appear-->
## Environment
**Note**: User could run `cd scripts && python collect_info.py all` under project directory to get system information
and paste them here directly.
- Qlib version:
- Python version:
- OS (`Windows`, `Linux`, `MacOS`):
- Commit number (optional, please provide it if you are using the dev version):
## Additional Notes
<!-- Add any other information about the problem here. -->
| 1medium
|
Title: Questions about using the known exogenous variables to conduct forecasted values
Body: Hi developers,
I have a little confusion about **using the known exogenous variables** to conduct forecasted values.
Firstly, I use the **multi-series function to "predict"** all the exogenous variables and the target output Y simultaneously. After that, I would like to use the 'predicted values' of the exogenous variables to conduct the direct/recursive forecasting for the target output Y, and I refer the related documents such as the example of [weather exogenous](https://www.cienciadedatos.net/documentos/py39-forecasting-time-series-with-skforecast-xgboost-lightgbm-catboost.html) variables.
However, I have the confusion about dealing with the known future values in an appropriate place because they are the future known values and the format are "not consistent" with the known training data. How can I "combine" them together during using the skforecast framework?
| 1medium
|
Title: a way to make stream-bytes produce multiple http chunks
Body: Currently (unless I'm missing something) the chunk_size parameter on stream-bytes controls the block size used to write the data to the socket. But the entire data is still sent as one HTTP chunk i.e. there is only one total size followed by the data.
Would it be reasonable to have chunk_size control the size of the HTTP chunks? Or if not, maybe another parameter to control that?
I don't know Python so I'm not sure how feasible that is.
Thanks for a great tool.
| 1medium
|
Title: Keras should support bitwise ops
Body: Numpy has bitwise ops https://numpy.org/doc/stable/reference/routines.bitwise.html
TensorFlow has bitwise ops https://www.tensorflow.org/api_docs/python/tf/bitwise
Jax has bitwise ops https://jax.readthedocs.io/en/latest/_autosummary/jax.numpy.bitwise_and.html
PyTorch has bitwise ops https://pytorch.org/docs/stable/generated/torch.bitwise_and.html
So it seems natural for Keras to support bitwise ops as well. | 1medium
|
Title: 登陆报错：Remote end closed connection without response
Body: TypeError: getresponse() got an unexpected keyword argument 'buffering'
http.client.RemoteDisconnected: Remote end closed connection without response
requests.packages.urllib3.exceptions.ProtocolError: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response',))
requests.exceptions.ConnectionError: ('Connection aborted.', RemoteDisconnected('Remote end closed connection without response',))
| 1medium
|
Title: [Bug/Feature]: long-short.py - Qty should be calculated relative to price of stock
Body: ### Is there an existing issue for this?
- [X] I have searched the existing issues
### Current Behavior
When opening new positions, the long-short.py opens all the positioning with approximately the same qty in terms of shares (ie: with 100k capital, each position is 160 shares). This does not make sense, the position should be calculated based on the price of the stock.
For example, the code tries to open a long position for AMZN for 160 shares (2.880$/each) and this goes way above the available liquidity. Likewise, it opens a position of 160 shares for GM (44.85$/each).
I've tried to fix the code but I'm not sure about all the references, so it'd be cool if someone from Alpaca could fix this.
### Expected Behavior
_No response_
### Steps To Reproduce
_No response_
### Anything else?
_No response_ | 1medium
|
Title: by_sound method failing
Body: **Describe the bug**
by_sound method returns an error
**The buggy code**
```
TikTokApi.get_instance(proxy=sys.argv[3]).by_sound(sys.argv[1], int(sys.argv[2]))
```
**Expected behavior**
by_sound method returns an error
**Error Trace (if any)**
```
raise EmptyResponseError(\nTikTokApi.exceptions.EmptyResponseError: Empty response from Tiktok to https://m.tiktok.com/api/music/item_list/?aid=1988&app_name=tiktok_web&device_platform=web&referer=&root_referer=&user_agent=Mozilla%252F5.0%2B%28iPhone%253B%2BCPU%2BiPhone%2BOS%2B12_2%2Blike%2BMac%2BOS%2BX%29%2BAppleWebKit%252F605.1.15%2B%28KHTML%2C%2Blike%2BGecko%29%2BVersion%252F13.0%2BMobile%252F15E148%2BSafari%252F604.1&cookie_enabled=true&screen_width=1858&screen_height=1430&browser_language=&browser_platform=&browser_name=&browser_version=&browser_online=true&ac=4g&timezone_name=&appId=1233&appType=m&isAndroid=False&isMobile=False&isIOS=False&OS=windows&secUid=&musicID=6763054442704145158&count=35&cursor=0&shareUid=&language=en&verifyFp=verify_khr3jabg_V7ucdslq_Vrw9_4KPb_AJ1b_Ks706M8zIJTq&did=4792150057109208114&_signature=_02B4Z6wo00f01X4-6mwAAIBDLD1T3YFqwoV-P-7AAD.80b\n
```
**Desktop (please complete the following information):**
- OS: MacOS
- TikTokApi Version 3.9.5
| 1medium
|
Title: [Overkiz] - Unsupported value CyclicSwingingGateOpener
Body: ### The problem
I have in my Tahoma an association with Netatmo station and legrand drivia switch (https://www.legrand.fr/pro/catalogue/pack-de-demarrage-drivia-with-netatmo-pour-installation-connectee-1-module-control-1-contacteur-connecte).
In the system log, I see an error related my Netatmo on Overkiz.
### What version of Home Assistant Core has the issue?
core-2025.3.3
### 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
Overkiz
### Link to integration documentation on our website
_No response_
### Diagnostics information
Enregistreur: pyoverkiz.enums.ui
Source: components/overkiz/__init__.py:87
S'est produit pour la première fois: 14 mars 2025 à 19:53:28 (3 occurrences)
Dernier enregistrement: 14 mars 2025 à 19:53:28
Unsupported value CyclicSwingingGateOpener has been returned for <enum 'UIWidget'>
Unsupported value NetatmoGateway has been returned for <enum 'UIWidget'>
### Example YAML snippet
```yaml
```
### Anything in the logs that might be useful for us?
```txt
```
### Additional information
_No response_ | 1medium
|
Title: Error solving the challenge.
Body: ### Have you checked our README?
- [X] I have checked the README
### Have you followed our Troubleshooting?
- [X] I have followed your Troubleshooting
### Is there already an issue for your problem?
- [X] I have checked older issues, open and closed
### Have you checked the discussions?
- [X] I have read the Discussions
### Environment
```markdown
- FlareSolverr version:3.3.21
- Last working FlareSolverr version:none
- Operating system:win10
- Are you using Docker: [yes/no]no
- FlareSolverr User-Agent (see log traces or / endpoint)Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/123.0.0.0 Safari/537.36
- Are you using a VPN: [yes/no]no
- Are you using a Proxy: [yes/no]no
- Are you using Captcha Solver: [yes/no]no
- If using captcha solver, which one:no
- URL to test this issue:https://unitedshop.ws/
```
### Description
check pics for more info
### Logged Error Messages
```text
500 internal server error
(Failed to recieve the response from the HTTP-server 'localhost'.)in silverbullet
```
### Screenshots
| 1medium
|
Title: Encode the video output in HEVC instead h264 ?
Body: Hello there , how i can make RVM encode the video output in HEVC instead h264 ? And is it possible to get the sound from the original vid too ? Thank you in advance ! | 1medium
|
Title: make nlp work for individual dictionaries
Body: right now it only works with dataframes.
we'll probably want to move this whole block into fit:
```
col_names = self.text_columns[key].get_feature_names()
# Make weird characters play nice, or just ignore them :)
for idx, word in enumerate(col_names):
try:
col_names[idx] = str(word)
except:
col_names[idx] = 'non_ascii_word_' + str(idx)
col_names = ['nlp_' + key + '_' + str(word) for word in col_names]
``` | 1medium
|
Title: nltk.lm.api entropy formula source?
Body: Hi! I've been experimenting with training and testing a standard trigram language model on my own dataset. Upon investigating the `entropy` method of the LM class, I was a bit confused. The [docs](https://www.nltk.org/api/nltk.lm.api.html#nltk.lm.api.LanguageModel.entropy) only mention a very brief description: "Calculate cross-entropy of model for given evaluation text."
It seems to me that this entropy measure just **averages the ngram negative log probability** (so trigram in my case) and **makes it positive** by multiplying by -1:
```python
def _mean(items):
"""Return average (aka mean) for sequence of items."""
return sum(items) / len(items)
def entropy(self, text_ngrams):
"""Calculate cross-entropy of model for given evaluation text.
:param Iterable(tuple(str)) text_ngrams: A sequence of ngram tuples.
:rtype: float
"""
return -1 * _mean(
[self.logscore(ngram[-1], ngram[:-1]) for ngram in text_ngrams]
)
```
**So my general question is: Where does this formula for entropy stem from? Is there any paper referencing the method?** I'm just a bit stuck with the different versions of entropy that exist and I don't know which one is used here (and therefore I don't know how to interpret it correctly).
### Other formulas of entropy/perplexity
As a formula I know the [Shannon entropy](https://en.wikipedia.org/wiki/Entropy_(information_theory)) which in Python would be:
```python
def shannon_entropy(self, text_ngrams):
return -1 * sum(
[self.score(ngram[-1], ngram[:-1]) * self.logscore(ngram[-1], ngram[:-1]) for ngram in text_ngrams]
)
```
And there's also the perplexity formula of [Jurafsky](https://www.youtube.com/watch?v=NCyCkgMLRiY), which returns a different score than `lm.perplexity` (which is 2**entropy):
```python
from numpy import prod
def jurafsky_perplexity(self, text_ngrams):
problist = [self.score(ngram[-1], ngram[:-1]) for ngram in text_ngrams]
return pow(prod(problist), -1/len(problist)
```
Thanks!
PS: My apologies if this issue lacks some information - I don't often open issues on Github 😅 Let me know if I need to update something!
UPDATE: my Python implementation of the Jurafsky perplexity was totally wrong as I had quickly written it. I've updated it to reflect the actual scores from the web lecture. | 1medium
|
Title: Database encryption
Body: Add encryption at rest to the database so the server's owner can't access user's data. Account password should be the master key to decrypt the database. | 2hard
|
Title: Docker compose stuck on waiting for my sql
Body: I cloned the repo and did docker-compose build. It ran successfully. I then did docker-compose up. This is stuck on Waiting for mysql+pymysql...
```
docker-compose up
Starting ctfd_db_1 ... done
Starting ctfd_cache_1 ... done
Starting ctfd_ctfd_1 ... done
Starting ctfd_nginx_1 ... done
Attaching to ctfd_cache_1, ctfd_db_1, ctfd_ctfd_1, ctfd_nginx_1
db_1 | 2021-07-24 17:57:00+00:00 [Note] [Entrypoint]: Entrypoint script for MySQL Server 1:10.4.12+maria~bionic started.
cache_1 | 1:C 24 Jul 17:56:59.849 # oO0OoO0OoO0Oo Redis is starting oO0OoO0OoO0Oo
cache_1 | 1:C 24 Jul 17:56:59.849 # Redis version=4.0.14, bits=64, commit=00000000, modified=0, pid=1, just started
cache_1 | 1:C 24 Jul 17:56:59.849 # Warning: no config file specified, using the default config. In order to specify a config file use redis-server /path/to/redis.conf
db_1 | 2021-07-24 17:57:01+00:00 [Note] [Entrypoint]: Switching to dedicated user 'mysql'
cache_1 | 1:M 24 Jul 17:56:59.903 * Running mode=standalone, port=6379.
cache_1 | 1:M 24 Jul 17:56:59.903 # Server initialized
cache_1 | 1:M 24 Jul 17:56:59.903 # WARNING overcommit_memory is set to 0! Background save may fail under low memory condition. To fix this issue add 'vm.overcommit_memory = 1' to /etc/sysctl.conf and then reboot or run the command 'sysctl vm.overcommit_memory=1' for this to take effect.
cache_1 | 1:M 24 Jul 17:56:59.903 # WARNING you have Transparent Huge Pages (THP) support enabled in your kernel. This will create latency and memory usage issues with Redis. To fix this issue run the command 'echo never > /sys/kernel/mm/transparent_hugepage/enabled' as root, and add it to your /etc/rc.local in order to retain the setting after a reboot. Redis must be restarted after THP is disabled.
db_1 | 2021-07-24 17:57:01+00:00 [Note] [Entrypoint]: Entrypoint script for MySQL Server 1:10.4.12+maria~bionic started.
cache_1 | 1:M 24 Jul 17:56:59.903 * DB loaded from disk: 0.000 seconds
cache_1 | 1:M 24 Jul 17:56:59.903 * Ready to accept connections
db_1 | 2021-07-24 17:57:02 0 [Note] mysqld (mysqld 10.4.12-MariaDB-1:10.4.12+maria~bionic) starting as process 1 ...
