text
stringlengths 20
57.3k
| labels
class label 4
classes |
|---|---|
Title: 'TextClassifier' object has no attribute 'embeddings'
Body: TARSClassifier.load error
AttributeError Traceback (most recent call last)
<ipython-input-13-710c2b4d40e4> in <module>
----> 1 tars = TARSClassifier.load('/content/drive/MyDrive/Text_classification/final-model.pt')
2 frames
/usr/local/lib/python3.7/dist-packages/flair/nn/model.py in load(cls, model_path)
147 state = torch.load(f, map_location="cpu")
148
--> 149 model = cls._init_model_with_state_dict(state)
150
151 if "model_card" in state:
/usr/local/lib/python3.7/dist-packages/flair/models/tars_model.py in _init_model_with_state_dict(cls, state, **kwargs)
739 label_dictionary=state.get("label_dictionary"),
740 label_type=state.get("label_type", "default_label"),
--> 741 embeddings=state.get("tars_model").embeddings,
742 num_negative_labels_to_sample=state.get("num_negative_labels_to_sample"),
743 **kwargs,
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in __getattr__(self, name)
1206 return modules[name]
1207 raise AttributeError("'{}' object has no attribute '{}'".format(
-> 1208 type(self).__name__, name))
1209
1210 def __setattr__(self, name: str, value: Union[Tensor, 'Module']) -> None:
AttributeError: 'TextClassifier' object has no attribute 'embeddings' | 1medium
|
Title: How to plot Matplotlib's surfaces?
Body: I'm exploring the possibility to use K3D-Jupyter as a plotting library for [SymPy](https://github.com/sympy/sympy/), instead of relying on Matplotlib which is quite slow in a notebook. However, it seems like there is no function/object capable of using the data format used by [Matplotlib's `plot_surface`](https://matplotlib.org/stable/api/_as_gen/mpl_toolkits.mplot3d.axes3d.Axes3D.html#mpl_toolkits.mplot3d.axes3d.Axes3D.plot_surface), in which `x, y, z` are two dimensional arrays.
I've seen [K3D's `surface`](https://k3d-jupyter.org/k3d.html?highlight=surface#k3d.factory.surface), but I think it assumes a uniform grid spacing between `xmin, xmax` and `ymin, ymax`.
What would be the best way to plot matplotlib's surfaces with K3D? I'm going to drop a couple of example on what I'd like to achieve.
**Example 1:**
```
from sympy import *
from sympy.plotting.plot import plot3d, plot3d_parametric_surface
var("x, y")
r = sqrt(x**2 + y**2)
expr = cos(r) * exp(-r / 10)
p = plot3d(expr)
s = p._series[0]
xx, yy, zz = s.get_meshes()
```
Here, `xx, yy, zz` contains the numerical data used by Matplotib to draw the surface. Note that for each `(x, y)` there is one `z`.
**Example 2:**
```
p = plot3d_parametric_surface(cos(u + v), sin(u - v), u - v, (u, -5, 5), (v, -5, 5))
s2 = p._series[0]
xx, yy, zz = s2.get_meshes()
```
Note that for each `(x, y)` there could be multiple values for `z`.
| 1medium
|
Title: Support stable diffusion model
Body: can i use stable diffusion model with petals? | 1medium
|
Title: RAM consumption of TimeSeriesDataset
Body: I have a dataframe that consumes aprox 10 G in memory. when i try to build the TimeSeriesDataset, it consumes >30G in memory (making explode my RAM). I know It makes sense because the time series dataset is a bigger structure than the dataframe.
How much can the memory consumption grow when building the time series dataset? Like a 4x? I would like to have an estimation to know how to reduce the original dataframe. Is there any way to make TimeSeriesDataset consume less RAM?
Thanks @jdb78
| 1medium
|
Title: size mismatch error
Body: When I run the "python3 app.py" for demo, it cannot load the pretrained model naver-clova-ix/donut-base-finetuned-docvqa, there is a size miss match error
pretrained_model = DonutModel.from_pretrained(args.pretrained_path)
File "/home/local/Project/chart/donut/donut/model.py", line 597, in from_pretrained
model = super(DonutModel, cls).from_pretrained(pretrained_model_name_or_path, revision="official", *model_args, **kwargs)
File "/home/local/.local/lib/python3.10/site-packages/transformers/modeling_utils.py", line 3091, in from_pretrained
) = cls._load_pretrained_model(
File "/home/local/.local/lib/python3.10/site-packages/transformers/modeling_utils.py", line 3532, in _load_pretrained_model
raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}")
RuntimeError: Error(s) in loading state_dict for DonutModel:
size mismatch for encoder.model.layers.1.downsample.norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for encoder.model.layers.1.downsample.norm.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for encoder.model.layers.1.downsample.reduction.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([256, 512]).
size mismatch for encoder.model.layers.2.downsample.norm.weight: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for encoder.model.layers.2.downsample.norm.bias: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for encoder.model.layers.2.downsample.reduction.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([512, 1024]).
You may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method.
| 1medium
|
Title: quiet mode execution
Body: Currently there are a ton of messages are printed. Is there a way to mute all or part of the messages? | 1medium
|
Title: The resend_activation endpoint discloses if a user with a given email exists
Body: The `resend_activation` endpoint returns a 400 response if the given email does not belong to an (inactive) user.
This endpoint re-uses the password reset serializer (#555) but does not respect the `PASSWORD_RESET_SHOW_EMAIL_NOT_FOUND` setting because of these lines:
https://github.com/sunscrapers/djoser/blob/c62371e3f9a8bbad2eaf55ffd0efad6eb6c02f26/djoser/views.py#L208-L209
All settings related to disclosing email default to `False`:
```
PASSWORD_RESET_SHOW_EMAIL_NOT_FOUND
USERNAME_RESET_SHOW_EMAIL_NOT_FOUND
```
`resend_activation` shouldn't break this default.
P.S.
These settings don't work as advertised by the way, setting one to True has the effect of also toggling the other:
https://github.com/sunscrapers/djoser/blob/c62371e3f9a8bbad2eaf55ffd0efad6eb6c02f26/djoser/serializers.py#L145-L149
| 1medium
|
Title: ModuleNotFoundError: No module named 'models.yolo'.
Body: ### Search before asking
- [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions.
### Question
I have finetuned my model on google colab and I have downloaded best.py model and save it locally. after that I run below code
`from ultralytics import YOLO
model = YOLO("./models/best.pt")
result = model.predict("input_videos/image.png")`
I make sure about my model path and Input Image path. Also I added __init__.py file in my models folder. Also I have again installed ultralytics library. I know I can run `import os
result = os.system("python yolov5/detect.py --weights models/last.pt --img 640 --conf 0.8 --source input_videos/input_video.mp4")` This code. But this code does not give the integer as a result. So I don't know how to do prediction and detect it's location.
### Additional
I am using python version 3.12.5 and for ultralytics I am using version 8.2.87. I run this code in CPU. I also run the same type of code for detecting person without finetuning model i.e using your models. In that case I got the correct result. | 1medium
|
Title: [Bug]: Auto Run script fails with error
Body: ### Commit before submitting
- [x] I understand that Issues are used to provide feedback and solve problems, not to complain in the comments section, and will provide more information to help solve the problem.
- [x] I have checked the top Issue and searched for existing [open issues](https://github.com/yeongpin/cursor-free-vip/issues) and [closed issues](https://github.com/yeongpin/cursor-free-vip/issues?q=is%3Aissue%20state%3Aclosed%20), and found no similar issues.
- [x] I have filled out a short and clear title, so that developers can quickly determine the general problem when browsing the Issue list. Not "a suggestion", "stuck", etc.
### Platform
macOS ARM64
### Version
1.7.12
### Description
fails to auto run script (manually download works only)
### Related log output
```shell
curl -fsSL https://raw.githubusercontent.com/yeongpin/cursor-free-vip/main/scripts/install.sh -o install.sh && chmod +x install.sh && ./install.sh
curl: (56) Failure writing output to destination, passed 1369 returned 4294967295
``` | 1medium
|
Title: Bug: Spigo demo in Python3
Body: Change import to:
```
try:
import urllib.request as urllib2
except ImportError:
import urllib2
``` | 0easy
|
Title: Issue running vwap with dataframe index from yFinance data
Body: **Which version are you running? The lastest version is on Github. Pip is for major releases.**
```python
import pandas_ta as ta
print(ta.version)
```
0.3.14b0
**Do you have _TA Lib_ also installed in your environment?**
```sh
$ pip list
```
no
**Did you upgrade? Did the upgrade resolve the issue?**
```sh
$ pip install -U git+https://github.com/twopirllc/pandas-ta
```
yes but didn't resolve the issue
**Describe the bug**
I'm trying to run the simple example to calculate the vwap. Since it's provided, the vwap requires the datetime indexing.
However, the data coming via yFinance doesn't have this column and we get the key error for the dataframe
**To Reproduce**
```python
import pandas as pd
import pandas_ta as ta
df = pd.DataFrame()
df = df.ta.ticker("aapl", period="9d", interval="5m")
df.set_index(pd.DatetimeIndex(df["datetime"]), inplace=True)
print(df.columns)
df.ta.vwap(append=True)
```
**Screenshots**
Thanks for using Pandas TA!
| 1medium
|
Title: generate template error: Stopping generation because post_gen_project hook script didn't exit successfully
Body: # Image
# Error Info
`
E:\Code>fastapi_template
Project name: fastapi_template_test
Project description:
Removing resources for disabled feature GraphQL API...