db_1 | 2021-07-24 17:57:02 0 [Note] InnoDB: Using Linux native AIO
db_1 | 2021-07-24 17:57:02 0 [Note] InnoDB: Mutexes and rw_locks use GCC atomic builtins
db_1 | 2021-07-24 17:57:02 0 [Note] InnoDB: Uses event mutexes
db_1 | 2021-07-24 17:57:02 0 [Note] InnoDB: Compressed tables use zlib 1.2.11
db_1 | 2021-07-24 17:57:02 0 [Note] InnoDB: Number of pools: 1
db_1 | 2021-07-24 17:57:02 0 [Note] InnoDB: Using SSE2 crc32 instructions
db_1 | 2021-07-24 17:57:02 0 [Note] mysqld: O_TMPFILE is not supported on /tmp (disabling future attempts)
db_1 | 2021-07-24 17:57:02 0 [Note] InnoDB: Initializing buffer pool, total size = 256M, instances = 1, chunk size = 128M
db_1 | 2021-07-24 17:57:02 0 [Note] InnoDB: Completed initialization of buffer pool
db_1 | 2021-07-24 17:57:02 0 [Note] InnoDB: If the mysqld execution user is authorized, page cleaner thread priority can be changed. See the man page of setpriority().
db_1 | 2021-07-24 17:57:03 0 [Note] InnoDB: 128 out of 128 rollback segments are active.
db_1 | 2021-07-24 17:57:03 0 [Note] InnoDB: Creating shared tablespace for temporary tables
db_1 | 2021-07-24 17:57:03 0 [Note] InnoDB: Setting file './ibtmp1' size to 12 MB. Physically writing the file full; Please wait ...
db_1 | 2021-07-24 17:57:03 0 [Note] InnoDB: File './ibtmp1' size is now 12 MB.
db_1 | 2021-07-24 17:57:03 0 [Note] InnoDB: Waiting for purge to start
db_1 | 2021-07-24 17:57:03 0 [Note] InnoDB: 10.4.12 started; log sequence number 61164; transaction id 21
db_1 | 2021-07-24 17:57:03 0 [Note] InnoDB: Loading buffer pool(s) from /var/lib/mysql/ib_buffer_pool
db_1 | 2021-07-24 17:57:04 0 [Note] Server socket created on IP: '::'.
db_1 | 2021-07-24 17:57:04 0 [Warning] 'user' entry 'root@c6e0dcba624f' ignored in --skip-name-resolve mode.
db_1 | 2021-07-24 17:57:04 0 [Warning] 'user' entry '@c6e0dcba624f' ignored in --skip-name-resolve mode.
db_1 | 2021-07-24 17:57:04 0 [Warning] 'proxies_priv' entry '@% root@c6e0dcba624f' ignored in --skip-name-resolve mode.
db_1 | 2021-07-24 17:57:04 0 [Note] InnoDB: Buffer pool(s) load completed at 210724 17:57:04
db_1 | 2021-07-24 17:57:04 0 [Note] mysqld: ready for connections.
db_1 | Version: '10.4.12-MariaDB-1:10.4.12+maria~bionic' socket: '/var/run/mysqld/mysqld.sock' port: 3306 mariadb.org binary distribution
ctfd_1 | Waiting for mysql+pymysql://ctfd:ctfd@db to be ready
``` | 1medium
|
Title: [Feature request] Implementation for FreeVC
Body: <!-- Welcome to the 🐸TTS project!
We are excited to see your interest and appreciate your support! --->
**🚀 Feature Description**
Original implementation: https://github.com/OlaWod/FreeVC
We can use it in combination with any 🐸TTS model and make it speak with any voice aka faking voice cloning.
<!--A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] -->
**Additional context**
I'll initially implement only inference with the pre-trained models and later we can implement the training. It should be easy as it a quite the same as the VITS.
<!-- Add any other context or screenshots about the feature request here. -->
| 1medium
|
Title: Why we don't use certifi root certificates by default?
Body: Hey there,
Today my colleauge step into the problem with ceritificate that time to time appears with other people who use your client.
You can read about this problem more here:
https://stackoverflow.com/questions/59808346/python-3-slack-client-ssl-sslcertverificationerror
As I see the problem that people have some problem with root certificates on their machine.
Python always had a problem with root certificatates and to solve this problem people started to use the project certifi.
For example you can find usage of this project in the kinda popular library `requests`
https://github.com/psf/requests/blob/main/src/requests/utils.py#L63
https://github.com/psf/requests/blob/main/src/requests/adapters.py#L294
Do you have any reason to don't use library certifi as requests library uses?
I think it would simplify of using your library everywhere.
| 1medium
|
Title: Sliding window (mix of rolling and coarsen)
Body: ### Is your feature request related to a problem?
I often have functions to apply to sliding windows, such as a fft computation, but both coarsen and rolling do not fit. For example, generating a spectrogram with overlap between windows is complicated using xarray. Of course, one could use [scipy STFT](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.ShortTimeFFT.html) if we consider the example of the spectrogram, but one still needs to manage the coordinates manually.
### Describe the solution you'd like
A new window function that combines rolling and coarsen. The new window would have the current dim parameter of rolling and coarsen split into two:
- window_size: the size of the window (equivalent to the current value of the dim parameter)
- hop: how much do we shift in between each window (see the hop parameter of [scipy STFT](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.ShortTimeFFT.html) for example).
- perhaps we could add a window function to apply, but this is not necessary as this can be done without.
This unifies rolling and coarsen as rolling is simply hop=1 and coarsen is hop=window_size.
As for the implementation of this datastructure, I suspect we could use [as_strided](https://numpy.org/devdocs/reference/generated/numpy.lib.stride_tricks.as_strided.html) for very efficient implementation of construct.
### Describe alternatives you've considered
- Using rolling + sel to only pick the windows with the correct hop, but this seems extremely inefficient
- Handling the coordinates manually...
### Additional context
_No response_ | 1medium
|
Title: Can't connect to mongo docker
Body: okey, spend a day, but still can't make this work
> .env
```
# Mongo DB
MONGO_INITDB_ROOT_USERNAME=admin-user
MONGO_INITDB_ROOT_PASSWORD=admin-password
MONGO_INITDB_DATABASE=container
```
> docker-compose.yml
```
mongo-db:
image: mongo:4.2.3
env_file:
- .env
ports:
- 27017:27107
volumes:
- ./bin/mongo-init.js:/docker-entrypoint-initdb.d/mongo-init.js:ro
# restart: always add this production
api:
build:
context: ./backend
dockerfile: Dockerfile
command: uvicorn app.main:app --host 0.0.0.0 --port 8006 --reload
volumes:
- ./backend:/app
env_file:
- .env
depends_on:
- mongo-db
ports:
- "8006:8006"
```
> mongo-init.js
```
db.auth('admin-user', 'admin-password')
db = db.getSiblingDB('container')
db.createUser({
user: 'test-user',
pwd: 'test-password',
roles: [
{
role: 'root',
db: 'admin',
},
],
});
```
using this as example - https://frankie567.github.io/fastapi-users/configuration/full_example/
changed few lines:
`DATABASE_URL = "mongodb://test-user:test-password@mongo-db/container"`
```
client = motor.motor_asyncio.AsyncIOMotorClient(DATABASE_URL)
db = client["container"]
collection = db["users"]
user_db = MongoDBUserDatabase(UserDB, collection)
```
so think Fatapi is linked to Mongo container
but when i try to register user using Fastapi docks section, i get
"Internal server error"
what I left unfinished?
| 1medium
|
Title: PROVIDE_AUTOMATIC_OPTIONS causes KeyError if not set
Body: https://github.com/pallets/flask/blob/bc098406af9537aacc436cb2ea777fbc9ff4c5aa/src/flask/sansio/app.py#L641C12-L641C86
Simply changing this to : `self.config.get("PROVIDE_AUTOMATIC_OPTIONS", False)` should resolve the problem.
This change now released is causing upstream trouble in other packages such as Quart:
https://github.com/pallets/quart/issues/371
| 0easy
|
Title: `remove_columns` method used with a streaming enable dataset mode produces a LibsndfileError on multichannel audio
Body: ### Describe the bug
When loading a HF dataset in streaming mode and removing some columns, it is impossible to load a sample if the audio contains more than one channel. I have the impression that the time axis and channels are swapped or concatenated.
### Steps to reproduce the bug
Minimal error code:
```python
from datasets import load_dataset
dataset_name = "zinc75/Vibravox_dummy"
config_name = "BWE_Larynx_microphone"
# if we use "ASR_Larynx_microphone" subset which is a monochannel audio, no error is thrown.
dataset = load_dataset(
path=dataset_name, name=config_name, split="train", streaming=True
)
dataset = dataset.remove_columns(["sensor_id"])
# dataset = dataset.map(lambda x:x, remove_columns=["sensor_id"])
# The commented version does not produce an error, but loses the dataset features.
sample = next(iter(dataset))
```
Error:
```
Traceback (most recent call last):
File "/home/julien/Bureau/github/vibravox/tmp.py", line 15, in <module>
sample = next(iter(dataset))
^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1392, in __iter__
example = _apply_feature_types_on_example(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/iterable_dataset.py", line 1080, in _apply_feature_types_on_example
encoded_example = features.encode_example(example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/features.py", line 1889, in encode_example
return encode_nested_example(self, example)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/features.py", line 1244, in encode_nested_example
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/features.py", line 1244, in <dictcomp>
{k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/features.py", line 1300, in encode_nested_example
return schema.encode_example(obj) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/datasets/features/audio.py", line 98, in encode_example
sf.write(buffer, value["array"], value["sampling_rate"], format="wav")
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/soundfile.py", line 343, in write
with SoundFile(file, 'w', samplerate, channels,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/soundfile.py", line 658, in __init__
self._file = self._open(file, mode_int, closefd)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/julien/.pyenv/versions/vibravox/lib/python3.11/site-packages/soundfile.py", line 1216, in _open
raise LibsndfileError(err, prefix="Error opening {0!r}: ".format(self.name))
soundfile.LibsndfileError: Error opening <_io.BytesIO object at 0x7fd795d24680>: Format not recognised.
Process finished with exit code 1
```
### Expected behavior
I would expect this code to run without error.
### Environment info
- `datasets` version: 2.18.0
- Platform: Linux-6.5.0-21-generic-x86_64-with-glibc2.35
- Python version: 3.11.0
- `huggingface_hub` version: 0.21.3
- PyArrow version: 15.0.0
- Pandas version: 2.2.1
- `fsspec` version: 2023.10.0 | 1medium
|
Title: Distributed Training with 4 machines on Translation Task
Body: Hello , I want to do distributed training using four machines ,each one has 8 1080ti GPUs on En-Zh translation task, and the t2t-version is 1.6.5. I have seen the other similar issues , and
the distributed_training.md ,but I still have some confusion. If the four machines named M1, M2 ,M3 ,M4 , the first step is to make the TF-Config. What I want is to make 4 machines to train together , thus I am not sure how to define the master and ps. And the second question is if four machines can do synchronous training ?
Hope someone can give me some advice, thanks | 2hard
|
Title: Can not install Flask-Restx in ARM
Body: ## Description:
I am trying to create a docker image in a RaspberryPi which runs `aarch64`, but when the Docker image starts installing the Flask-Restx package, it fails.
!!**This was working correctly 4 months ago because that was the day of my last build.**
## **Environment**
RaspberryPi 4
```commandline
$ uname -a
Linux RPi 5.10.10-1-ARCH #1 SMP Sat Jan 23 16:26:04 MST 2021 aarch64 GNU/Linux
$ docker --version
Docker version 20.10.2, build 2291f610ae
```
## **requirements.txt**
```text
flask-restx==0.4.0
```
## **Dockerfile**
```dockerfile
# Build
FROM python:3.9 as build
COPY requirements.txt /
WORKDIR /pip-packages
RUN pip download -r /requirements.txt --no-input
# Prod
FROM python:3.9
COPY --from=build /pip-packages/ /pip-packages/
RUN pip install --no-index --find-links=/pip-packages/ /pip-packages/*
RUN pip list
```
## **Expected Behavior**
Docker image can be built correctly.
## **Actual Behavior**
Docker image fails when building.