Removing resources for disabled feature Kafka support...
Removing resources for disabled feature Kubernetes...
Removing resources for disabled feature Migrations...
Removing resources for disabled feature Gitlab CI...
Removing resources for disabled feature Dummy model...
Removing resources for disabled feature Self-hosted swagger...
Removing resources for disabled feature Tortoise ORM...
Removing resources for disabled feature Ormar ORM...
Removing resources for disabled feature PsycoPG...
Removing resources for disabled feature Piccolo...
Removing resources for disabled feature Postgresql DB...
Removing resources for disabled feature Opentelemetry support...
Removing resources for disabled feature SQLite DB...
cleanup complete!
โญ Placing resources nicely in your new project โญ
Resources are happy to be where they are needed the most.
Git repository initialized.
warning: in the working copy of 'fastapi_template_test/static/docs/redoc.standalone.js', LF will be replaced by CRLF the next time Git touches it
warning: in the working copy of 'fastapi_template_test/static/docs/swagger-ui-bundle.js', LF will be replaced by CRLF the next time Git touches it
warning: in the working copy of 'fastapi_template_test/static/docs/swagger-ui.css', LF will be replaced by CRLF the next time Git touches it
Added files to index.
Traceback (most recent call last):
File "C:\Users\pc\AppData\Local\Temp\tmpo5ko_okk.py", line 74, in <module>
init_repo()
File "C:\Users\pc\AppData\Local\Temp\tmpo5ko_okk.py", line 64, in init_repo
subprocess.run(["poetry", "install", "-n"])
File "C:\Python311\Lib\subprocess.py", line 546, in run
with Popen(*popenargs, **kwargs) as process:
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Python311\Lib\subprocess.py", line 1022, in __init__
self._execute_child(args, executable, preexec_fn, close_fds,
File "C:\Python311\Lib\subprocess.py", line 1491, in _execute_child
hp, ht, pid, tid = _winapi.CreateProcess(executable, args,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [WinError 2] ็ณป็ปๆพไธๅฐๆๅฎ็ๆไปถใ
Stopping generation because post_gen_project hook script didn't exit successfully
`
# Context Info
Python: 3.11.0 (main, Oct 24 2022, 18:26:48) [MSC v.1933 64 bit (AMD64)] on win32
Pip Package:
`C:\Users\pc>pip list
Package Version
------------------ ---------
arrow 1.2.3
binaryornot 0.4.4
certifi 2022.9.24
cfgv 3.3.1
chardet 5.0.0
charset-normalizer 2.1.1
click 8.1.3
colorama 0.4.6
cookiecutter 1.7.3
distlib 0.3.6
fastapi-template 3.3.10
filelock 3.8.0
identify 2.5.8
idna 3.4
Jinja2 3.1.2
jinja2-time 0.2.0
MarkupSafe 2.1.1
nodeenv 1.7.0
pip 22.3
platformdirs 2.5.3
poyo 0.5.0
pre-commit 2.20.0
prompt-toolkit 3.0.32
pydantic 1.10.2
python-dateutil 2.8.2
python-slugify 6.1.2
PyYAML 6.0
requests 2.28.1
setuptools 65.5.0
six 1.16.0
termcolor 1.1.0
text-unidecode 1.3
toml 0.10.2
typing_extensions 4.4.0
urllib3 1.26.12
virtualenv 20.16.6
wcwidth 0.2.5` | 1medium
|
Title: ManyToMany for SQLAlchemy
Body: I've trying to get M2M field working with SQLAlchemy. Following the documentation
```
class LandingPageFactory(alchemy.SQLAlchemyModelFactory):
FACTORY_FOR = LandingPage
FACTORY_SESSION = db.session
name = Sequence(lambda n: u'Landing Page %d' % n)
class LPRotatorFactory(alchemy.SQLAlchemyModelFactory):
FACTORY_FOR = LPRotator
FACTORY_SESSION = db.session
name = Sequence(lambda n: u'Landing Page %d' % n)
@post_generation
def landing_pages(self, create, extracted, **kwargs):
if not create:
return
if extracted:
for landing_page in extracted:
self.landing_pages.add(landing_page)
```
Then if I try to set it up like so
```
lp1 = LandingPageFactory()
lp2 = LandingPageFactory()
db.session.commit() # All good here
lpr = LPRotatorFactory(landing_pages=(lp1, lp2))
db.session.commit() # This throws the error
```
This throws an Attribute error.
```
self.landing_pages.add(landing_page)
AttributeError: 'InstrumentedList' object has no attribute 'add'
```
I noticed all the docs and examples use Django, but didn't see anything too specific. Am I doing something wrong?
Thanks
| 1medium
|
Title: module 'keras.utils' has no attribute 'PyDataset'
Body: I have correctly installed version 3.0.5 of keras and used pytorch for the backend, but it always prompts module 'keras. utils' has no attribute' PyDataset '. How can I solve this problem? | 1medium
|
Title: favorable
Body: coolest idea | 3misc
|
Title: top10_floatholders ๅ top10_holders ๆฅๅฃ็ผบๅฐ 2006 ๅนดๆฐๆฎ
Body: top10_floatholders ๅ top10_holders ๆฅๅฃ็ผบๅฐ 2006 ๅนดๆฐๆฎ็ 4 ไธชๅญฃๅบฆๆฐๆฎ๏ผๅ
ถไปๅนดไปฝๆญฃๅธธ
tushare id: 224776
| 1medium
|
Title: Geocoder with own locations
Body: Greetings, i slightly edited geocoder such that it can now accept your own places.
https://github.com/JohnyCarrot/folium-geocoder-own-locations
Hope someone find it usefull. | 3misc
|
Title: How to compute metrics for each class in multi class segmentation
Body: I would compute the metrics individually for each class so I would like to have in output a (1xC) vector where C is the number of classes, I was trying like this but it throws me an error:
```
output = torch.rand([10, 3, 256, 256])
target = torch.rand([10, 1, 256, 256]).round().long()
# first compute statistics for true positives, false positives, false negative and
# true negative "pixels"
tp, fp, fn, tn = smp.metrics.get_stats(output, target, mode='multi class', num_classes = 3)
# then compute metrics with required reduction (see metric docs)
iou_score = smp.metrics.iou_score(tp, fp, fn, tn, reduction="macro-imagewise")
f1_score = smp.metrics.f1_score(tp, fp, fn, tn, reduction="macro-imagewise")
false_negatives = smp.metrics.false_negative_rate(tp, fp, fn, tn, reduction=None)
recall = smp.metrics.recall(tp, fp, fn, tn, reduction=None)
```
The error:
```
ValueError: For ``multiclass`` mode ``target`` should be one of the integer types, got torch.float32.
``` | 1medium
|
Title: Websockets RuntimeError "This event loop is already running"
Body: When I'm trying to run a websocket, then in some time stop it, and run a new websocket, the following error occurs:
Exception in thread Thread-2:
Traceback (most recent call last):
File "D:\python\Python39\lib\threading.py", line 950, in _bootstrap_inner
self.run()
File "D:\python\Python39\lib\site-packages\binance\threaded_stream.py", line 59, in run
self._loop.run_until_complete(self.socket_listener())
File "D:\python\Python39\lib\asyncio\base_events.py", line 618, in run_until_complete
self._check_running()
File "D:\python\Python39\lib\asyncio\base_events.py", line 578, in _check_running
raise RuntimeError('This event loop is already running')
RuntimeError: This event loop is already running
D:\python\Python39\lib\threading.py:952: RuntimeWarning: coroutine 'ThreadedApiManager.socket_listener' was never awaited
self._invoke_excepthook(self)
RuntimeWarning: Enable tracemalloc to get the object allocation traceback
This is a part of code which I use to run websocket:
`twm = ThreadedWebsocketManager(self.main_window.api_key, self.main_window.api_secret)`
`twm.start()`
`current_candle_websocket = twm.start_kline_futures_socket(callback=self.handle_candle_message, symbol=self.symbol, interval=Client.KLINE_INTERVAL_5MINUTE)`
This is a part of code which I use to stop websocket:
`twm.stop_socket(current_candle_websocket)`
`twm.stop()`
`twm = ''`
I use Python 3.9. The error didn't occur on python-binance 1.0.15, but since some API features are retired I can no longer use this version and updated python-binance to 1.0.19, and after that I am getting this error. | 1medium
|
Title: ไปฅ็้ขๆจกๅผๅฏๅจ๏ผ็จๅบๆชๅๅบๅๅ
ณ้ญ้ๆฐๅฏๅจ๏ผๆ ๆณๆพ็คบๅบ็้ข
Body: **Summary[้ฎ้ข็ฎ่ฟฐ๏ผไธๅฅ่ฏ๏ผ]**
ไปฅ็้ขๆจกๅผๅฏๅจ๏ผ็จๅบๆชๅๅบๅๅ
ณ้ญ้ๆฐๅฏๅจ๏ผๆ ๆณๆพ็คบๅบ็้ข
**Env & To Reproduce[ๅค็ฐไธ็ฏๅข]**
python3.7.9
`python demo_toolbox.py -d dataset` ไปฅ็้ขๆจกๅผๅฏๅจๅ๏ผ็นๅป load above ็ถๅๅจๅณไพง่พๅ
ฅๆก่พๅ
ฅๆๅญๆถๅๅกไฝ๏ผ็จๅบๆชๅๅบใๅ
ณ้ญๅๅๆฌกๅฏๅจ็จๅบๅไธๅบ็ฐ็้ขใ
**Screenshots[ๆชๅพ๏ผๅฆๆ๏ผ]**
ๅๆฌกๅฏๅจไผๅกๅจ่ฟ้
| 1medium
|
Title: Fix error on user register
Body: review user forms, register and login
| 1medium
|
Title: Grads w.r.t. weights of `MixtureGeneral` Distribution are giving `nan`s
Body: Hi,
We have created some models where we estimate the weights of the `MixtureGeneral` distribution. However, when computing the gradient of this argument, we are encountering `nan` values. We enabled `jax.config.update("debug_nan", True)` to diagnose the issue, and it pointed to the following line:
https://github.com/pyro-ppl/numpyro/blob/8e9313fd64a34162bc1c08b20ed310373e82e347/numpyro/distributions/mixtures.py#L152
I suspect that after the implementation of https://github.com/pyro-ppl/numpyro/pull/1791, extra care is needed to handle `inf` and `nan` values, possibly by using a double `where` for a safe `logsumexp`.