## **Error Messages/Stack Trace**
### **Message Error**
```text
ERROR: Command errored out with exit status 1:
command: /usr/local/bin/python /usr/local/lib/python3.9/site-packages/pip install --ignore-installed --no-user --prefix /tmp/pip-build-env-qf_d47c3/overlay --no-warn-script-location --no-binary :none: --only-binary :none: --no-index --find-links /pip-packages/ -- 'setuptools>=42' wheel
cwd: None
Complete output (4 lines):
Looking in links: /pip-packages/
Processing /pip-packages/setuptools-57.2.0-py3-none-any.whl
ERROR: Could not find a version that satisfies the requirement wheel
ERROR: No matching distribution found for wheel
----------------------------------------
WARNING: Discarding file:///pip-packages/pyrsistent-0.18.0.tar.gz. Command errored out with exit status 1: /usr/local/bin/python /usr/local/lib/python3.9/site-packages/pip install --ignore-installed --no-user --prefix /tmp/pip-build-env-qf_d47c3/overlay --no-warn-script-location --no-binary :none: --only-binary :none: --no-index --find-links /pip-packages/ -- 'setuptools>=42' wheel Check the logs for full command output.
ERROR: Command errored out with exit status 1: /usr/local/bin/python /usr/local/lib/python3.9/site-packages/pip install --ignore-installed --no-user --prefix /tmp/pip-build-env-qf_d47c3/overlay --no-warn-script-location --no-binary :none: --only-binary :none: --no-index --find-links /pip-packages/ -- 'setuptools>=42' wheel Check the logs for full command output.
The command '/bin/sh -c pip install --no-index --find-links=/pip-packages/ /pip-packages/*' returned a non-zero code: 1
```
### Docker build output
```commandLine
$ docker build --tag docker-arm-poc-3:latest --no-cache .
```
```text
Sending build context to Docker daemon 5.12kB
Step 1/8 : FROM python:3.9 as build
---> 8ff7a2865c18
Step 2/8 : COPY requirements.txt /
---> 1f6972b1b83d
Step 3/8 : WORKDIR /pip-packages
---> Running in 4d614d4db71b
Removing intermediate container 4d614d4db71b
---> 45d2797d6000
Step 4/8 : RUN pip download -r /requirements.txt --no-input
---> Running in 53c81d589f32
Collecting flask-restx==0.4.0
Downloading flask_restx-0.4.0-py2.py3-none-any.whl (5.3 MB)
Collecting six>=1.3.0
Downloading six-1.16.0-py2.py3-none-any.whl (11 kB)
Collecting Flask<2.0.0,>=0.8
Downloading Flask-1.1.4-py2.py3-none-any.whl (94 kB)
Collecting werkzeug<2.0.0
Downloading Werkzeug-1.0.1-py2.py3-none-any.whl (298 kB)
Collecting jsonschema
Downloading jsonschema-3.2.0-py2.py3-none-any.whl (56 kB)
Collecting pytz
Downloading pytz-2021.1-py2.py3-none-any.whl (510 kB)
Collecting aniso8601>=0.82
Downloading aniso8601-9.0.1-py2.py3-none-any.whl (52 kB)
Collecting itsdangerous<2.0,>=0.24
Downloading itsdangerous-1.1.0-py2.py3-none-any.whl (16 kB)
Collecting Jinja2<3.0,>=2.10.1
Downloading Jinja2-2.11.3-py2.py3-none-any.whl (125 kB)
Collecting click<8.0,>=5.1
Downloading click-7.1.2-py2.py3-none-any.whl (82 kB)
Collecting MarkupSafe>=0.23
Downloading MarkupSafe-2.0.1-cp39-cp39-manylinux2014_aarch64.whl (31 kB)
Collecting setuptools
Downloading setuptools-57.2.0-py3-none-any.whl (818 kB)
Collecting pyrsistent>=0.14.0
Downloading pyrsistent-0.18.0.tar.gz (104 kB)
Installing build dependencies: started
Installing build dependencies: finished with status 'done'
Getting requirements to build wheel: started
Getting requirements to build wheel: finished with status 'done'
Preparing wheel metadata: started
Preparing wheel metadata: finished with status 'done'
Collecting attrs>=17.4.0
Downloading attrs-21.2.0-py2.py3-none-any.whl (53 kB)
Saved ./flask_restx-0.4.0-py2.py3-none-any.whl
Saved ./aniso8601-9.0.1-py2.py3-none-any.whl
Saved ./Flask-1.1.4-py2.py3-none-any.whl
Saved ./click-7.1.2-py2.py3-none-any.whl
Saved ./itsdangerous-1.1.0-py2.py3-none-any.whl
Saved ./Jinja2-2.11.3-py2.py3-none-any.whl
Saved ./MarkupSafe-2.0.1-cp39-cp39-manylinux2014_aarch64.whl
Saved ./six-1.16.0-py2.py3-none-any.whl
Saved ./Werkzeug-1.0.1-py2.py3-none-any.whl
Saved ./jsonschema-3.2.0-py2.py3-none-any.whl
Saved ./attrs-21.2.0-py2.py3-none-any.whl
Saved ./pyrsistent-0.18.0.tar.gz
Saved ./pytz-2021.1-py2.py3-none-any.whl
Saved ./setuptools-57.2.0-py3-none-any.whl
Successfully downloaded flask-restx aniso8601 Flask click itsdangerous Jinja2 MarkupSafe six werkzeug jsonschema attrs pyrsistent pytz setuptools
WARNING: You are using pip version 21.0.1; however, version 21.1.3 is available.
You should consider upgrading via the '/usr/local/bin/python -m pip install --upgrade pip' command.
Removing intermediate container 53c81d589f32
---> 1467b30d43ef
Step 5/8 : FROM python:3.9
---> 8ff7a2865c18
Step 6/8 : COPY --from=build /pip-packages/ /pip-packages/
---> 89c8bfc9a430
Step 7/8 : RUN pip install --no-index --find-links=/pip-packages/ /pip-packages/*
---> Running in a10bdd1551cb
Looking in links: /pip-packages/
Processing /pip-packages/Flask-1.1.4-py2.py3-none-any.whl
Processing /pip-packages/Jinja2-2.11.3-py2.py3-none-any.whl
Processing /pip-packages/MarkupSafe-2.0.1-cp39-cp39-manylinux2014_aarch64.whl
Processing /pip-packages/Werkzeug-1.0.1-py2.py3-none-any.whl
Processing /pip-packages/aniso8601-9.0.1-py2.py3-none-any.whl
Processing /pip-packages/attrs-21.2.0-py2.py3-none-any.whl
Processing /pip-packages/click-7.1.2-py2.py3-none-any.whl
Processing /pip-packages/flask_restx-0.4.0-py2.py3-none-any.whl
Processing /pip-packages/itsdangerous-1.1.0-py2.py3-none-any.whl
Processing /pip-packages/jsonschema-3.2.0-py2.py3-none-any.whl
Processing /pip-packages/pyrsistent-0.18.0.tar.gz
Installing build dependencies: started
Installing build dependencies: finished with status 'error'
ERROR: Command errored out with exit status 1:
command: /usr/local/bin/python /usr/local/lib/python3.9/site-packages/pip install --ignore-installed --no-user --prefix /tmp/pip-build-env-qf_d47c3/overlay --no-warn-script-location --no-binary :none: --only-binary :none: --no-index --find-links /pip-packages/ -- 'setuptools>=42' wheel
cwd: None
Complete output (4 lines):
Looking in links: /pip-packages/
Processing /pip-packages/setuptools-57.2.0-py3-none-any.whl
ERROR: Could not find a version that satisfies the requirement wheel
ERROR: No matching distribution found for wheel
----------------------------------------
WARNING: Discarding file:///pip-packages/pyrsistent-0.18.0.tar.gz. Command errored out with exit status 1: /usr/local/bin/python /usr/local/lib/python3.9/site-packages/pip install --ignore-installed --no-user --prefix /tmp/pip-build-env-qf_d47c3/overlay --no-warn-script-location --no-binary :none: --only-binary :none: --no-index --find-links /pip-packages/ -- 'setuptools>=42' wheel Check the logs for full command output.
ERROR: Command errored out with exit status 1: /usr/local/bin/python /usr/local/lib/python3.9/site-packages/pip install --ignore-installed --no-user --prefix /tmp/pip-build-env-qf_d47c3/overlay --no-warn-script-location --no-binary :none: --only-binary :none: --no-index --find-links /pip-packages/ -- 'setuptools>=42' wheel Check the logs for full command output.
The command '/bin/sh -c pip install --no-index --find-links=/pip-packages/ /pip-packages/*' returned a non-zero code: 1
``` | 1medium
|
Title: How to uninstall it?
Body: I'm trying to test this package but not good for my project.
And this package needs comercial license, so I want to remove it all.
How can I uninstall it?
| 0easy
|
Title: [🐛 BUG] Lambda expression not working with new line
Body: ### What went wrong? 🤔
Lambda expressions are not working for no reason.
The second selector is not working.
### Expected Behavior
It should work.
### Steps to Reproduce Issue
```python
from taipy.gui import Gui
import taipy.gui.builder as tgb
value: int = 10
value_selector = 10
def get_list(value):
return [str(i) for i in range(value)]
with tgb.Page() as page:
# Works
tgb.selector(value="{value_selector}", lov=lambda value: get_list(value))
# don't work
tgb.selector(value="{value_selector}",
lov=lambda value: get_list(value))
Gui(page=page).run(title="Frontend Demo")
```
### Browsers
Chrome
### OS
Windows
### Version of Taipy
develop - 7/15/24
### Acceptance Criteria
- [ ] Ensure new code is unit tested, and check code coverage is at least 90%.
- [ ] Create related issue in taipy-doc for documentation and Release Notes.
### Code of Conduct
- [X] I have checked the [existing issues](https://github.com/Avaiga/taipy/issues?q=is%3Aissue+).
- [ ] I am willing to work on this issue (optional) | 1medium
|
Title: [Question] Face verification does not meet expectations
Body: I am very thankful for this open source project, it solved my immediate problem.
I am trying to accomplish the task of face comparison with yolov8n-face and Facenet512. But for some samples, the results are not too good.
I tried to replace the face detection with a paid api and this is my test result:
|Face detection type|x|y|w|h|left eye|right eye|Facenet512 cosine distance |
|---|---|---|---|---|---|---|---|
|yolov8n-face.pt img1|43|167|242|334|(99, 288)|(216, 290)| 0.2299605098248957|
|yolov8n-face.pt img2|170|165|405|541|(280, 377)|(457, 372)| |
|yolov8n-face.onnx img1|43|166|241|333|(99, 289)|(216, 291)|0.22794579406637527 |
|yolov8n-face.onnx img2|168|160|401|544|(278, 380)|(456, 376)| |
|payment api img1|44|229|245|271|(100, 284)|(220, 283)| 0.3275035163254295|
|payment api img2|169|307|385|414|(279, 376)|(453, 362)| |
On the left is the yolov8 detection area and on the right is the paid api detection area:
Now I'm confused. Based on the recommended threshold (0.3), the face region obtained from the paid api seems to be better suited to accomplish the face matching task. So should I find a way to shrink the face region or should I find a more suitable threshold based on my actual usage scenario?
Can you provide optimization suggestions?
| 1medium
|
Title: Multiple authentication methods enabled
Body: Hi, is it possible to have multiple authentication methods enabled at the same time? I noticed that if I set
`AUTH_TYPE = AUTH_LDAP`
DB authentication does not work anymore. | 1medium
|
Title: Provide some basic Django Auth Factories (User, Group, Permission...)
Body: #### The problem
Any project who uses `django.contrib.auth` and/or `django.contrib.contenttypes` now has to create the factories for User, Group, Permission, ContentType for using them in his tests, right?
Why we do not provide related Factories for them as they are very basic models...