> [!IMPORTANT]
> This is an urgent issue, so a prompt response would be greatly appreciated. | 2hard
|
Title: [Feature Request] dcc.Slider - Enable user to define slider direction
Body:
I have an application where I am like to use sliders to crop an image (heatmap graph). The image's (0,0) is defined as the top left of the image. I'd like the y slider to start with 0 at the top, and end with the height of the image at the bottom. Currently, I cannot find a method to invert the slider to allow for the slider to go {top: low, bottom: high}, instead of the default {top: high, bottom: low}
**Describe the solution you'd like**
A slider where the direction from minimum to maximum could be swapped.
**Describe alternatives you've considered**
I tried setting the min to be higher than the max, did not work.
I've tried flipping the slider with CSS and the result is... erratic
| 1medium
|
Title: Update c-ares version to 1.19.1
Body: * gevent version: 22.10.2 from PyPI
* Python version: python 3.11.3
* Operating System: docker python:latest
### Description:
Update c-ares version to 1.19.1 (it is the latest version as of today: https://c-ares.org). It is not a bug with the gevent itself, but with the depedency c-ares. These vulnerabilities exists:
* https://nvd.nist.gov/vuln/detail/CVE-2023-32067
* https://nvd.nist.gov/vuln/detail/CVE-2022-4904
* https://nvd.nist.gov/vuln/detail/CVE-2023-31124
Version 1.19.1 seems fine (at least considering these vulnerabilities). Gevent is currently using 1.18.1: https://github.com/gevent/gevent/blob/master/deps/c-ares/include/ares_version.h#L14. It would be nice to update the c-ares version to 1.19.1.
### What I've run:
I tried this using this Dockerile:
```python
FROM python
RUN pip3 install gevent==22.10.2
```
| 1medium
|
Title: training failed
Body: we have a large dataset that contains 1m tables..tained on yolov11x model..
```
def model_train(data_yaml_path):
model = YOLO('yolo11x.pt')
data = Path(data_yaml_path)
results = model.train(data=data, epochs=10, imgsz=800, patience=2, cls=0.25, box=0.05, project="final-table-detection",
device=[0, 1, 2, 3, 4, 5], batch=36)
```
trained only 3 epochs the model takes first epoch as best one..
..
but the result are worst..wht the reason @glenn-jocher | 2hard
|
Title: [Feature request] Add apply_to_batch
Body: There is request how to apply to video or to batch.
Albumentations was not originally designed to be applied to batch.
But it looks like we can add such functionality without too much pain, by just looping over frames with annotations.
Related to:
https://github.com/albumentations-team/albumentations/issues/465
https://github.com/albumentations-team/albumentations/issues/683
https://github.com/albumentations-team/albumentations/issues/1561 | 1medium
|
Title: Question - how do I mark fields as minLength 0 and nullable?
Body: I have an app using DRF 3.8.2, Django 1.11.16, and drf-yasg 1.10.2. Schema generation works, but I tried to add automated test cases to verify that the response matches the generated schema.
I have a CharField on a ModelSerializer that is for a model field that has null=True, blank=True. Despite this, drf-yasg appears to be generating a minLength: 1 requirement. Oracle makes no difference between NULL and empty string for VARCHAR2 and NVARCHAR2. DRF returns these fields as '', which I can change, but is there an easier to control this than a "NullableCharField" subclass?
| 1medium
|
Title: better dependency manager for project
Body: currently marzban use pip to manage dependencies.
using pip can lead to some problems and always we need some 3rd party programs as venv
we can replace pip with [uv ](https://docs.astral.sh/uv/) to avoid this and have better dependency manager. | 1medium
|
Title: parameter alpha in focal loss is not the same as that of paper
Body: ### Describe the bug
The parameter alpha in focal loss is a scalor, so it couldn't has the effect of balanced the different classes. I think alpha should be a tensor with n_classses element and every value refers to the weiht of this class
### Reproduction steps
```bash
Please refer to the description
```
### Expected behavior
Please refer to the description
### Environment
```shell
wget https://raw.githubusercontent.com/pytorch/pytorch/main/torch/utils/collect_env.py
# For security purposes, please check the contents of collect_env.py before running it.
python collect_env.py
```
- PyTorch Version (e.g., 1.0):
- OS (e.g., Linux):
- How you installed PyTorch (`conda`, `pip`, source):
- Build command you used (if compiling from source):
- Python version:
- CUDA/cuDNN version:
- GPU models and configuration:
- Any other relevant information:
```
### Additional context
_No response_ | 1medium
|
Title: Reconsider HttpClient interface
Body: As of now, the python `BaseHttpClient` look like this:
https://github.com/apify/crawlee-python/blob/beac9fa0eb415caafc04cdaef2888e77fad915e0/src/crawlee/http_clients/_base.py#L55
It has two methods, `send_request` and `crawl`. This is the first iteration of decoupled HTTP clients.
Later on, we refactored the JS version to use this one:
https://github.com/apify/crawlee/blob/f912b8b06da2bc4f3f3db508cc39c936a5c87f23/packages/core/src/http_clients/base-http-client.ts#L179
It also has two methods, `sendRequest` and `stream`. Unlike the python version, the signatures of those methods match quite well. It is worth noting that the two serious attempts at implementing this interface (so far) both couldn't manage to implement `stream` correctly. Although we could probably live without it in the most common case, streaming is paramount for downloading files (potentially large ones), which is a use case that we want to support.
We should simplify this interface and make it look the same in both versions. Any thoughts on how to achieve that? @vdusek @B4nan @barjin... and whoever else wants to chat :slightly_smiling_face: | 2hard
|
Title: ่ฎญ็ปๆถๅบ้๏ผRuntimeError: Error(s) in loading state_dict for Tacotron:
Body: Arguments:
run_id: mandarin
syn_dir: k:/mockingbird/datame/SV2TTS/synthesizer
models_dir: synthesizer/saved_models/
save_every: 1000
backup_every: 25000
log_every: 200
force_restart: False
hparams:
Checkpoint path: synthesizer\saved_models\mandarin\mandarin.pt
Loading training data from: k:\mockingbird\datame\SV2TTS\synthesizer\train.txt
Using model: Tacotron
Using device: cpu
Initialising Tacotron Model...
Trainable Parameters: 32.866M
Loading weights at synthesizer\saved_models\mandarin\mandarin.pt
Traceback (most recent call last):
File "synthesizer_train.py", line 37, in <module>
train(**vars(args))
File "K:\MockingBird\synthesizer\train.py", line 114, in train
model.load(weights_fpath, optimizer)
File "K:\MockingBird\synthesizer\models\tacotron.py", line 536, in load
self.load_state_dict(checkpoint["model_state"], strict=False)
File "f:\anaconda3\envs\mockingbird\lib\site-packages\torch\nn\modules\module.py", line 1482, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for Tacotron:
size mismatch for encoder_proj.weight: copying a param with shape torch.Size([128, 512]) from checkpoint, the shape in current model is torch.Size([128, 1024]).
size mismatch for decoder.attn_rnn.weight_ih: copying a param with shape torch.Size([384, 768]) from checkpoint, the shape in current model is torch.Size([384, 1280]).
size mismatch for decoder.rnn_input.weight: copying a param with shape torch.Size([1024, 640]) from checkpoint, the shape in current model is torch.Size([1024, 1152]).
size mismatch for decoder.stop_proj.weight: copying a param with shape torch.Size([1, 1536]) from checkpoint, the shape in current model is torch.Size([1, 2048]).
ๆๅทฒ็ปๆsymbol้็้ฃ่กๅญ็ฌฆๆนๆๆง็็้ฃไธชไบ๏ผ่ฟๆฏๆฅ่ฟไธช้ใๆ่ฟ้็จ็ๆฏ่ชๅทฑ็ๆฐๆฎ๏ผๆจกไปฟaishell3็็ปๆๆพไบ๏ผๅทฒ็ปๅไบ pre.py ็้ขๅค็๏ผๅจๅผๅง่ฎญ็ป่ฟไธๆญฅ็ๆถๅๅฐฑๅบไบ่ฟไธช้ | 1medium
|
Title: Cannot install cartopy in Scientific Linux
Body: ### Description
<!-- Please provide a general introduction to the issue/proposal. -->
Cartopy cannot be installed on Scientific Linux because the newest available version of `proj` is 4.8.0 and cartopy requires 4.9.0. I'm not sure why `setup.py` checks the system-level versions of `proj` and `geos` instead of the versions in the current virtualenv though...
<!--
If you are reporting a bug, attach the *entire* traceback from Python.