#### Proposed solution
Something similar to:
```
import factory
import factory.fuzzy
from django.contrib.auth import get_user_model
from django.contrib.auth.models import Group, Permission
from django.contrib.contenttypes.models import ContentType
from factory.django import DjangoModelFactory
class ContentTypeFactory(factory.django.DjangoModelFactory):
class Meta:
model = ContentType
django_get_or_create = ('app_label', 'model')
app_label = factory.Faker("word")
model = factory.Faker("word")
class PermissionFactory(DjangoModelFactory):
class Meta:
model = Permission
django_get_or_create = ("name",)
name = factory.Faker("name")
content_type = factory.SubFactory(ContentTypeFactory)
class GroupFactory(DjangoModelFactory):
class Meta:
model = Group
django_get_or_create = ("name",)
name = factory.Faker("name")
class UserFactory(DjangoModelFactory):
username = factory.Faker("user_name")
email = factory.Faker("email")
password = factory.Faker("password")
is_active = True
is_staff = False
is_superuser = False
class Meta:
model = get_user_model()
django_get_or_create = ("username",)
@classmethod
def _create(cls, model_class, *args, **kwargs):
"""Override the default ``_create`` with our custom call."""
manager = cls._get_manager(model_class)
# Some support to generate auth users....
if kwargs.get("is_superuser"):
return manager.create_superuser(*args, **kwargs)
return manager.create_user(*args, **kwargs)
@factory.post_generation
def groups(self, create, extracted, **kwargs):
if not create:
# Simple build, do nothing.
return
if extracted:
# A list of groups were passed in, use them
for group in extracted:
self.groups.add(Group.objects.get_or_create(name=group)[0])
@factory.post_generation
def user_permissions(self, create, extracted, **kwargs):
if not create:
# Simple build, do nothing.
return
if extracted:
# A list of tuple (ModelClass, "my_permission")
for model, permission in extracted:
content_type = ContentType.objects.get_for_model(model)
self.user_permissions.add(
Permission.objects.get_or_create(
codename=permission, content_type=content_type
)[0]
)
```
#### Extra notes
Please, notice this is only a draft... I have to refine them :)
If could be interesting I can set up a PR draft :)
| 1medium
|
Title: Escape special characters
Body: matplotlib2tikz does not correctly escape special or accented characters in plots. Specifically, including characters such as the percent sign (%) in labels or captions causes a `Extra }, or forgotten \endgroup. \end{tikzpicture}` error; on the other hand, including accented characters such as á, é, and the like causes an inpuntenc error `Package inputenc Error: Unicode char ícu (U+E5)(inputenc) not set up for use with LaTeX. \end{axis}`
**(Not-so Minimal) Working Example:**
**Python code:**
```Python
def vehicles_vs_time():
cols = ['nvhcs', 't', 'demand', 'runID']
df = pandas.read_csv('NVeh_vs_T.csv', encoding='ISO-8859-1', sep=';')
df.columns = cols
df['nvhcs'] = pandas.to_numeric(df['nvhcs'])
df['t'] = pandas.to_numeric(df['t'])
df['runID'] = pandas.to_numeric(df['runID'])
df['demand'] = df['demand'].map(lambda x: float(x.strip('%')) / 100)
df100 = df.loc[df['demand'] == 1.00]
df75 = df.loc[df['demand'] == 0.75]
df50 = df.loc[df['demand'] == 0.50]
df25 = df.loc[df['demand'] == 0.25]
mean_df = pandas.DataFrame(columns=['demand', 'mean_vhcs', 'mean_time'])
mean_df.loc[0] = [1.00, df100['nvhcs'].mean(), df100['t'].mean()]
mean_df.loc[1] = [0.75, df75['nvhcs'].mean(), df75['t'].mean()]
mean_df.loc[2] = [0.50, df50['nvhcs'].mean(), df50['t'].mean()]
mean_df.loc[3] = [0.25, df25['nvhcs'].mean(), df25['t'].mean()]
# from this point onward, plot
fig, ax = pyplot.subplots()
ax.set_facecolor('white')
ax.grid(color='#a1a1a1', linestyle='-', alpha=0.1)
pyplot.xlim([df['nvhcs'].min() - 50, df['nvhcs'].max() + 50])
pyplot.ylim(0, df['t'].max() + 120)
yticks_mins = numpy.arange(0, df['t'].max() + 120, 120)
yticks_10secs = numpy.arange(0, df['t'].max() + 120, 60)
xticks = numpy.arange(200, 1500, 100)
xticks_minor = numpy.arange(150, 1500, 10)
ax.set_yticks(yticks_mins)
ax.set_yticks(yticks_10secs, minor=True)
ax.set_xticks(xticks)
ax.set_xticks(xticks_minor, minor=True)
# trendline
z = numpy.polyfit(df['nvhcs'], df['t'], 2)
p = numpy.poly1d(z)
nx = range(0, int(df['nvhcs'].max()) + 200)
ax.plot(nx, p(nx), '-.', alpha=0.3, label='Ajuste polinomial', color='#F06449')
# scatter
ax.plot(df100['nvhcs'], df100['t'], 'o', color='#17BEBB', label='Factor de demanda 100%')
ax.plot(df75['nvhcs'], df75['t'], 'o', color='#EF2D56', label='Factor de demanda 75%')
ax.plot(df50['nvhcs'], df50['t'], 'o', color='#8CD867', label='Factor de demanda 50%')
ax.plot(df25['nvhcs'], df25['t'], 'o', color='#2F243A', label='Factor de demanda 25%')
ax.legend(loc='upper left')
pyplot.ylabel('Tiempo (MM:SS)')
formatter = matplotlib.ticker.FuncFormatter(to_min_secs)
ax.yaxis.set_major_formatter(formatter)
pyplot.xlabel('Cantidad promedio vehículos en simulación')
# pyplot.title('Scatterplot: Cantidad promedio de vehículos vs duración en tiempo real de simulación')
# pyplot.savefig('n_vhcs_vs_time.pgf')
# pyplot.show()
tikz_save('n_vhcs_vs_time.tex',
figureheight='\\figureheight',
figurewidth='\\figurewidth')
```
**Generated output (note the labels and legends):**
```Latex
% This file was created by matplotlib2tikz v0.6.10.
\begin{tikzpicture}
\definecolor{color0}{rgb}{0.941176470588235,0.392156862745098,0.286274509803922}
\definecolor{color1}{rgb}{0.0901960784313725,0.745098039215686,0.733333333333333}
\definecolor{color2}{rgb}{0.937254901960784,0.176470588235294,0.337254901960784}
\definecolor{color3}{rgb}{0.549019607843137,0.847058823529412,0.403921568627451}
\definecolor{color4}{rgb}{0.184313725490196,0.141176470588235,0.227450980392157}
\begin{axis}[
xlabel={Cantidad promedio vehículos en simulación},
ylabel={Tiempo (MM:SS)},
xmin=150, xmax=1490,
ymin=0, ymax=1713,
width=\figurewidth,
height=\figureheight,
tick align=outside,
tick pos=left,
xmajorgrids,
x grid style={lightgray!84.183006535947712!black},
ymajorgrids,
y grid style={lightgray!84.183006535947712!black},
axis line style={white},
legend style={at={(0.03,0.97)}, anchor=north west, draw=white!80.0!black, fill=white!89.803921568627459!black},
legend cell align={left},
legend entries={{Ajuste polinomial},{Factor de demanda 100%},{Factor de demanda 75%},{Factor de demanda 50%},{Factor de demanda 25%}}
]
\addplot [semithick, color0, opacity=0.3, dash pattern=on 1pt off 3pt on 3pt off 3pt]
table {%
0 6.3172112993997
1 6.57640934437459
2 6.83677322746044
3 7.09830294865727
% ...
% ... lots of points
% ...
1611 1935.80824515293
1612 1937.94560839468
1613 1940.08413747453
1614 1942.2238323925
};
\addplot [semithick, color1, mark=*, mark size=3, mark options={solid}, only marks]
table {%
1398.4 1593
1351.4 1439
1354.67 1388
1415.13 1466
};
\addplot [semithick, color2, mark=*, mark size=3, mark options={solid}, only marks]
table {%
870.2 660
872.27 664
842.13 703
890.4 707
};
\addplot [semithick, color3, mark=*, mark size=3, mark options={solid}, only marks]
table {%
521.13 281
504.4 268
512.2 272
520.6 282
};
\addplot [semithick, color4, mark=*, mark size=3, mark options={solid}, only marks]
table {%
249.67 118
254.8 122
240.87 113
240.93 100
};
\end{axis}
\end{tikzpicture}
```
**Expected (and working!) output:**
```Latex
% This file was created by matplotlib2tikz v0.6.10.
\begin{tikzpicture}
\definecolor{color0}{rgb}{0.941176470588235,0.392156862745098,0.286274509803922}
\definecolor{color1}{rgb}{0.0901960784313725,0.745098039215686,0.733333333333333}
\definecolor{color2}{rgb}{0.937254901960784,0.176470588235294,0.337254901960784}
\definecolor{color3}{rgb}{0.549019607843137,0.847058823529412,0.403921568627451}
\definecolor{color4}{rgb}{0.184313725490196,0.141176470588235,0.227450980392157}
\begin{axis}[
xlabel={Cantidad promedio veh\'iculos en simulaci\'on},
ylabel={Tiempo (MM:SS)},
xmin=150, xmax=1490,
ymin=0, ymax=1713,
width=\figurewidth,
height=\figureheight,
tick align=outside,
tick pos=left,
xmajorgrids,
x grid style={lightgray!84.183006535947712!black},
ymajorgrids,
y grid style={lightgray!84.183006535947712!black},
axis line style={white},
legend style={at={(0.03,0.97)}, anchor=north west, draw=white!80.0!black, fill=white!89.803921568627459!black},
legend cell align={left},
legend entries={{Ajuste polinomial},{Factor de demanda 100\%},{Factor de demanda 75\%},{Factor de demanda 50\%},{Factor de demanda 25\%}}
]
\addplot [semithick, color0, opacity=0.3, dash pattern=on 1pt off 3pt on 3pt off 3pt]
table {%
0 6.3172112993997
1 6.57640934437459
2 6.83677322746044
3 7.09830294865727
% ...
% ... lots of points
% ...
1611 1935.80824515293
1612 1937.94560839468
1613 1940.08413747453
1614 1942.2238323925
};
\addplot [semithick, color1, mark=*, mark size=3, mark options={solid}, only marks]
table {%
1398.4 1593
1351.4 1439
1354.67 1388
1415.13 1466
};
\addplot [semithick, color2, mark=*, mark size=3, mark options={solid}, only marks]
table {%
870.2 660
872.27 664
842.13 703
890.4 707
};
\addplot [semithick, color3, mark=*, mark size=3, mark options={solid}, only marks]
table {%
521.13 281
504.4 268
512.2 272
520.6 282
};
\addplot [semithick, color4, mark=*, mark size=3, mark options={solid}, only marks]
table {%
249.67 118
254.8 122
240.87 113
240.93 100
};
\end{axis}
\end{tikzpicture}
```
| 1medium
|
Title: Add attachment to order_placed email
Body: I need to add an attachment to the `order_placed` email. I see the function I would ideally hook into. The `Checkout.mixins.OrderPlacementMixin` there is a function called `send_order_placed_email`.
So would I fork the whole Checkout app? And add my own `mixins.py` and override the `OrderPlacementMixin`? | 1medium
|
Title: Add social preview image for documentation
Body: Reference: https://squidfunk.github.io/mkdocs-material/setup/setting-up-social-cards/#meta-tags | 0easy
|
Title: BUG: Exporting df as a ESRI shapefile error: Object of type 'float' has no len()
Body: - [x] I have checked that this issue has not already been reported.
- [x] I have confirmed this bug exists on the latest version of geopandas.
- [x] (optional) I have confirmed this bug exists on the main branch of geopandas.
---
#### Code Sample, a copy-pastable example
```python
# Your code here
df.to_file(filename="test.shp", driver="ESRI Shapefile")
```
#### Problem description
The problem is occurred when the column name is an `float` type. Below is the Traceback,
```python
Traceback (most recent call last):
File "/opt/sdss/django/sdss/exposure/views.py", line 353, in downloadExposureResult
df.to_file(filename=shp_path, driver='ESRI Shapefile')
File "/home/sdss/.virtualenvs/sdss/lib/python3.8/site-packages/geopandas/geodataframe.py", line 1114, in to_file
_to_file(self, filename, driver, schema, index, **kwargs)
File "/home/sdss/.virtualenvs/sdss/lib/python3.8/site-packages/geopandas/io/file.py", line 376, in _to_file
if driver == "ESRI Shapefile" and any([len(c) > 10 for c in df.columns.tolist()]):
File "/home/sdss/.virtualenvs/sdss/lib/python3.8/site-packages/geopandas/io/file.py", line 376, in <listcomp>
if driver == "ESRI Shapefile" and any([len(c) > 10 for c in df.columns.tolist()]):
TypeError: object of type 'float' has no len()
```
#### Expected Output
It should write df into the shapefile.
#### Output of ``geopandas.show_versions()``
0.10.2
[paste the output of ``geopandas.show_versions()`` here leaving a blank line after the details tag]
</details>
| 1medium
|
Title: WiringConfiguration(packages=[top_level_package]) affect subpackages only if they have __init__.py
Body: Hello folks! I'm just started to learn dependency-injector.
I was following this public template [https://github.com/teamhide/fastapi-boilerplate](https://github.com/teamhide/fastapi-boilerplate) and found that WiringConfiguration can be affect subpackages of designated top level package only if all of paths have __init__.py.
I understand that from Python 3.6 we don't necessarily need to use __init__.py not if in some of situation but i believe this might not be one of the those cases.
**app.container.py**
```python
from dependency_injector.containers import DeclarativeContainer, WiringConfiguration
from dependency_injector.providers import Factory, Singleton
from app.auth.application.service.jwt import JwtService
from app.user.adapter.output.persistence.repository_adapter import UserRepositoryAdapter
from app.user.adapter.output.persistence.sqlalchemy.user import UserSQLAlchemyRepo
from app.user.application.service.user import UserService
class Container(DeclarativeContainer):
wiring_config = WiringConfiguration(packages=["app"])
user_repo = Singleton(UserSQLAlchemyRepo)
user_repo_adapter = Factory(UserRepositoryAdapter, user_repo=user_repo)
user_service = Factory(UserService, repository=user_repo_adapter)
jwt_service = Factory(JwtService)
``` | 1medium
|
Title: ATRTS not included in df.ta.study("volatility")
Body: Using the latest version of develop, `Average True Range Trailing Stop: atrts`, isn't included in `df.ta.study("volatility")`. To be honest, I'm not entirely sure why. Things appears to be set up right.
```python
Python 3.8.13 (default, Mar 18 2022, 02:13:38)
[GCC 11.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas_ta as ta
>>> print(ta.version)
0.3.59b0
>>> import talib
>>> import pytz
>>> from datetime import datetime
>>> from config import OUTPUT_DIR
>>> import numpy as np
>>> timezone = pytz.utc
>>> date_range = ["20200801", "20200901"]
>>> start_date = timezone.localize(datetime.strptime(date_range[0], "%Y%m%d"))
>>> end_date = timezone.localize(datetime.strptime(date_range[1], "%Y%m%d"))
>>> data = pd.read_feather(f"{OUTPUT_DIR}/BTC.feather")
>>> df = data[data["Datetime"].between(left=start_date, right=end_date, inclusive="both")]
>>> df.set_index(pd.DatetimeIndex(df["Datetime"]), inplace=True)
>>> df.ta.study("volatility")
>>> df.columns
Index(['Datetime', 'Open', 'High', 'Low', 'Close', 'Volume', 'ABER_ZG_5_15',
'ABER_SG_5_15', 'ABER_XG_5_15', 'ABER_ATR_5_15', 'ACCBL_20', 'ACCBM_20',
'ACCBU_20', 'ATRr_14', 'BBL_5_2.0', 'BBM_5_2.0', 'BBU_5_2.0',
'BBB_5_2.0', 'BBP_5_2.0', 'DCL_20_20', 'DCM_20_20', 'DCU_20_20',
'HWM_1', 'HWU_1', 'HWL_1', 'KCLe_20_2', 'KCBe_20_2', 'KCUe_20_2',
'MASSI_9_25', 'NATR_14', 'PDIST', 'RVI_14', 'THERMO_20_2_0.5',
'THERMOma_20_2_0.5', 'THERMOl_20_2_0.5', 'THERMOs_20_2_0.5',
'TRUERANGE_1', 'UI_14'],
dtype='object')``` | 1medium
|
Title: The dataset for lecture 5 practical is not available
Body: I was watching practical of lecture 5 and I like to code along the video, but the dataset used in the video is not available anywhere. Its not present in your kaggle nor in the github repo.
Would humbly request you to upload the same | 0easy
|
Title: Stop automatically sorting `gr.Barplot` labels alphabetically
Body: It seems that the `gr.Barplot` component ignores the actual order of the labels in your dataframe and instead sorts the x values alphabetically / numerically? It should respect the order of values in your dataframe:
<img width="1237" alt="Image" src="https://github.com/user-attachments/assets/1b825a1c-61fa-4feb-9c14-1f5b5d414c92" />
See [playground link](https://www.gradio.app/playground?demo=Hello_World&code=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%3D&reqs=cGFuZGFzCm51bXB5) | 1medium
|
Title: [tabular] Speed-up learning-curve unit tests
Body: Currently learning curve unit tests (introduced in #4411) take a long time (~25+ minutes). They comprise of 380 unit tests that each fit a model and evaluate it, which is time consuming.
We should either speed up these unit tests or disable the majority of them and only run the disabled ones prior to release. | 1medium
|
Title: Any support return dict from fetch?
Body: In many synchronous IO libraries, cursor.fetch_all() is allowed to return a sequence of dictionaries. Do databases consider similar implementations?
Just like:
```python
rows: List[Dict[str, Any]] = await database.fetch_all(query=query, include_field_name=True)
``` | 1medium
|
Title: Document `column_default_sort` requires strings
Body: ### Checklist
- [X] The bug is reproducible against the latest release or `master`.
- [X] There are no similar issues or pull requests to fix it yet.
### Describe the bug
The examples in the [documentation](https://aminalaee.dev/sqladmin/configurations/#list-page) show `column_default_sort` as accepting class attributes. However, since https://github.com/aminalaee/sqladmin/commit/8f9d07d0705b241490a1f55fc69115ef598b8c50 `split()` is being called on them, and that fails unless they are strings
### Steps to reproduce the bug
Visit an admin page with default sort defined via class attributes:
```python
class UserAdmin(ModelView, model=User):
...
column_default_sort = [(User.email, True), (User.name, False)]
```
### Expected behavior
The default sort is applied as defined
### Actual behavior
The server crashes because it can't `split()` class attributes
### Debugging material
_No response_
### Environment
Ubuntu 22.04, Python 3.11
### Additional context
`column_default_sort = [("email", True), ("name", False)]` works as expected | 1medium
|
Title: sh: /usr/bin/dotnet: 权限不够
Body: 想要执行 dotnet core 5 ，提示 sh: /usr/bin/dotnet: 权限不够。
目录下执行也类似:sh: ./dotnet: 权限不够 | 1medium
|
Title: Feature: Filtering Rule for HTTP requests payload (like on emails)
Body: Hi,
I'd like to see it possible to filter the HTTP request for a specific value, as it's currently possible with emails.
I've not spotted this in the current solution.
I'm using a solution where I can create a webhook, but not control other than "on job run". So I'd like to use healthcheck to filter the content, on basis of the payload (like if it contains "success" or similar).
I would be great to see a JSON parsed, however, using plain-text should possibly also work :)
Thank you for making this. I actually enjoy how simple it is it work with.
## Example data
Here's one Request Body for the application I'm trying to Filter (JSON formatted for easier reading).
The first one are the "job done" post, where I'd like to look to match the `params.vms.id` and `result`.
Solution one would be to look after `VM_ID_I_NEED_TO_MATCH_GOES_HERE` in the text, **and** `"result":true` in the text, too (so more than one check), or have the JSON parsed, and define the `result` nested_key, including a `mandatory` information to be part of the payload, before healthchecks will acknowledge the requests as related to the current `check`.
### Example for "Job Done", I need to acknowledge as a success
Requirements:
- `VM_ID_I_NEED_TO_MATCH_GOES_HERE` needs to be part of the payload
- Result should be `true`
For both requirements, it should be possible to just cheat the payload as a string, without having to use a JSON parser or similar. But two different requirements need to be allowed as a user-input (or a regex sting, maybe?... I mean.. Keep is simpel stupid, if that's the easiest thing to do).
```json
{
"callId":"CALL_ID_GOES_HERE",
"method":"backupNg.runJob",
"params":{
"id":"SECRET_ID_GOES_HERE",
"schedule":"SCHEDULER_ID_GOES_HERE",
"settings":{
"SETTINGS_ID_GOES_HERE":{
"snapshotRetention":5
}
},
"vms":{
"id":"VM_ID_I_NEED_TO_MATCH_GOES_HERE"
}
},
"timestamp":1709391402615,
"userId":"USER_ID_GOES_HERE",
"userName":"admin",
"result":true,
"duration":15735,
"type":"post"
}
```
Here's another example. Note the difference in the `vms` "array", which looks a bit odd, but, that's how it's reported.
Therefore, the plain-text validator are OK with me, I guess.
Again, more real-world like data. I've just replaced letters and stuff like that.
```json
{
"callId":"222h2akasaa",
"method":"backupNg.runJob",
"params":{
"id":"fccd0fbb-5ba8-47fd-9e44-401a1f10c474",
"schedule":"2a029a93-0f6d-4a10-891f-0a891041eeec",
"settings":{
"6d564d15-3d24-40e4-aa81-5c7df522def9":{
"exportRetention":1
}
},
"vms":{
"id":{
"__or":[
"bcdff841-c452-4eee-af61-5b8fddc3318b",
"083ac012-06ea-44f8-96e4-93b4731328e4",
"2c1ed51f-67bc-4306-b8f6-c080ee9221d9",
"8577151d-c758-49ce-a177-ad29d3e5fec2",
"f3c60996-418a-41f3-a246-aad3731e5c84",
"25d04cd3-292e-430d-85db-c67f1ecc45b0"
]
}
}
},
"timestamp":1709396008102,
"userId":"9a9758c5-233d-4a69-84e6-7b62ba522895",
"userName":"admin",
"result":true,
"duration":4645273,
"type":"post"
}
```
### Another example
Here's another example, which I'd need to maybe acknowledge,or maybe ignore, on basis of the "result" value, and the
params.id value (`a22aa300-2310-11dd-a222-2201aa1101b1`) value.
```json
{
"callId":"CALL_ID_GOES_HERE",
"userId":"USER_ID_GOES_HERE",
"userName":"admin",
"userIp":"::USER_IP_GOES_HERE",
"method":"backupNg.runJob",
"params":{
"id":"ID_GOES_HERE",
"schedule":"SCHDEULE_ID_GOES_HERE"
},
"timestamp":1709391402650,
"duration":15776,
"result":true,
"type":"post"
}
```
With more "real world" data:
```json
{
"callId":"1ab2abcdaa2a",
"userId":"USER_ID_GOES_HERE",
"userName":"admin",
"userIp":"::REMOVED",
"method":"backupNg.runJob",
"params":{
"id":"a22aa300-2310-11dd-a222-2201aa1101b1",
"schedule":"a3cfd101-faaa-12c2-bbd1-12b22c22eaa1"
},
"timestamp":1709396008114,
"duration":4645290,
"result":true,
"type":"post"
} | 1medium
|
Title: New release?
Body: Hi, could you please make a new release on pypi? I'd like my deploy systems to grab the new fixes and it would probably be a nice idea to also have the updated author on there.
| 0easy
|
Title: docs: code snippet needed: DocumentArray construct
Body: In [this section](https://docarray.jina.ai/fundamentals/documentarray/construct/#construct-from-local-files) there's no snippet for `read_mode`.
Note: Please leave snippet as comment, don't fix directly. I'm working on docs right now and don't want merge conflicts. | 0easy
|
Title: [Bug]: Executor performance degradation
Body: ### Your current environment
<details>
<summary>The output of `python collect_env.py`</summary>
```text
Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.1.3.1
[pip3] nvidia-cuda-cupti-cu12==12.1.105
[pip3] nvidia-cuda-nvrtc-cu12==12.1.105
[pip3] nvidia-cuda-runtime-cu12==12.1.105
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.0.2.54
[pip3] nvidia-curand-cu12==10.3.2.106
[pip3] nvidia-cusolver-cu12==11.4.5.107
[pip3] nvidia-cusparse-cu12==12.1.0.106
[pip3] nvidia-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] pyzmq==26.2.0
[pip3] torch==2.4.0
[pip3] torch_tensor_module==0.0.0
[pip3] torchvision==0.19.0
[pip3] transformers==4.47.0
[pip3] triton==3.0.0
[conda] numpy 1.26.4 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.1.3.1 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.1.105 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.1.105 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.1.105 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.0.2.54 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.2.106 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.4.5.107 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.1.0.106 pypi_0 pypi
[conda] nvidia-ml-py 12.560.30 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.20.5 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.6.85 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.1.105 pypi_0 pypi
[conda] pyzmq 26.2.0 pypi_0 pypi
[conda] torch 2.4.0 pypi_0 pypi
[conda] torch-tensor-module 0.0.0 pypi_0 pypi
[conda] torchvision 0.19.0 pypi_0 pypi
[conda] transformers 4.47.0 pypi_0 pypi
[conda] triton 3.0.0 pypi_0 pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.6.3.post1
```
</details>
### 🐛 Describe the bug
I am currently using `vllm serve` to create an online server and using this server as a Scheduler to send and execute `ExecuteModelRequest` to one `LLM` instances created with `self.llm = LLM(model=self.model, gpu_memory_utilization=self.gpu_memory_utilization, enforce_eager=self.enforce_eager, tensor_parallel_size=self.tp_size)`. The inference is then performed by calling `self.llm.llm_engine.model_executor.execute_model(req)`. However, when I test using `vllm/benchmarks/benchmark_serving.py`, I notice a significant drop in inference performance, with both TTFT and TPOT increasing substantially, while directly using `vllm serve` for inference does not exhibit this issue. Is it possible that `vllm` has implemented some caching or optimizations above the Executor layer in the `LLMEngine`?
### Before submitting a new issue...
- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions. | 1medium
|
Title: AutoKeras with tensorflow.dataset gives TypeError: 'PrefetchDataset' object does not support indexing
Body:
### Bug Description
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-18-e8d1a7f824a0> in <module>
1 # Feed the image classifier with training data.