If you are proposing an enhancement/new feature, provide links to related articles, reference examples, etc.
If you are asking a question, please ask on StackOverflow and use the cartopy tag. All cartopy
questions on StackOverflow can be found at https://stackoverflow.com/questions/tagged/cartopy
-->
#### Code to reproduce
```
pip install cartopy
```
#### Traceback
```
Collecting cartopy
Using cached Cartopy-0.17.0.tar.gz (8.9 MB)
Installing build dependencies ... done
Getting requirements to build wheel ... error
ERROR: Command errored out with exit status 1:
command: /home/jba/venv/gdal3/bin/python3 /home/jba/venv/gdal3/lib64/python3.6/site-packages/pip/_vendor/pep517/_in_process.py get_requires_for_build_wheel /tmp/tmp312hzzaa
cwd: /tmp/pip-install-u9agv4jo/cartopy
Complete output (1 lines):
Proj version 4.8.0 is installed, but cartopy requires at least version 4.9.0.
----------------------------------------
ERROR: Command errored out with exit status 1: /home/jba/venv/gdal3/bin/python3 /home/jba/venv/gdal3/lib64/python3.6/site-packages/pip/_vendor/pep517/_in_process.py get_requires_for_build_wheel /tmp/tmp312hzzaa Check the logs for full command output.
```
<details>
<summary>Full environment definition</summary>
<!-- fill in the following information as appropriate -->
### Operating system
$ cat /proc/version
Linux version 3.10.0-1062.18.1.el7.x86_64 ([email protected]) (gcc version 4.8.5 20150623 (Red Hat 4.8.5-39) (GCC) ) #1 SMP Tue Mar 17 10:44:42 CDT 2020
$ cat /etc/*-release
NAME="Scientific Linux"
VERSION="7.4 (Nitrogen)"
ID="scientific"
ID_LIKE="rhel centos fedora"
VERSION_ID="7.4"
PRETTY_NAME="Scientific Linux 7.4 (Nitrogen)"
ANSI_COLOR="0;31"
CPE_NAME="cpe:/o:scientificlinux:scientificlinux:7.4:GA"
HOME_URL="http://www.scientificlinux.org//"
BUG_REPORT_URL="mailto:[email protected]"
REDHAT_BUGZILLA_PRODUCT="Scientific Linux 7"
REDHAT_BUGZILLA_PRODUCT_VERSION=7.4
REDHAT_SUPPORT_PRODUCT="Scientific Linux"
REDHAT_SUPPORT_PRODUCT_VERSION="7.4"
Scientific Linux release 7.4 (Nitrogen)
Scientific Linux release 7.4 (Nitrogen)
Scientific Linux release 7.4 (Nitrogen)
### Cartopy version
0.17.0
### conda list
```
n/a
```
### pip list
```
Package Version Location
--------------------- ------- ----------------------
arrow 0.15.5
attrs 19.3.0
backcall 0.1.0
chardet 3.0.4
click 7.1.1
cloudpickle 1.3.0
colorama 0.4.3
coverage 5.0.3
cycler 0.10.0
Cython 0.29.15
dask 2.12.0
decorator 4.4.2
entrypoints 0.3
farmlib 1.5.9 /home/jba/work/farmlib
flake8 3.7.9
flake8-builtins 1.4.2
flake8-commas 2.0.0
flake8-comprehensions 3.2.2
flake8-docstrings 1.5.0
flake8-logging-format 0.6.0
flake8-polyfill 1.0.2
flake8-print 3.1.4
flake8-string-format 0.3.0
future 0.18.2
fuzzywuzzy 0.17.0
GDAL 3.0.4
importlib-metadata 1.5.0
ipython 7.1.1
ipython-genutils 0.2.0
jbafarm 1.7.3 /home/jba/work/farmmap
jedi 0.16.0
jsonschema 3.2.0
kiwisolver 1.1.0
litmus 1.3.4
matplotlib 3.2.0
mccabe 0.6.1
mock 4.0.2
networkx 2.4
nose 1.3.7
nosexcover 1.0.11
numpy 1.18.1
packaging 20.3
pandas 0.25.0
parso 0.6.2
pep8-naming 0.9.1
pexpect 4.8.0
pickleshare 0.7.5
Pillow 7.0.0
pip 20.0.2
proj 0.1.0
prompt-toolkit 2.0.10
psycopg2-binary 2.8.4
ptyprocess 0.6.0
pycodestyle 2.5.0
pydocstyle 3.0.0
pyflakes 2.1.1
Pygments 2.6.1
pyparsing 2.4.6
pyproj 2.1.3
pyrsistent 0.15.7
python-dateutil 2.8.1
python-Levenshtein 0.12.0
pytz 2019.3
PyWavelets 1.0.3
Rtree 0.9.4
scikit-image 0.14.5
scipy 1.2.1
setuptools 39.2.0
Shapely 1.7.0
simplejson 3.17.0
six 1.14.0
snowballstemmer 2.0.0
toolz 0.10.0
tqdm 4.43.0
traitlets 4.3.3
wcwidth 0.1.8
wheel 0.34.2
zipp 3.1.0
```
</details>
| 1medium
|
Title: Loading up Json_files built and trained in Keras 2 for Keras 3
Body: Using Keras 3, I am trying to load up a built and trained model from Keras 2 API that is stored in .json with weights stored in .h5. The model file is the following: [cnn_model.json](https://github.com/user-attachments/files/16462021/cnn_model.json). Since model_from_json does not exist in Keras 3, I rewrote the function from the Keras 2 API so that I can load the .json file. With Keras 3 (with torch backend), I am trying to load the model and the weights with the following code
```
import os
import keras
import json
os.environ["KERAS_BACKEND"] = "torch"
def model_from_json(json_string, custom_objects=None):
"""Parses a JSON model configuration string and returns a model instance.
Args:
json_string: JSON string encoding a model configuration.
custom_objects: Optional dictionary mapping names
(strings) to custom classes or functions to be
considered during deserialization.
Returns:
A Keras model instance (uncompiled).
model_config = json.loads(json_string)
return deserialize_keras_object(model_config, custom_objects=custom_objects)
def model_torch():
model_name = 'cnn_model' #model file name
model_file = model_name + '.json'
with open(model_file, 'r') as json_file:
print('USING MODEL:' + model_file)
loaded_model_json = json_file.read()
loaded_model = model_from_json(loaded_model_json)
loaded_model.load_weights(model_name + '.h5')
loaded_model.compile('sgd', 'mse')
if __name__ == "__main__":
model_torch()
```
However, when I run this code, I obtain the error below (as shown below). With this, I have the three following questions:
1. How does one possibly fix this error given that the model I want to load (in Keras 3) was built and trained in tensorflow-keras 2?
2. Is it better to rebuild the model in Keras using the load_model() function in Keras 3, and if so, how can you translate the weights from the .h5 file that was created in tensorflow-keras 2 to keras 3?
3. To rebuild how, how should one translate the json dictionary to actual code?
Error I obtain:
`
TypeError: Could not locate class 'Sequential'. Make sure custom classes are decorated with `@keras.saving.register_keras_serializable()`. Full object config: {'class_name': 'Sequential', 'config': {'name': 'sequential', 'layers': [{'class_name': 'Conv2D', 'config': {'name': 'conv2d_20', 'trainable': True, 'batch_input_shape': [None, 50, 50, 1], 'dtype': 'float32', 'filters': 32, 'kernel_size': [3, 3], 'strides': [1, 1], 'padding': 'valid', 'data_format': 'channels_last', 'dilation_rate': [1, 1], 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None, 'dtype': 'float32'}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {'dtype': 'float32'}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}}, {'class_name': 'Activation', 'config': {'name': 'activation_13', 'trainable': True, 'dtype': 'float32', 'activation': 'relu'}}, {'class_name': 'Conv2D', 'config': {'name': 'conv2d_21', 'trainable': True, 'dtype': 'float32', 'filters': 32, 'kernel_size': [3, 3], 'strides': [1, 1], 'padding': 'valid', 'data_format': 'channels_last', 'dilation_rate': [1, 1], 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None, 'dtype': 'float32'}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {'dtype': 'float32'}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}}, {'class_name': 'Activation', 'config': {'name': 'activation_14', 'trainable': True, 'dtype': 'float32', 'activation': 'relu'}}, {'class_name': 'MaxPooling2D', 'config': {'name': 'max_pooling2d_10', 'trainable': True, 'dtype': 'float32', 'pool_size': [2, 2], 'padding': 'valid', 'strides': [2, 2], 'data_format': 'channels_last'}}, {'class_name': 'Dropout', 'config': {'name': 'dropout_17', 'trainable': True, 'dtype': 'float32', 'rate': 0.25, 'noise_shape': None, 'seed': None}}, {'class_name': 'Conv2D', 'config': {'name': 'conv2d_22', 'trainable': True, 'dtype': 'float32', 'filters': 64, 'kernel_size': [3, 3], 'strides': [1, 1], 'padding': 'same', 'data_format': 'channels_last', 'dilation_rate': [1, 1], 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None, 'dtype': 'float32'}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {'dtype': 'float32'}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}}, {'class_name': 'Activation', 'config': {'name': 'activation_15', 'trainable': True, 'dtype': 'float32', 'activation': 'relu'}}, {'class_name': 'Conv2D', 'config': {'name': 'conv2d_23', 'trainable': True, 'dtype': 'float32', 'filters': 64, 'kernel_size': [3, 3], 'strides': [1, 1], 'padding': 'valid', 'data_format': 'channels_last', 'dilation_rate': [1, 1], 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None, 'dtype': 'float32'}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {'dtype': 'float32'}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}}, {'class_name': 'Activation', 'config': {'name': 'activation_16', 'trainable': True, 'dtype': 'float32', 'activation': 'relu'}}, {'class_name': 'MaxPooling2D', 'config': {'name': 'max_pooling2d_11', 'trainable': True, 'dtype': 'float32', 'pool_size': [2, 2], 'padding': 'valid', 'strides': [2, 2], 'data_format': 'channels_last'}}, {'class_name': 'Dropout', 'config': {'name': 'dropout_18', 'trainable': True, 'dtype': 'float32', 'rate': 0.25, 'noise_shape': None, 'seed': None}}, {'class_name': 'Flatten', 'config': {'name': 'flatten_8', 'trainable': True, 'dtype': 'float32', 'data_format': 'channels_last'}}, {'class_name': 'Dense', 'config': {'name': 'dense_15', 'trainable': True, 'dtype': 'float32', 'units': 512, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None, 'dtype': 'float32'}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {'dtype': 'float32'}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}}, {'class_name': 'Activation', 'config': {'name': 'activation_17', 'trainable': True, 'dtype': 'float32', 'activation': 'relu'}}, {'class_name': 'Dropout', 'config': {'name': 'dropout_19', 'trainable': True, 'dtype': 'float32', 'rate': 0.5, 'noise_shape': None, 'seed': None}}, {'class_name': 'Dense', 'config': {'name': 'dense_16', 'trainable': True, 'dtype': 'float32', 'units': 2, 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None, 'dtype': 'float32'}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {'dtype': 'float32'}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}}, {'class_name': 'Activation', 'config': {'name': 'activation_18', 'trainable': True, 'dtype': 'float32', 'activation': 'softmax'}}]}, 'keras_version': '2.2.4-tf', 'backend': 'tensorflow'}
`
| 2hard
|
Title: Instance Segmentation vs Object Detection
Body: I would like to find out whether it is better to use instance segmentation or object detection to classify vehicles and count them, in the case of traffic congestion.