----> 2 clf.fit(train_ds[0], train_ds[1])
TypeError: 'PrefetchDataset' object does not support indexing
### Reproducing Steps
Following the steps for creating a tf.dataset from: https://www.tensorflow.org/tutorials/load_data/images
I encountered an issue with AutoModel when calling:
clf.fit(train_ds[0], train_ds[1])
and
clf.fit(next(iter(train_ds))[0], next(iter(train_ds))[1])
gives :
TypeError: Expect the data to ImageInput to be numpy.ndarray or tf.data.Dataset, but got <class 'tensorflow.python.framework.ops.EagerTensor'>.
### Expected Behavior
I expect to be able to use AutoKeras with imagesets that are too large to fit in memory.
| 1medium
|
Title: TYPO
Body: Astounding and Great Template for SQL-based Project with FastAPI.
There's a small typo with `USERNAME` here: https://github.com/Aeternalis-Ingenium/FastAPI-Backend-Template/blob/571981c3d6af5feccb90de25a81e82442c22b40f/backend/src/config/settings/base.py#L35C11-L35C11
Consider making this for NoSQL-based aswell as it seems really becoming "Convention" to startup on any project! | 0easy
|
Title: Missing data in HTML report table after upgrading to pytest-html 4.1.1
Body:
Hi Team,
I'm currently encountering a problem with pytest-html after upgrading from version 3.2.0 to 4.1.1. My HTML reports are now empty when running tagged tests, although untagged tests generate reports correctly.
Environment: Python version: 3.10 pytest version: 7.4.4 pytest-html version: 4.1.1 (previously 3.2.0)
I'm suspecting since pytest_html_results_table_header hook is not invoking in pytest-html 4.1.1 when running tests with tags. With pytest_html_results_table_header hook I’m trying to insert a snapshot column for failed test , since the table is not properly customized I'm getting an empty report. This hook was invoked in the previous version and is still invoking in 4.1.1 when running tests without tags. The lack of this hook prevents proper table customization, resulting in an empty table.
Observed Behavior: The HTML report's results table is empty when running the tests with tags.
Expected Behavior: The results table should display all test results (name, outcome, duration), including the newer column 'snapshot' when running the tests with tags.
If you have a moment, I'd appreciate your help. | 1medium
|
Title: Save nparray as list
Body: ### Describe the bug
When I use the `map` function to convert images into features, datasets saves nparray as a list. Some people use the `set_format` function to convert the column back, but doesn't this lose precision?
### Steps to reproduce the bug
the map function
```python
def convert_image_to_features(inst, processor, image_dir):
image_file = inst["image_url"]
file = image_file.split("/")[-1]
image_path = os.path.join(image_dir, file)
image = Image.open(image_path)
image = image.convert("RGBA")
inst["pixel_values"] = processor(images=image, return_tensors="np")["pixel_values"]
return inst
```
main function
```python
map_fun = partial(
convert_image_to_features, processor=processor, image_dir=image_dir
)
ds = ds.map(map_fun, batched=False, num_proc=20)
print(type(ds[0]["pixel_values"])
```
### Expected behavior
(type < list>)
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-4.19.91-009.ali4000.alios7.x86_64-x86_64-with-glibc2.35
- Python version: 3.11.5
- `huggingface_hub` version: 0.23.4
- PyArrow version: 14.0.2
- Pandas version: 2.1.4
- `fsspec` version: 2023.10.0 | 1medium
|
Title: Documentation Hidden by Menu
Body: ### Is there an existing issue for this?
- [X] I have searched the existing issues
### Describe the bug
When scrolling and zooming on sanic.dev via safari on OSX (is it MacOS now?) half the documentation gets obscured and it is not possible to correct it without a refresh.
<img width="979" alt="Screenshot 2024-03-13 at 17 07 46" src="https://github.com/sanic-org/sanic/assets/159023003/fdd4c1aa-2e17-418d-9658-f10e0645326a">
### Code snippet
_No response_
### Expected Behavior
The documentation is not obscured.
### How do you run Sanic?
As a module
### Operating System
MacOS
### Sanic Version
current
### Additional context
_No response_ | 1medium
|
Title: Custom fields serialisation & deserialisation
Body: Hi,
I am trying to create a custom field to implement client side encryption. I am able to perform serialisation during save, however I dont see a way to deserialise the data after a find (decrypt it). Is this possible by some means?
This is how I created the custom field
`class EncryptedFieldType(str):
@classmethod
def __get_validators__(cls):
yield cls.validate
@classmethod
def validate(cls, v):
if isinstance(v, bytes): # Handle data coming from MongoDB
print("In isinstance(v, bytes) ...")
return "hello"
if not isinstance(v, str):
raise TypeError("string required")
if not v.isascii():
raise ValueError("Only ascii characters are allowed")
return v
@classmethod
def __bson__(cls, v) -> str:
print("In __bson__")
return "*******"
class CollectionWithEncField(Model):
encrypted_field: EncryptedFieldType` | 1medium
|
Title: Doc error: on Docs » Visualizers and API » Feature Analysis Visualizers » PCA Projection "principle component analysis" should be "principal component analysis"
Body: In http://www.scikit-yb.org/en/latest/api/features/pca.html
the phrase "principle component analysis" should be "principal component analysis" | 0easy
|
Title: Tokenization in 8.2.2
Body: I think in tokenize function, if it's tokenizing words, it should add space_character to tokens too. Otherwise, in predict function, it will assume '<unk>' for spaces and the predictions doesn't have spaces between them (which can be solved by manipulating the predict function to this line: `return ''.join([vocab.idx_to_token[i] + ' ' for i in outputs])`)
I think tokenized should change like this:
`[line.split() for line in lines] + [[' ']]`
If I'm right, I can make a pr for both tokenize and predict funcs. (although for predict I might have to change inputs of function as well to recognize if it's a char level or word lever rnn) | 1medium
|
Title: API Calls not working
Body: I am not getting the expected response from Alpha Vantage APIs. The following are the details, could someone help?
Infrastructure - GCP Windows server
GCP Region: asia-south1-c
Error - { "Note": "Thank you for using Alpha Vantage! Our standard API call frequency is 5 calls per minute and 500 calls per day. Please visit https://www.alphavantage.co/premium/ if you would like to target a higher API call frequency." }
Key - Free trial keys
Pls note our url /python code works well to fetch the details however through GCP infrastructure is not working.
Code:
def __init__(self, api_key, part_url, query, function, exchange, symbol, interval, format):
self.api_key = api_key
self.part_url = part_url
self.query = query
self.function = function
self.exchange = exchange
self.symbol = symbol
self.interval = interval
self.format = format
def get_data(self):
#data_url = 'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol=NSE:TATAMOTORS&interval=1min&apikey=STPO0LS61SH9DZ0J&datatype=csv'
data_url = self.part_url + self.query + self.function + '&symbol' + '=' + self.exchange + ':' + self.symbol + '&' + 'interval=' + self.interval + '&apikey=' + self.api_key + '&datatype=' + self.format
print (data_url)
try:
data=requests.get(data_url)
except Exception as e:
print ("Failed with error {0}".format(e))
raise SystemExit
| 1medium
|
Title: Detected short number
Body: Hello. I have image:
but easyOCR do not work.
I try use `detect` with diferrent parameters, but method return empty.
then i tried to make my simple-detector:
```
def recognition_number(self, img: np.ndarray) -> str:
image = self._filtration(img)
thresh = cv2.adaptiveThreshold(image, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 3, 1)
cnts, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
ox = [i[0][0] for i in cnts[0]]
oy = [i[0][1] for i in cnts[0]]
data = self.reader.recognize(image, horizontal_list=[[min(ox), max(ox), min(oy), max(oy)]], free_list=[], allowlist="1234567890.,")
return "".join([el[1] for el in data])
```
this solved the problem and now the number is recognized.
is it possible to do something with a easyOCR `detect`? | 1medium
|
Title: [RFC]typed: Turn typed.Dynaconf into a class (currently it is just a constructor)
Body: Current implementation of `typed.Dynaconf` is just a pass-through constructor, this class intercepts passed arguments and then created an instance of `LazySettings` with `cast`ed type annotation for auto complete.
## Proposal
- Turn that class into a real class with a `__init__` method, that will perform all the processes currently performed on `__new__` but instead of returning a `LazySettings` will instantiate `self` and assign `self._dynaconf_instance = LazySettings(...)`
- Stop inheriting from `BaseSettings`
- Implement attribute and method `stubs` that will feed the type check and auto completion
- Implement lookup for missing keys on the `self._dynaconf_instance`
The advantages will be:
- Expose only selected attributes for type annotation
- cache the values on the instance itself, after the first access, assign the value to the instance
- LazySettings will be just the `settings storage backend` that will eventually be replaced
| 2hard
|
Title: [ChunkStore] Decompression error on concurrent read & write
Body: #### Arctic Version
```
1.70.0
```
#### Arctic Store
```
ChunkStore
```
#### Platform and version
CentOS 7.2
#### Description of problem and/or code sample that reproduces the issue
I'm getting an error when I have two threads simultaneously reading and writing from one symbol. The reading thread will periodically throw a LZ4BlockError:
```LZ4BlockError: Decompression failed: corrupt input or insufficient space in destination buffer. Error code: 24049```
Here is the code to reproduce it:
```
def get_library():
return Arctic('some_host:1234').get_library(LIB_NAME)
def write_loop():
library = get_library()
while True:
data = create_data(100) # Creates 100 rows of random data
library.append('SYMBOL', data)
def read_loop():
library = get_library()
while True:
df = library.read('SYMBOL')
proc = mp.Process(target=write_loop)
proc.start()
try:
read_loop()
finally:
proc.terminate()
```
From a quick check, it seems that the data being passed to decompress(_str) in _compression.py is not valid lz4 - could the the block metadata and data be out of sync? | 2hard
|
Title: FastAPI integration error
Body: ### Description
I apologize if this belongs in the FastAPI issues instead of logfire. I'm not really sure who is the culprit here.
I'm attaching a [sample project](https://github.com/user-attachments/files/16090524/example.zip) to demonstrate an error when `logfire[fastapi]` is added to a FastAPI project.
> **Note:** forcing a downgrade to pydantic v1 fixes the issue _(or removing logfire all together)_
### Error Reproduction
A simple API without any input works:
```shell
curl "http://localhost:8000/hello/"
```
Calling an API with a pydantic model as input fails:
```shell
curl -X POST "http://localhost:8000/test/" -H "Content-Type: application/json" -d '{"name": "test"}'
````
Error log:
```
INFO: 127.0.0.1:58776 - "POST /test/ HTTP/1.1" 500 Internal Server Error
ERROR: Exception in ASGI application
Traceback (most recent call last):
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/uvicorn/protocols/http/httptools_impl.py", line 399, in run_asgi
result = await app( # type: ignore[func-returns-value]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/uvicorn/middleware/proxy_headers.py", line 70, in __call__
return await self.app(scope, receive, send)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/fastapi/applications.py", line 1054, in __call__
await super().__call__(scope, receive, send)
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/starlette/applications.py", line 123, in __call__
await self.middleware_stack(scope, receive, send)
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/starlette/middleware/errors.py", line 186, in __call__
raise exc
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/starlette/middleware/errors.py", line 164, in __call__
await self.app(scope, receive, _send)
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/opentelemetry/instrumentation/asgi/__init__.py", line 631, in __call__
await self.app(scope, otel_receive, otel_send)
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/starlette/middleware/exceptions.py", line 65, in __call__
await wrap_app_handling_exceptions(self.app, conn)(scope, receive, send)
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app
raise exc
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
await app(scope, receive, sender)
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/starlette/routing.py", line 756, in __call__
await self.middleware_stack(scope, receive, send)
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/starlette/routing.py", line 776, in app
await route.handle(scope, receive, send)
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/starlette/routing.py", line 297, in handle
await self.app(scope, receive, send)
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/starlette/routing.py", line 77, in app
await wrap_app_handling_exceptions(app, request)(scope, receive, send)
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/starlette/_exception_handler.py", line 64, in wrapped_app
raise exc
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/starlette/_exception_handler.py", line 53, in wrapped_app
await app(scope, receive, sender)
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/starlette/routing.py", line 72, in app
response = await func(request)
^^^^^^^^^^^^^^^^^^^
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/fastapi/routing.py", line 269, in app
solved_result = await solve_dependencies(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/logfire/_internal/integrations/fastapi.py", line 111, in patched_solve_dependencies
return await instrumentation.solve_dependencies(request, original)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/logfire/_internal/integrations/fastapi.py", line 173, in solve_dependencies
result = await original
^^^^^^^^^^^^^^
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/fastapi/dependencies/utils.py", line 628, in solve_dependencies
) = await request_body_to_args( # body_params checked above
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/fastapi/dependencies/utils.py", line 758, in request_body_to_args
v_, errors_ = field.validate(value, values, loc=loc)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/fastapi/_compat.py", line 127, in validate
self._type_adapter.validate_python(value, from_attributes=True),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/pydantic/type_adapter.py", line 142, in wrapped
return func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/pydantic/type_adapter.py", line 373, in validate_python
return self.validator.validate_python(object, strict=strict, from_attributes=from_attributes, context=context)
^^^^^^^^^^^^^^
File "/Users/mcantrell/.pyenv/versions/3.11.9/lib/python3.11/functools.py", line 1001, in __get__
val = self.func(instance)
^^^^^^^^^^^^^^^^^^^
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/pydantic/type_adapter.py", line 142, in wrapped
return func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/acme/fastapi-pydantic-error/.venv/lib/python3.11/site-packages/pydantic/type_adapter.py", line 318, in validator
assert isinstance(self._validator, SchemaValidator)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AssertionError
```
To fix the problem, either remove logfire completely or downgrade pydantic to v1.