From my experience, traffic congestion has a lot of occlusion for bounding box to be accurate, it may classify a car as a truck, and a truck as a car. I have a relatively large datasets, approx 7000 - 10000 images, it may be better to just use object detection as it will be easier to manage as the dataset gets larger
Image example:
If anyone can give some input, that would be greatly appreciated.
Thanks | 1medium
|
Title: Router cannot find endpoint with id parameter
Body: ### Issue
When trying to hit an endpoint with an integer variable in the URL, flask-restx responds with
`{
"message": "The requested URL was not found on the server. If you entered the URL manually please check your spelling and try again. You have requested this URI [/xman/statements/1] but did you mean /xman/statements/<int:statement_id> or /xman/statements ?"
}`
### **Code**
In my code, there is a namespace defined as such:
`api = Namespace("xman", description="External Managers related operations", path="/xman")`
and the resource is decorated as such:
<code>
@api.route("/statements/<int:statement_id>")
class StatementDetailsEndpoint(Resource):
@inject.autoparams()
def __init__(self, logging_service: LoggingService, data_service: EMDataService):
super().__init__()
self.logging_service = logging_service
self.data_service = data_service
def get(self, statement_id: int):
...get logic....
</code>
When **not** passing in a parameter into the route, everything works correctly. For example:
<code>
@api.route("/statements")
class StatementListEndpoint(Resource):
@inject.autoparams()
def __init__(self, logging_service: LoggingService, data_service: EMDataService):
super().__init__()
self.logging_service = logging_service
self.data_service = data_service
self.parser = reqparse.RequestParser()
def get(self):
...code....
</code>
### **Expected Behavior**
Expect a JSON response from endpoint
### **Actual Behavior**
Endpoint is not found
### **Environment**
- Python 3.6.7
- Flask 1.0.2
- Flask-RESTX 0.1.1
- Other installed Flask extensions
### **Additional Context**
The endpoint does appear in the Swagger documentation when the application is running and the same issue occurs when inputting a valid integer into the parameter box
| 1medium
|
Title: Code for simple for throws PydanticImportError
Body: <!--
Thanks for reporting a bug ๐ โค๏ธ
Before opening a new issue, please make sure that we do not have any duplicates already open. You can ensure this by searching the issue list for this repository. If there is a duplicate, please close your issue and add a comment to the existing issue instead. Also, be sure to check our documentation first.
-->
**Code for Simple Form in README.md cannot be run**
I was starting out with `streamlit-pydantic` and tried to run the code for the simple form given in the `README.md`, but I encountered an import error while running the simple example.
```python
2023-07-21 20:33:40.546 Uncaught app exception
Traceback (most recent call last):
File "/Users/ayangangopadhyay/Documents/Projects/Expenditure_Dashboard/.venv/lib/python3.10/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 552, in _run_script
exec(code, module.__dict__)
File "/Users/ayangangopadhyay/Documents/Projects/Expenditure_Dashboard/simple_form.py", line 4, in <module>
import streamlit_pydantic as sp
File "/Users/ayangangopadhyay/Documents/Projects/Expenditure_Dashboard/.venv/lib/python3.10/site-packages/streamlit_pydantic/__init__.py", line 9, in <module>
from .settings import StreamlitSettings
File "/Users/ayangangopadhyay/Documents/Projects/Expenditure_Dashboard/.venv/lib/python3.10/site-packages/streamlit_pydantic/settings.py", line 4, in <module>
from pydantic import BaseSettings
File "/Users/ayangangopadhyay/Documents/Projects/Expenditure_Dashboard/.venv/lib/python3.10/site-packages/pydantic/__init__.py", line 207, in __getattr__
return _getattr_migration(attr_name)
File "/Users/ayangangopadhyay/Documents/Projects/Expenditure_Dashboard/.venv/lib/python3.10/site-packages/pydantic/_migration.py", line 288, in wrapper
raise PydanticImportError(
pydantic.errors.PydanticImportError: `BaseSettings` has been moved to the `pydantic-settings` package. See https://docs.pydantic.dev/2.0.3/migration/#basesettings-has-moved-to-pydantic-settings for more details.
For further information visit https://errors.pydantic.dev/2.0.3/u/import-error
```
The code I am trying to run is exactly the one given for the simple form -
```python
import streamlit as st
from pydantic import BaseModel
import streamlit_pydantic as sp
class ExampleModel(BaseModel):
some_text: str
some_number: int
some_boolean: bool
data = sp.pydantic_form(key="my_form", model=ExampleModel)
if data:
st.json(data.json())
```
**Expected behaviour:**
Rendering a simple form with `streamlit-pydantic`.
**Steps to reproduce the issue:**
1. Create a `.venv`
2. Install required dependencies
3. Create the `simple_form.py` file using the above code or copying it from the `README.md`
4. `streamlit run simple_form.py`
-->
**Technical details:**
- Host Machine OS (Windows/Linux/Mac): Mac
- Browser (Chrome/Firefox/Safari): Arc/Mozilla
- Python Version: 3.10.8
- streamlit-pydantic Version: 0.6.0
- streamlit: 1.24.1
Please let me know if any further details are required, and I will be happy to provide them. Thanks! | 1medium
|
Title: Does tracking mode support NMS threshold?
Body: ### Search before asking
- [x] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/orgs/ultralytics/discussions) and found no similar questions.
### Question
I'm currently using YOLOv10 to track some objects and there are a lot of cases when two bounding boxes (of the same class) have a high IoU, I tried setting the NMS threshold ("iou" parameter) of the tracker very low it but doesn't change anything... I also tried setting a high NMS threshold (expecting a lot of overlapping BBs) but no matter what value i set, the predictions/tracking looks the same.
I tried to search about the parameters of the YOLOv10 tracker on the Ultralytics Docs and on Ultralytics GitHub but couldn't find anything about the NMS Threshold on the tracker. Is it implemented? Is the parameter name "iou" similar to the predict mode?
Can someone help me in this regard? Thanks!
### Additional
_No response_ | 1medium
|
Title: Card layouts break and overlap when in a container of a constrained size and expanded
Body: <details>
<summary>Software Version Info</summary>
```plaintext
panel == 1.6.1
```
</details>
#### Description of expected behavior and the observed behavior
I expect the cards to respect the overflow property of the container they are in and not overlap when expanded.
**Example 1 overflow: auto in column container**
https://github.com/user-attachments/assets/78a749ae-f82b-4836-9b04-85464a60210a
**Example 2 no overflow specified**
https://github.com/user-attachments/assets/d9d82ab0-a5c3-43f8-9925-07e996223b30
#### Complete, minimal, self-contained example code that reproduces the issue
**Example 1 overflow: auto in column container**
```python
import panel as pn
card1 = pn.layout.Card(pn.pane.Markdown("""
Lorem ipsum dolor sit amet, consectetur adipiscing elit.
Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
"""), title="Card 1")
card2 = pn.layout.Card(pn.pane.Markdown("""
In a world where technology and nature coexist,
the balance between innovation and preservation becomes crucial.
As we advance into the future, we must remember the lessons of the past,
embracing sustainable practices that honor our planet.
Together, we can forge a path that respects both progress and the environment,
ensuring a brighter tomorrow for generations to come.
"""), title="Card 2")
pn.Column(card1, card2, height=200, styles={'overflow': 'auto'}).servable()
```
**Example 2 no overflow specified**
```python
import panel as pn
card1 = pn.layout.Card(pn.pane.Markdown("""
Lorem ipsum dolor sit amet, consectetur adipiscing elit.
Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
"""), title="Card 1")
card2 = pn.layout.Card(pn.pane.Markdown("""
In a world where technology and nature coexist,
the balance between innovation and preservation becomes crucial.
As we advance into the future, we must remember the lessons of the past,
embracing sustainable practices that honor our planet.
Together, we can forge a path that respects both progress and the environment,
ensuring a brighter tomorrow for generations to come.
"""), title="Card 2")
pn.Column(card1, card2, height=200).servable()
```
I think this stems from the recalculation of this style, but I'm not quite sure how to get around it:
 | 1medium
|
Title: bug: "include" returns deleted relationships also
Body: I have a one to many relationship between Track and Session i.e. a Track can have multiple associated sessions but a session has only one associated Track.
**TrackSchema**
```Python
sessions = Relationship(attribute='sessions',
self_view='v1.track_sessions',
self_view_kwargs={'id': '<id>'},
related_view='v1.session_list',
related_view_kwargs={'track_id': '<id>'},
schema='SessionSchema',
many=True,
type_='session')
```
**Track model**
```python
sessions = db.relationship('Session', backref='track')
```
**SessionSchema**
```python
track = Relationship(attribute='track',
self_view='v1.session_track',
self_view_kwargs={'id': '<id>'},
related_view='v1.track_detail',
related_view_kwargs={'session_id': '<id>'},
schema='TrackSchema',
type_='track')
```
**Session Model**
```python
track_id = db.Column(db.Integer, db.ForeignKey('tracks.id', ondelete='CASCADE'))
```
When I try to include the sessions under a track using ```tracks/{{track_id}}?include=sessions```. It also returns the sessions which were deleted. Seems to be a bug in the library. | 1medium
|
Title: load_dataset("livecodebench/code_generation_lite", version_tag="release_v2") TypeError: 'NoneType' object is not callable
Body: ### Describe the bug
from datasets import load_dataset
lcb_codegen = load_dataset("livecodebench/code_generation_lite", version_tag="release_v2")
or
configs = get_dataset_config_names("livecodebench/code_generation_lite", trust_remote_code=True)
both error:
Traceback (most recent call last):
File "", line 1, in
File "/workspace/miniconda/envs/grpo/lib/python3.10/site-packages/datasets/load.py", line 2131, in load_dataset
builder_instance = load_dataset_builder(
File "/workspace/miniconda/envs/grpo/lib/python3.10/site-packages/datasets/load.py", line 1888, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
TypeError: 'NoneType' object is not callable
### Steps to reproduce the bug
from datasets import get_dataset_config_names
configs = get_dataset_config_names("livecodebench/code_generation_lite", trust_remote_code=True)
OR
lcb_codegen = load_dataset("livecodebench/code_generation_lite", version_tag="release_v2")
### Expected behavior
load datasets livecodebench/code_generation_lite
### Environment info
import datasets
version '3.3.2' | 1medium
|
Title: 'deepface.commons.functions' has no attribute 'preprocess_face'
Body: I'm trying to call DeepFace.stream() (library 0.0.78 installed from pip)
but get an error message
```
AttributeError: module 'deepface.commons.functions' has no attribute 'preprocess_face'
``` | 1medium
|
Title: Support multithreaded profiling
Body: Thanks for pyintrument - it's incredibly useful
I had need to trace a multithreaded python app and examine the relationship between threads. Obviously in some cases multithreading can be a little interesting in python, but in this particular case works well.
I have extended a fork of pyinstrument to support showing all child threads from the one where profiling starts. I get nice results with all threads separated which has been hugely helpful.
I'll file a PR and reference this issue. It's still a bit of a WIP, but i'd be curious to see if it looks reasonable to you. | 2hard
|
Title: MIME Type of the generated xlsx file
Body: Title: Issue with MIME Type of the generated xlsx file
Hello,
I am using XlsxWriter to generate excel files (obviously), no problem on the generation part but when I check the MIME type of the generated file, it's not what it should be.
I am using Python version 3.7 and XlsxWriter 1.1.5 and Excel version 16.23 (Excel for Mac).
Here is the code to demonstrates the problem:
```python
from xlsxwriter import Workbook
wb = Workbook("created_with_xlsxwriter.xlsx")
ws = wb.add_worksheet()
ws.write(0, 0, "Hello")
wb.close()
```
And when I check the MIME type of the file with the following command:
```
> file --mime-type -b created_with_xlsxwriter.xlsx
application/zip
```
If I do the same command on a file created with excel:
```
> file --mime-type -b created_with_excel.xlsx
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
```
The strangest thing is that if I open the generated file in Excel and do ```command + s``` on my Mac, and try the above command again, the MIME type become the good one.
Same happens if I open the file with a PHP xlsx library and save it again. | 1medium
|
Title: How patch chromedriver.exe with your package?
Body: Hello, could you please guide me on how I can patch the chromedriver.exe file using your package and use that Chrome driver file in a project that is not in Python? Thank you. | 1medium
|
Title: [BUG] xdeepfm error in AzureML test
Body: ### Description
<!--- Describe your issue/bug/request in detail -->
```
@pytest.mark.gpu
@pytest.mark.notebooks
@pytest.mark.integration
@pytest.mark.parametrize(
"syn_epochs, criteo_epochs, expected_values, seed",
[
(
15,
10,
***
"res_syn": ***"auc": 0.9716, "logloss": 0.699***,
"res_real": ***"auc": 0.749, "logloss": 0.4926***,
***,
42,
)
],
)
def test_xdeepfm_integration(
notebooks,
output_notebook,
kernel_name,
syn_epochs,
criteo_epochs,
expected_values,
seed,
):
notebook_path = notebooks["xdeepfm_quickstart"]
pm.execute_notebook(
notebook_path,
output_notebook,
kernel_name=kernel_name,
parameters=dict(
EPOCHS_FOR_SYNTHETIC_RUN=syn_epochs,
EPOCHS_FOR_CRITEO_RUN=criteo_epochs,
BATCH_SIZE_SYNTHETIC=1024,
BATCH_SIZE_CRITEO=1024,
RANDOM_SEED=seed,
),
)
results = sb.read_notebook(output_notebook).scraps.dataframe.set_index("name")[
"data"
]
for key, value in expected_values.items():
> assert results[key]["auc"] == pytest.approx(value["auc"], rel=TOL, abs=ABS_TOL)
E assert 0.5131 == 0.9716 ยฑ 9.7e-02
E comparison failed
E Obtained: 0.5131
E Expected: 0.9716 ยฑ 9.7e-02
```
### In which platform does it happen?
<!--- Describe the platform where the issue is happening (use a list if needed) -->
<!--- For example: -->
<!--- * Azure Data Science Virtual Machine. -->
<!--- * Azure Databricks. -->
<!--- * Other platforms. -->
### How do we replicate the issue?
<!--- Please be specific as possible (use a list if needed). -->
<!--- For example: -->
<!--- * Create a conda environment for pyspark -->
<!--- * Run unit test `test_sar_pyspark.py` with `pytest -m 'spark'` -->
<!--- * ... -->
See https://github.com/microsoft/recommenders/actions/runs/3459763061/jobs/5775521889
### Expected behavior (i.e. solution)
<!--- For example: -->
<!--- * The tests for SAR PySpark should pass successfully. -->
### Other Comments
| 2hard
|
Title: Populate Slack Welcome message
Body: Context:
Currently, we will send the user a welcome message when user joins the Ploomber Slack Workspace.
We may consider to provide the list of our current links.
Example - Ideal After:
<img width="768" alt="Screenshot 2023-01-02 at 9 26 32 PM" src="https://user-images.githubusercontent.com/9766828/210292932-b20a7a26-8591-4dd0-b1ce-95ff5fa4efd8.png">
Example - Currently Implementation:
<img width="901" alt="Screenshot 2023-01-02 at 9 30 49 PM" src="https://user-images.githubusercontent.com/9766828/210293134-f2b0e0d1-1948-4e2d-b494-fdca8440642a.png">
Action Item:
- [ ] Discuss if providing those links will help users to find the resource easier
- What to include in the list? Currently we can have Ploomber.io, Github link, Doc link, Blog link
- [ ] Modify the bot message with the new adding section | 1medium
|
Title: Unable to install requirements.txt missing
Body: ### Description
Unable to install on windows 10
### Steps to Reproduce
pip install Scrappy
**Expected behavior:** [What you expect to happen]
No Errores
**Actual behavior:** [What actually happens]
Collecting Scrappy
Using cached Scrappy-0.3.0.alpha.4.tar.gz (17 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
Collecting guessit (from Scrappy)
Using cached guessit-3.7.1-py3-none-any.whl (170 kB)
Collecting tvdb-api (from Scrappy)
Using cached tvdb_api-3.1.0.tar.gz (23 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
INFO: pip is looking at multiple versions of scrappy to determine which version is compatible with other requirements. This could take a while.
Collecting Scrappy
Using cached Scrappy-0.3.0.alpha.3.tar.gz (16 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
Using cached Scrappy-0.3.0.alpha.2.tar.gz (16 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
Using cached Scrappy-0.3.0.alpha.tar.gz (16 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
Using cached Scrappy-0.2.10.beta.14.tar.gz (16 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
Using cached Scrappy-0.2.10.beta.13.tar.gz (15 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
Using cached Scrappy-0.2.10.beta.12.tar.gz (15 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
Using cached Scrappy-0.2.10.beta.11.tar.gz (15 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... error
error: subprocess-exited-with-error
ร Getting requirements to build wheel did not run successfully.