### Python, Logfire & OS Versions, related packages (not required)
```TOML
requests="2.32.3"
pydantic="2.8.0"
fastapi="0.111.0"
protobuf="4.25.3"
rich="13.7.1"
executing="2.0.1"
opentelemetry-api="1.25.0"
opentelemetry-exporter-otlp-proto-common="1.25.0"
opentelemetry-exporter-otlp-proto-http="1.25.0"
opentelemetry-instrumentation="0.46b0"
opentelemetry-instrumentation-asgi="0.46b0"
opentelemetry-instrumentation-fastapi="0.46b0"
opentelemetry-proto="1.25.0"
opentelemetry-sdk="1.25.0"
opentelemetry-semantic-conventions="0.46b0"
opentelemetry-util-http="0.46b0"
```
| 1medium
|
Title: geemap.ee_to_geopandas allow more than 5,000 features
Body: It seems that it is not possible to convert from EE to geopandas using the geemap.ee_to_geopandas function if there are more than 5,000 features. If so it fails with:
Exception: Collection query aborted after accumulating over 5000 elements.
Is it possible to allow more than 5,000 features?
| 1medium
|
Title: Suppress Matplotlib 2.0 warning
Body: cf. #45 | 1medium
|
Title: Set video to main video in edit screen throws: 'File' object has no attribute 'content_type'
Body: ## Steps to Reproduce
<!-- Please include as many steps to reproduce so that we can replicate the problem. -->
1. Upload a video without setting it as main video
2. Edit the uploaded video
3. set it as main video
4. get this error message
whole error message in html:
[AttributeError at _de_exercise_video_677_1_edit.html.txt](https://github.com/wger-project/wger/files/8497979/AttributeError.at._de_exercise_video_677_1_edit.html.txt)
PS: Aren't this too much sensible informations for a productivity error page?
EDIT: I don't choose an video for replacing the old one. | 1medium
|
Title: Latest requirements.txt seems to be over-specifying dependencies
Body: This is the latest version requirements.txt file:
```
black>=19.3b0
darglint
hypothesis>=4.4.0
interrogate
ipykernel
isort>=4.3.18
jupyter_client
lxml
natsort
nbsphinx>=0.4.2
pandas-flavor
pandas-vet
pre-commit
pyspark
pytest-azurepipelines
pytest-cov
pytest>=3.4.2
scikit-learn
seaborn
setuptools>=38.5.2
sphinxcontrib-fulltoc==1.2.0
unyt
xarray
```
Those are inject in the setup.py as mandatory dependencies but a lot in there looks like docs, dev, or optional. Is pyjanitor really require all that at run time? | 1medium
|
Title: Request/Guidance - HowTo make using pywinauto easy
Body: **This is not an issue - but a request**
I'm a novice in python, perhaps I was still able to use pywinauto and do a good set of automation in the Unified Service Desk of Dynamics 365.
Now that I struggle a lot to progress quickly to cover more scenarios, I was looking for the below:
- A good tutorial with examples on how to use pywinauto libraries including how to use the uia_control wrappers
- A utility similar to inpect.exe but generates the pywinauto code to the action that we do on the uia control
- The same utility to give the hierarchical path of the uia control. because using the print_control_identifiers output to navigate the controls is way too difficult in complex application like USD
- How to switch between wpf controls and html components
@vasily-v-ryabov @airelil @enjoysmath | 3misc
|
Title: Where are natures of pokemons?
Body: not able to find nature a pokemon in the pokemon endpoint
| 3misc
|
Title: 【百度深度学习开源平台飞桨合作】
Body: 您好，我是百度飞桨的产品经理施依欣，看到transbigdata提供了较丰富的交通时空大数据分析能力，飞桨作为国内首个开源的深度学习平台，希望能不断深耕各行各业，因此想要和您进一步沟通，看看是否能够有深入结合/合作的机会，如果您方便的话可以添加我的微信（同电话）：18108656919
期待您的回复~
施依欣 | 百度飞桨产品经理 | 3misc
|
Title: Texttiling and paragraphs
Body: Hello.
I have a block of text that I want to segment. However I found out that the texttiling implementation requires that the input text is split into paragraphs. My questions is why does this happen? Doesn't the algorithm create pseudosentences and blocks on its own? After all, Hearst didn't rely on sentences and paragraphs to segment text. | 1medium
|
Title: Add support to Julia notebooks
Body: Please add option to convert Julia notebooks into standalone web apps. | 1medium
|
Title: broken with httpx 0.10
Body: **Describe the bug**
authlib uses old classes of httpx. New version of httpx 0.10 removed `AsyncRequest` `AsyncResponse` and also there's no middleware.
**Environment:**
- OS: Linux
- Python Version: 3.7
- Authlib Version: 0.13
| 1medium
|
Title: [BUG] ERROR: Failed building wheel for duckdb
Body: **Describe the bug**
When attempting to install via `pip install pygwalker` I receive the following errors:
`ERROR: Failed building wheel for duckdb`
`ERROR: Could not build wheels for duckdb, which is required to install pyproject.toml-based projects`
**To Reproduce**
Steps to reproduce the behavior:
1. Run ` pip install pygwalker `
2. See error
**Expected behavior**
The library should install without issue.
**Screenshots**
<img width="1513" alt="Screenshot 2024-01-19 at 8 06 33 PM" src="https://github.com/Kanaries/pygwalker/assets/7577457/65938774-af79-4325-a1b6-1aa4c1ee888f">
**Versions**
- pygwalker version: 0.4.2
- python version: 3.12.1 | 1medium
|
Title: [🐛 BUG] Problem of intercompability between environment Docker/Un*x and Windows for path
Body: ### What went wrong? 🤔
First, create a storage folder, ".taipy", built when running Taipy with Windows.
Then, try to run your code in a Linux-based Docker with this ".taipy".
This will create an issue for File Based Datanodes.
```
Traceback (most recent call last):
File "/app/main.py", line 20, in <module>
print(scenario.data.read())
^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/taipy/core/data/data_node.py", line 411, in read
return self.read_or_raise()
^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/taipy/core/data/data_node.py", line 402, in read_or_raise
return self._read()
^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/taipy/core/data/pickle.py", line 99, in _read
return self._read_from_path()
^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/taipy/core/data/pickle.py", line 105, in _read_from_path
with open(path, "rb") as pf:
^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: 'user_data\\pickles\\DATANODE_data_dec56298-e438-4fcd-a1d9-313ad7bfa33e.p'
```
### Expected Behavior
This should work on all supported versions of Taipy.
### How to reproduce
Dockerfile
```
# Copyright 2021-2024 Avaiga Private Limited
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
# an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
# specific language governing permissions and limitations under the License.
FROM python:3.11
WORKDIR /app
# Install application dependencies.
COPY src/requirements.txt .
RUN pip install -r requirements.txt
# Copy the application source code.
COPY src .
CMD ["taipy", "run", "--no-debug", "--no-reloader", "main.py", "--host", "0.0.0.0", "-P", "5000", "--experiment", "1", "--force"]
```
src/requirements.txt
```
taipy==4.0.1
```
src/main.py
```python
import taipy as tp
from taipy import Config, Scope
data_cfg = Config.configure_data_node(
id="data", storage_type="pickle", scope=Scope.GLOBAL, default_data=1
)
scenario_cfg = Config.configure_scenario(
id="scenario",
additional_data_node_configs=[data_cfg],
)
if __name__ == "__main__":
tp.Orchestrator().run()
scenarios = tp.get_scenarios()
if len(scenarios) == 0:
scenario = tp.create_scenario(scenario_cfg)
else:
scenario = scenarios[0]
print(scenario.data.read())
```
Go into `src` and run the main.py locally (Windows) with :
```
taipy run main.py --experiment 1 --force
```
This will create the `.taipy` and `user_data` folders inside of src.
Then, go in the root folder and build and run the docker:
```bash
docker build -t issue2261 .
docker run -it -p 5000:5000issue2261
```
Here is the error raised:
```
(dev) PS C:\Users\jacta\OneDrive\Bureau\taipy\Code\basic-demo> docker run -it -p 5000:5000 issue
[2024-11-21 09:02:51.005][Taipy][INFO] Updating configuration with command-line arguments...
[2024-11-21 09:02:51.005][Taipy][INFO] Managing application's version...
[2024-11-21 09:02:51.014][Taipy][INFO] Checking application's version...
[2024-11-21 09:02:51.015][Taipy][WARNING] tasks field of ScenarioConfig `scenario` is empty.
[2024-11-21 09:02:51.015][Taipy][INFO] Blocking configuration update...
[2024-11-21 09:02:51.015][Taipy][INFO] Starting job dispatcher...
[2024-11-21 09:02:51.016][Taipy][INFO] Orchestrator service has been started.
Traceback (most recent call last):
File "/app/main.py", line 20, in <module>
print(scenario.data.read())
^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/taipy/core/data/data_node.py", line 411, in read
return self.read_or_raise()
^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/taipy/core/data/data_node.py", line 402, in read_or_raise
return self._read()
^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/taipy/core/data/pickle.py", line 99, in _read
return self._read_from_path()
^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/taipy/core/data/pickle.py", line 105, in _read_from_path
with open(path, "rb") as pf:
^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: 'user_data\\pickles\\DATANODE_data_dec56298-e438-4fcd-a1d9-313ad7bfa33e.p'
```
### Version of Taipy
4.0.1
### Acceptance Criteria
- [x] A unit test reproducing the bug is added.
- [x] Any new code is covered by a unit tested.
- [x] Check code coverage is at least 90%.
- [ ] The bug reporter validated the fix.
- [ ] Related issue(s) in taipy-doc are created for documentation and Release Notes are updated.
### Code of Conduct
- [X] I have checked the [existing issues](https://github.com/Avaiga/taipy/issues?q=is%3Aissue+).
- [ ] I am willing to work on this issue (optional) | 1medium
|
Title: From future import annotations breaks lazy types
Body: ## Describe the Bug
`from __future__ import annotations` breaks type resolver which results in `TypeError: Model fields cannot be resolved. Unexpected type 'typing.Any'`
I did not manage to track down what exactly causes this issue, but whenever I add `from __future__ import annotations` to the top of the file with strawberry types - strawberry type resolver breaks
<!-- A clear and concise description of what the bug is. -->
## System Information
- Operating system:
- Strawberry version (if applicable): 0.235.2
## Additional Context
<!-- Add any other relevant information about the problem here. --> | 2hard
|
Title: Add intensity_median to regionprops
Body: ### Description:
We have `intensity_min`, `intensity_mean` and `intensity_max` in `regionprops` already:
https://github.com/scikit-image/scikit-image/blob/5305ae2ae1fa5d8c01a45e77bb0c44cbabfe1102/skimage/measure/_regionprops.py#L573-L585
What about adding `intensity_median` as well, backed by `np.median` in a similar way as the other props?
I can open a pull request if there are no general objections against this idea. | 1medium
|
Title: [Bug][V1]: allenai/OLMo-2-0325-32B-Instruct - unexpected keyword argument 'inputs_embeds'
Body: ### Your current environment
<details>
INFO 03-17 17:57:21 [__init__.py:256] Automatically detected platform cuda.
Collecting environment information...