โ exit code: 1
โฐโ> [17 lines of output]
Traceback (most recent call last):
File "C:\Users\test\AppData\Local\Programs\Python\Python310\lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 353, in <module>
main()
File "C:\Users\test\AppData\Local\Programs\Python\Python310\lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 335, in main
json_out['return_val'] = hook(**hook_input['kwargs'])
File "C:\Users\test\AppData\Local\Programs\Python\Python310\lib\site-packages\pip\_vendor\pyproject_hooks\_in_process\_in_process.py", line 118, in get_requires_for_build_wheel
return hook(config_settings)
File "C:\Users\test\AppData\Local\Temp\pip-build-env-8hm_g8ev\overlay\Lib\site-packages\setuptools\build_meta.py", line 341, in get_requires_for_build_wheel
return self._get_build_requires(config_settings, requirements=['wheel'])
File "C:\Users\test\AppData\Local\Temp\pip-build-env-8hm_g8ev\overlay\Lib\site-packages\setuptools\build_meta.py", line 323, in _get_build_requires
self.run_setup()
File "C:\Users\test\AppData\Local\Temp\pip-build-env-8hm_g8ev\overlay\Lib\site-packages\setuptools\build_meta.py", line 487, in run_setup
super(_BuildMetaLegacyBackend,
File "C:\Users\test\AppData\Local\Temp\pip-build-env-8hm_g8ev\overlay\Lib\site-packages\setuptools\build_meta.py", line 338, in run_setup
exec(code, locals())
File "<string>", line 4, in <module>
FileNotFoundError: [Errno 2] No such file or directory: 'requirements.txt'
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error
ร Getting requirements to build wheel did not run successfully.
โ exit code: 1
โฐโ> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
**Reproduces how often:** [What percentage of the time does it reproduce?]
| 1medium
|
Title: Incorrect model schema generation
Body: Hi, I made a dynamic model serializer `UserCouponSerializer`, the fields of which vary according to `fields` argument passed when it initialized. If `fields` is None, it includes all fields of its model. The code is below.
```
class DynamicFieldsModelSerializer(serializers.ModelSerializer):
def __init__(self, *args, **kwargs):
fields = kwargs.pop('fields', None)
super(DynamicFieldsModelSerializer, self).__init__(*args, **kwargs)
if fields is not None:
allowed = set(fields)
existing = set(self.fields)
for field_name in existing - allowed:
self.fields.pop(field_name)
```
```
class UserCouponSerializer(DynamicFieldsModelSerializer):
id = serializers.CharField(max_length=20, read_only=True)
class Meta:
model = UserCoupon
fields = '__all__'
```
After that, I made another serializer `UserCouponListSerializer`, which uses `UserCouponSerializer` for its own fields(active_coupon_list, inactive_coupon_list). That two fields only needs partial fields from `UserCouponSerializer`, and I specified `fields` argument as seen below.
```
class UserCouponListSerializer(serializers.Serializer):
active_coupon_list = serializers.ListField(
child=UserCouponSerializer(
fields=['affiliate', 'goods_name', 'period_end', 'status', 'id', 'pay_id']
)
)
inactive_coupon_list = serializers.ListField(
child=UserCouponSerializer(
fields=['affiliate', 'goods_name', 'period_end', 'status', 'id', 'pay_id']
)
)
```
However, the problem is, when I rendered API documentation, everything generated from `UserCouponSerializer` includes only the fields specified in `UserCouponListSerializer`, that is, 'affiliate', 'goods_name', 'period_end', 'status', 'id', and 'pay_id'.
I expected all fields of `UserCoupon` fields are rendered as I defined `UserCouponSerializer`.
Can I know the cause and get some help about that?
| 2hard
|
Title: Redesign SQLAlchemy dialect layout
Body: https://github.com/sqlalchemy/sqlalchemy/blob/main/README.dialects.rst | 2hard
|
Title: Django Admin not loading css
Body: **Server Info (please complete the following information):**
- OS: Debian 11
- Browser: Chrome
- RMM Version (as shown in top left of web UI): v0.15.12
**Installation Method:**
- [X] Standard
- [ ] Docker
**Describe the bug**
The Django Admin page doesn't load assets (css and js)
**To Reproduce**
Steps to reproduce the behavior:
1. Enable Django Admin Interface
2. Restart rmm
3. Go to Django Admin page
**Expected behavior**
The Django Admin Interface loading with the css and js.
**Screenshots**
| 1medium
|
Title: Gradio Block() can't detect imported library like numpy in jupyter notebook
Body: ### Describe the bug
Exact issue described here is still valid but I cannot reopen this ticket https://github.com/gradio-app/gradio/issues/3625
Gradio fails to pull imports on reload
### Have you searched existing issues? ๐
- [x] I have searched and found no existing issues
### Reproduction
```python
import gradio as gr
%%blocks
# Anything in under gr.NO_RELOAD won't be reloaded when the block is re-run (afaik)
if gr.NO_RELOAD:
import numpy as np
from transformers import pipeline
transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
def transcribe(stream, new_chunk):
sr, y = new_chunk
# Convert to mono if stereo
if y.ndim > 1:
y = y.mean(axis=1)
y = y.astype(np.float32)
y /= np.max(np.abs(y))
if stream is not None:
stream = np.concatenate([stream, y])
else:
stream = y
return stream, transcriber({"sampling_rate": sr, "raw": stream})["text"]
waveform_options = gr.WaveformOptions(
waveform_color="#01C6FF",
waveform_progress_color="#0066B4",
skip_length=2,
show_controls=False,
)
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
state = gr.State()
audio = gr.Audio(
sources=["microphone", "upload"],
show_download_button=True,
waveform_options=waveform_options,
streaming=True,
)
with gr.Row():
clear_btn = gr.ClearButton()
submit_btn = gr.Button("Submit", variant="primary")
output = gr.Textbox(label="Output")
submit_btn.click(
fn=transcribe,
inputs=[state, audio],
outputs=[state, output],
api_name="transcribe")
gr.on(
triggers=[audio.stream],
fn=transcribe,
inputs=[state, audio],
outputs=[state, output],
)
```
### Screenshot
_No response_
### Logs
```shell
Traceback (most recent call last):
File "/opt/conda/lib/python3.12/site-packages/gradio/queueing.py", line 625, in process_events
response = await route_utils.call_process_api(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.12/site-packages/gradio/route_utils.py", line 322, in call_process_api
output = await app.get_blocks().process_api(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.12/site-packages/gradio/blocks.py", line 2098, in process_api
result = await self.call_function(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.12/site-packages/gradio/blocks.py", line 1645, in call_function
prediction = await anyio.to_thread.run_sync( # type: ignore
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.12/site-packages/anyio/to_thread.py", line 56, in run_sync
return await get_async_backend().run_sync_in_worker_thread(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.12/site-packages/anyio/_backends/_asyncio.py", line 2461, in run_sync_in_worker_thread
return await future
^^^^^^^^^^^^
File "/opt/conda/lib/python3.12/site-packages/anyio/_backends/_asyncio.py", line 962, in run
result = context.run(func, *args)
^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.12/site-packages/gradio/utils.py", line 883, in wrapper
response = f(*args, **kwargs)
^^^^^^^^^^^^^^^^^^
File "<string>", line 16, in transcribe
NameError: name 'np' is not defined
```
### System Info
```shell
Gradio Environment Information:
------------------------------
Operating System: Linux
gradio version: 5.16.0
gradio_client version: 1.7.0
------------------------------------------------
gradio dependencies in your environment:
aiofiles: 23.2.1
anyio: 4.8.0
audioop-lts is not installed.
fastapi: 0.115.8
ffmpy: 0.5.0
gradio-client==1.7.0 is not installed.
httpx: 0.28.1
huggingface-hub: 0.28.1
jinja2: 3.1.5
markupsafe: 2.1.5
numpy: 2.1.3
orjson: 3.10.15
packaging: 24.2
pandas: 2.2.3
pillow: 11.1.0
pydantic: 2.10.6
pydub: 0.25.1
python-multipart: 0.0.20
pyyaml: 6.0.2
ruff: 0.9.6
safehttpx: 0.1.6
semantic-version: 2.10.0
starlette: 0.45.3
tomlkit: 0.13.2
typer: 0.15.1
typing-extensions: 4.12.2
urllib3: 2.3.0
uvicorn: 0.34.0
authlib; extra == 'oauth' is not installed.
itsdangerous; extra == 'oauth' is not installed.
gradio_client dependencies in your environment:
fsspec: 2024.9.0
httpx: 0.28.1
huggingface-hub: 0.28.1
packaging: 24.2
typing-extensions: 4.12.2
websockets: 14.2
```
### Severity
Blocking usage of gradio | 1medium
|
Title: [BUG] Beanie projection and Pydantic Schema do not play well together
Body: **Describe the bug**
Beanie projections are expecting an "_id" field, but Pydantic schema expect "id". This makes it impossible to use the same Schema and create duplicated code (unless Iโm missing the proper method to do it)
**To Reproduce**
```python
from motor.motor_asyncio import AsyncIOMotorClient
from pydantic import BaseModel, Field
from beanie import Document, init_beanie, PydanticObjectId
class Author(Document, BaseModel):
name: str
class AuthorRead(BaseModel):
id: PydanticObjectId = Field(alias="id")
name: str
class AuthorProjection(BaseModel):
# note the underscore
id: PydanticObjectId = Field(alias="_id")
name: str
async def example():
client = AsyncIOMotorClient("mongodb://localhost:27017")
await init_beanie(database=client.db_name, document_models=[Author])
dict = { "name": "Joe" }
joe = Author(**dict)
await joe.insert()
# created object contains "id"
print(AuthorRead(**joe.dict()))
# Beanie get, also give us an 'id' field, so AuthorRead expect id too
# (get() method does not have a project() method)
result = await Author.get(joe.id)
print(AuthorRead(**joe.dict()))
# projection is expecting "_id", not "id"
# we cannot use the same Schema!
result = await Author.find_all().project(AuthorProjection).to_list()
print(result)
await example()
```
**Expected behavior**
A way to use the same Schema for projections, like mapping _id to id during projection
| 1medium
|
Title: Empty report when running GPTR on Docker with Windows
Body: **Describe the bug**
I'm running gpt-researcher with Docker. When I try to use Deep Researcher, it runs with no problems, but the output that it produces is empty. So, it provides a doc file that is empty.