PyTorch version: 2.5.1+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.30.4
Libc version: glibc-2.35
Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.5.0-35-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.5.82
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA H100 80GB HBM3
GPU 1: NVIDIA H100 80GB HBM3
GPU 2: NVIDIA H100 80GB HBM3
GPU 3: NVIDIA H100 80GB HBM3
GPU 4: NVIDIA H100 80GB HBM3
GPU 5: NVIDIA H100 80GB HBM3
GPU 6: NVIDIA H100 80GB HBM3
GPU 7: NVIDIA H100 80GB HBM3
Nvidia driver version: 555.42.02
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.2.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 128
On-line CPU(s) list: 0-127
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Platinum 8462Y+
CPU family: 6
Model: 143
Thread(s) per core: 2
Core(s) per socket: 32
Socket(s): 2
Stepping: 8
CPU max MHz: 4100.0000
CPU min MHz: 800.0000
BogoMIPS: 5600.00
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hfi vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 3 MiB (64 instances)
L1i cache: 2 MiB (64 instances)
L2 cache: 128 MiB (64 instances)
L3 cache: 120 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-31,64-95
NUMA node1 CPU(s): 32-63,96-127
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] mypy==1.11.1
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] onnx==1.14.1
[pip3] onnxruntime==1.18.1
[pip3] pyzmq==26.2.1
[pip3] sentence-transformers==3.2.1
[pip3] torch==2.5.1
[pip3] torchaudio==2.5.1
[pip3] torchvision==0.20.1
[pip3] transformers==4.48.3
[pip3] transformers-stream-generator==0.0.5
[pip3] triton==3.1.0
[pip3] tritonclient==2.51.0
[pip3] vector-quantize-pytorch==1.21.2
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.7.4.dev448+g977a16772.d20250314
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 NIC7 CPU AffinityNUMA Affinity GPU NUMA ID
GPU0 X NV18 NV18 NV18 NV18 NV18 NV18 NV18 PIX NODE NODE NODE SYS SYS SYS SYS 0-31,64-95 0 N/A
GPU1 NV18 X NV18 NV18 NV18 NV18 NV18 NV18 NODE PIX NODE NODE SYS SYS SYS SYS 0-31,64-95 0 N/A
GPU2 NV18 NV18 X NV18 NV18 NV18 NV18 NV18 NODE NODE PIX NODE SYS SYS SYS SYS 0-31,64-95 0 N/A
GPU3 NV18 NV18 NV18 X NV18 NV18 NV18 NV18 NODE NODE NODE PIX SYS SYS SYS SYS 0-31,64-95 0 N/A
GPU4 NV18 NV18 NV18 NV18 X NV18 NV18 NV18 SYS SYS SYS SYS PIX NODE NODE NODE 32-63,96-127 1 N/A
GPU5 NV18 NV18 NV18 NV18 NV18 X NV18 NV18 SYS SYS SYS SYS NODE PIX NODE NODE 32-63,96-127 1 N/A
GPU6 NV18 NV18 NV18 NV18 NV18 NV18 X NV18 SYS SYS SYS SYS NODE NODE PIX NODE 32-63,96-127 1 N/A
GPU7 NV18 NV18 NV18 NV18 NV18 NV18 NV18 X SYS SYS SYS SYS NODE NODE NODE PIX 32-63,96-127 1 N/A
NIC0 PIX NODE NODE NODE SYS SYS SYS SYS X NODE NODE NODE SYS SYS SYS SYS
NIC1 NODE PIX NODE NODE SYS SYS SYS SYS NODE X NODE NODE SYS SYS SYS SYS
NIC2 NODE NODE PIX NODE SYS SYS SYS SYS NODE NODE X NODE SYS SYS SYS SYS
NIC3 NODE NODE NODE PIX SYS SYS SYS SYS NODE NODE NODE X SYS SYS SYS SYS
NIC4 SYS SYS SYS SYS PIX NODE NODE NODE SYS SYS SYS SYS X NODE NODE NODE
NIC5 SYS SYS SYS SYS NODE PIX NODE NODE SYS SYS SYS SYS NODE X NODE NODE
NIC6 SYS SYS SYS SYS NODE NODE PIX NODE SYS SYS SYS SYS NODE NODE X NODE
NIC7 SYS SYS SYS SYS NODE NODE NODE PIX SYS SYS SYS SYS NODE NODE NODE X
Legend:
X = Self
SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
PIX = Connection traversing at most a single PCIe bridge
NV# = Connection traversing a bonded set of # NVLinks
NIC Legend:
NIC0: mlx5_0
NIC1: mlx5_1
NIC2: mlx5_2
NIC3: mlx5_3
NIC4: mlx5_4
NIC5: mlx5_5
NIC6: mlx5_6
NIC7: mlx5_7
CUDA_HOME=/usr/local/cuda
CUDA_HOME=/usr/local/cuda
CUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda
LD_LIBRARY_PATH=:/usr/local/cuda/lib64
NCCL_CUMEM_ENABLE=0
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY
</details>
### 🐛 Describe the bug
I am hitting the following
```
ERROR 03-17 17:56:39 [core.py:340] TypeError: Olmo2ForCausalLM.forward() got an unexpected keyword argument 'inputs_embeds'
```
When running `vllm serve allenai/OLMo-2-0325-32B-Instruct --tensor-parallel-size 2` in V1. V0 is fine:
```
ERROR 03-17 17:56:39 [core.py:340] EngineCore hit an exception: Traceback (most recent call last):
ERROR 03-17 17:56:39 [core.py:340] File "/home/tms/vllm/vllm/v1/engine/core.py", line 332, in run_engine_core
ERROR 03-17 17:56:39 [core.py:340] engine_core = EngineCoreProc(*args, **kwargs)
ERROR 03-17 17:56:39 [core.py:340] File "/home/tms/vllm/vllm/v1/engine/core.py", line 287, in __init__
ERROR 03-17 17:56:39 [core.py:340] super().__init__(vllm_config, executor_class, log_stats)
ERROR 03-17 17:56:39 [core.py:340] File "/home/tms/vllm/vllm/v1/engine/core.py", line 62, in __init__
ERROR 03-17 17:56:39 [core.py:340] num_gpu_blocks, num_cpu_blocks = self._initialize_kv_caches(
ERROR 03-17 17:56:39 [core.py:340] File "/home/tms/vllm/vllm/v1/engine/core.py", line 121, in _initialize_kv_caches
ERROR 03-17 17:56:39 [core.py:340] available_gpu_memory = self.model_executor.determine_available_memory()
ERROR 03-17 17:56:39 [core.py:340] File "/home/tms/vllm/vllm/v1/executor/abstract.py", line 66, in determine_available_memory
ERROR 03-17 17:56:39 [core.py:340] output = self.collective_rpc("determine_available_memory")
ERROR 03-17 17:56:39 [core.py:340] File "/home/tms/vllm/vllm/v1/executor/multiproc_executor.py", line 133, in collective_rpc
ERROR 03-17 17:56:39 [core.py:340] raise e
ERROR 03-17 17:56:39 [core.py:340] File "/home/tms/vllm/vllm/v1/executor/multiproc_executor.py", line 122, in collective_rpc
ERROR 03-17 17:56:39 [core.py:340] raise result
ERROR 03-17 17:56:39 [core.py:340] TypeError: Olmo2ForCausalLM.forward() got an unexpected keyword argument 'inputs_embeds'
ERROR 03-17 17:56:39 [core.py:340]
CRITICAL 03-17 17:56:39 [core_client.py:269] Got fatal signal from worker processes, shutting down. See stack trace above for root cause issue.
```
### Before submitting a new issue...
- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions. | 1medium
|
Title: QString' object has no attribute 'rfind'
Body: Ubuntu 16.04
python 2.7
qt4
when I load the image, the terminal show that "QString' object has no attribute 'rfind'.So ,I can't create the rectbox.
| 1medium
|
Title: Use of matplotlib's colormap viridis
Body: When I use matplotlibs new colormap _viridis_ and add a colorbar in a figure, matplotlib2tikz returns an AssertionError, because _viridis_ is not an linear-segemented-colormap.
``` python
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib2tikz
x, y = np.meshgrid(np.linspace(0, 1), np.linspace(0, 1))
z = x**2 - y**2
plt.pcolormesh(x, y, z, cmap=cm.viridis)
plt.colorbar()
matplotlib2tikz.save('test.tex')
```
Is it possible to use pgfplots built-in colormap viridis for the colorbar specification with `colormap/viridis`, `point meta min=x` and `point meta max=y` in the axis options or is there a problem with different colormap-calculations/-interpolations?
| 1medium
|
Title: Validation runs during overfit, even when turned off
Body: ### Bug description
I am attempting to overfit a model for demonstration. I am using the CLI and I am using trainer.overfit_batches=.125 and trainer.limit_val_batches=0.
If trainer.limit_val_batches=0 is run without the overfit_batches, the desired effect of turning off the validation dataloader and epoch is achieved.
### What version are you seeing the problem on?
v2.1
### How to reproduce the bug
```python
python cli.py fit -c config.yaml --trainer.limit_val_batches=0 --trainer.overfit_batches=.125
```
```
### Error messages and logs
```
No error message, undesired behavior
```
### Environment
<details>
<summary>Current environment</summary>
```
#- Lightning Component (e.g. Trainer, LightningModule, LightningApp, LightningWork, LightningFlow):
#- PyTorch Lightning Version (e.g., 1.5.0): 2.1
#- Lightning App Version (e.g., 0.5.2): N/A
#- PyTorch Version (e.g., 2.0):2.1
#- Python version (e.g., 3.9): 3.10
#- OS (e.g., Linux): Linux
#- CUDA/cuDNN version: 12.1
#- GPU models and configuration: A100
#- How you installed Lightning(`conda`, `pip`, source): PIP
#- Running environment of LightningApp (e.g. local, cloud): SLURM
```
</details>
### More info
_No response_
cc @borda @justusschock @awaelchli | 1medium
|
Title: Pre prompt like GPT
Body: Just like OPENAI library has parameters like prompt where we put instruction to it. For example: Act like a mychatbot who's name is Toranto. so it will act like Toranto. Can't we do the same with bardapi? | 1medium
|
Title: 获取用户的粉丝数等数据
Body: 期望提供一个获取用户的粉丝数等数据的接口，谢谢大佬
<img width="499" alt="image" src="https://user-images.githubusercontent.com/16174533/211499289-88218d27-b738-449f-957c-39ea9436c617.png">
| 1medium
|
Title: [Proposal] Allowing environments with different Box action/observation space limits in a vector environment
Body: ### Proposal
https://github.com/Farama-Foundation/Gymnasium/blob/8333df8666811d1d0f87f1ca71803cc58bcf09c6/gymnasium/vector/sync_vector_env.py#L251-L266
https://github.com/Farama-Foundation/Gymnasium/blob/8333df8666811d1d0f87f1ca71803cc58bcf09c6/gymnasium/vector/async_vector_env.py#L576-L596
Currently, these methods checks if all the observation and action spaces in a vector environment are identical, and raises an error if they are not. I'm assuming this is the case because we want to ensure that we can stack the observations and actions into one numpy array. I'm proposing a change to allow differences in the observation and action spaces as long as the shapes are consistent (e.g. the values in the `low` and `high` portions of a Box space).
The change can be implemented with an optional parameter to enable/disable it when creating the vector environments to preserve current default behaviours for now.
### Motivation
I want to vectorize environments with different action space boundaries but the current implementation of vector environments does not allow for that.
### Pitch
_No response_
### Alternatives
_No response_
### Additional context
_No response_
### Checklist
- [ ] I have checked that there is no similar [issue](https://github.com/Farama-Foundation/Gymnasium/issues) in the repo
| 1medium
|
Title: seaborn objects scale with two visualisations with same kwargs?
Body: Hello,
I ran into a problem with scale, when I'm trying to display two visualizations with color mapped by column.
I'm trying to create bar plot with labels on bars. Position of labels and color of labels depends on column of dataframe. Also, I would like to color bars by column.
here is my question on stack overflow: https://stackoverflow.com/questions/75161245/how-to-use-seaborn-objects-scale-with-two-visualisations-with-same-kwargs
Is there a way to do this?
Thank you for answer | 1medium
|
Title: Is it possible to load my own quantized model from local
Body: Here is the code I tried for the coreference resolution
```
from allennlp.predictors.predictor import Predictor
model_url = 'https://storage.googleapis.com/pandora-intelligence/models/crosslingual-coreference/minilm/model.tar.gz'
predictor = Predictor.from_path(model_url)
text = "Eva and Martha didn't want their friend Jenny \
to feel lonely so they invited her to the party."
prediction = predictor.predict(document=text)
print(prediction['clusters'])
print(predictor.coref_resolved(text))
```
And it worked well I got the output with solved coreference. like below
```
Eva and Martha didn't want Eva and Martha's friend Jenny to feel lonely so Eva and Martha invited their friend Jenny to the party.
```
Now I have quantized the model used here (https://storage.googleapis.com/pandora-intelligence/models/crosslingual-coreference/minilm/model.tar.gz) and the new quantized model is stored in a specific path in my local machine.
Shall I use that customized(quantized) model from my local path in ```model_url``` value and use this prediction command like below?
```
model_url = <Path to the quantized model in my local machine>
predictor = Predictor.from_path(model_url)
```
| 1medium
|
Title: Day 10 Exercises level 3
Body: I'm trying to solve exercise number 3, but I can't iterate through the file: `countries_data.py` I got this error message: `non iterable value is used in an iterating context` any ideas that could help me to solve this issue? and also How can I iterate a list of dictionaries like the one in `countries_data.py`. Thanks in advance! | 1medium
|
Title: can I run the code in windows and without GPU
Body: can I run the code in windows and without GPU? | 0easy
|
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