**To Reproduce**
I have not made any changes, just used the default settings.
**Expected behavior**
I would expect the provided document to contain text that answers the raised questions.
**Desktop (please complete the following information):**
- OS: Windows
- Browser: Chrome
- Version: 10
| 1medium
|
Title: Non-deterministic results based on group_max_seq_len in NaViT
Body: I'm having trouble understanding what the various parameters do, even after reading the source code.
Specifically, I'm wondering what group_max_seq_len does, and why it has non-deterministic results? For example:
```
v = NaViT(patch_size=60, **vit_args) # these are extremely large images
v(image_list, group_images = True, group_max_seq_len=1315)
tensor([[ 0.5456, -0.4548, 0.3367, ..., 0.5904, 0.5517, 0.6039],
[ 0.5456, -0.4548, 0.3367, ..., 0.5904, 0.5517, 0.6039],
[ 0.5456, -0.4548, 0.3367, ..., 0.5904, 0.5517, 0.6039],
...,
[ 0.5456, -0.4548, 0.3367, ..., 0.5904, 0.5517, 0.6039],
[ 0.5456, -0.4548, 0.3367, ..., 0.5904, 0.5517, 0.6039],
[ 0.5456, -0.4548, 0.3367, ..., 0.5904, 0.5517, 0.6039]])
v(image_list, group_images = True, group_max_seq_len=229)
tensor([[ 0.2724, -0.8302, 0.4734, ..., 0.7219, 0.6409, 0.4224],
[ 0.5486, -0.4530, 0.3360, ..., 0.5885, 0.5462, 0.6067],
[ 0.2724, -0.8302, 0.4734, ..., 0.7219, 0.6409, 0.4224],
...,
[ 0.4754, -0.4497, 0.3625, ..., 0.6052, 0.6225, 0.5106],
[ 0.2724, -0.8302, 0.4734, ..., 0.7219, 0.6409, 0.4224],
[ 0.4645, -0.4711, 0.3736, ..., 0.6147, 0.6285, 0.5033]])
```
For larger maximum sequence lengths, all the images have identical outputs. My problem is, I want deterministic results, therefore I want a constant max sequence length regardless of how the images are batched (kind of the whole reason I want to use NaViT). However, if I pick the maximum of the whole dataset, then you have the above (1315) result where every single image has identical logits.
If you can clarify how I decide on this parameter I would really appreciate it. | 2hard
|
Title: Random 'Segmentation fault (core dumped)' error when training for long spancat
Body: Hi,
I am getting 'Segmentation fault (core dumped)' when trying to train model for long SpanCat. I know this error could be related to OOM issues but this does not seem the case here. I tried to reduce [nlp] batch_size and [training.batcher.size] as shown in the attached config file and used a VM with very large RAM to make sure we are not running out of memory.
During training the VM memory usage never goes above 40% and even when reducing the [components.spancat.suggester] min_size and max_size the memory usage does not exceed 20% but the training exits with error 'Segmentation fault (core dumped)'.
Note: when training with low [components.spancat.suggester] values the training completes but with all zeroes for F, P and R.
His is the command I am using for training:
python -m spacy train config_spn.cfg --output ./output_v3_lg_1.3 --paths.train ./spacy_models_v3/train_data.spacy --paths.dev ./spacy_models_v3/test_data.spacy --code functions.py -V
This is the training output:
[2023-09-28 09:25:08,461] [DEBUG] Config overrides from CLI: ['paths.train', 'paths.dev']
โน Saving to output directory: output_v3_lg_1.3
โน Using CPU
=========================== Initializing pipeline ===========================
[2023-09-28 09:25:08,610] [INFO] Set up nlp object from config
[2023-09-28 09:25:08,618] [DEBUG] Loading corpus from path: spacy_models_v3/test_data.spacy
[2023-09-28 09:25:08,618] [DEBUG] Loading corpus from path: spacy_models_v3/train_data.spacy
[2023-09-28 09:25:08,619] [INFO] Pipeline: ['tok2vec', 'spancat']
[2023-09-28 09:25:08,621] [INFO] Created vocabulary
[2023-09-28 09:25:09,450] [INFO] Added vectors: en_core_web_lg
[2023-09-28 09:25:09,450] [INFO] Finished initializing nlp object
[2023-09-28 09:25:16,150] [INFO] Initialized pipeline components: ['tok2vec', 'spancat']
โ Initialized pipeline
============================= Training pipeline =============================
[2023-09-28 09:25:16,158] [DEBUG] Loading corpus from path: spacy_models_v3/test_data.spacy
[2023-09-28 09:25:16,159] [DEBUG] Loading corpus from path: spacy_models_v3/train_data.spacy
โน Pipeline: ['tok2vec', 'spancat']
โน Initial learn rate: 0.001
E # LOSS TOK2VEC LOSS SPANCAT SPANS_SC_F SPANS_SC_P SPANS_SC_R SCORE
--- ------ ------------ ------------ ---------- ---------- ---------- ------
0 0 98109.47 19535.08 0.00 0.00 4.58 0.00
0 200 528.73 781.51 0.00 0.00 3.75 0.00
Segmentation fault (core dumped)
Environment:
Operating System: Ubuntu 20.04.6 LTS
Python Version Used: 3.8.10
spaCy Version Used: 3.6.0
[config_spn.cfg.txt](https://github.com/explosion/spaCy/files/12748569/config_spn.cfg.txt)
Thanks in advance!
| 2hard
|
Title: Please install TA-Lib to use 2crows. (pip install TA-Lib) message ...
Body: **Which version are you running? The lastest version is on Github. Pip is for major releases.**
```python
import pandas_ta as ta
print(ta.version)
```
0.3.14b0
**Do you have _TA Lib_ also installed in your environment?**
```sh
$ pip list
```
yes
**Did you upgrade? Did the upgrade resolve the issue?**
```sh
$ pip install -U git+https://github.com/twopirllc/pandas-ta
```
Yes.. i have installed talib using the updated version only as mentioned in the readme...
**Describe the bug**
[A clear and concise description of what the bug is.]
i am simply trying to import ticker data from yfinance and using df.ta.strategy(ta.AllStrategy) to build all the indicators. However, i am getting messages saying: **Please install TA-Lib to use 2crows. (pip install TA-Lib)**
first 7 lines of the output of df.ta.strategy(ta.AllStrategy)
**0it [00:00, ?it/s][X] Please install TA-Lib to use 2crows. (pip install TA-Lib)
[X] Please install TA-Lib to use 3blackcrows. (pip install TA-Lib)
[X] Please install TA-Lib to use 3inside. (pip install TA-Lib)
[X] Please install TA-Lib to use 3linestrike. (pip install TA-Lib)
[X] Please install TA-Lib to use 3outside. (pip install TA-Lib)
[X] Please install TA-Lib to use 3starsinsouth. (pip install TA-Lib)
[X] Please install TA-Lib to use 3whitesoldiers. (pip install TA-Lib)**
**To Reproduce**
Provide sample code.
Code:
(https://colab.research.google.com/drive/1eGviU_45HrZLDj_hSHLvXkTvw2gln-vd?usp=sharing)
**Expected behavior**
A clear and concise description of what you expected to happen.
My expectation is what is written in the readme that "Runs and appends all indicators to the current DataFrame by default"
**Screenshots**
If applicable, add screenshots to help explain your problem.
**Additional context**
Add any other context about the problem here.
I want to generate all the indicators at one go.. Also, I am seeing many indicators are having Null. Not sure whether it is because of the installation issue itself mentioned previously..
Thanks for using Pandas TA!
| 1medium
|
Title: CockroachDB + SQLAlchemy trouble
Body: <!--
Thank you for reporting an issue/feature request.
If this is a feature request, please disregard this template. If this is
a bug report, please answer to the questions below.
It will be much easier for us to fix the issue if a test case that reproduces
the problem is provided, with clear instructions on how to run it.
Thank you!
-->
* **asyncpg version**: 0.23.0
* **PostgreSQL version**: cockroachdb/cockroach:latest
* **Do you use a PostgreSQL SaaS? If so, which? Can you reproduce
the issue with a local PostgreSQL install?**: N/A
* **Python version**: 3.8.10
* **Platform**: Arch Linux
* **Do you use pgbouncer?**: No
* **Did you install asyncpg with pip?**: Yes
* **If you built asyncpg locally, which version of Cython did you use?**: N/A
* **Can the issue be reproduced under both asyncio and
[uvloop](https://github.com/magicstack/uvloop)?**: Yes
<!-- Enter your issue details below this comment. -->
Disclaimer, I'm not entirely sure whether the actual bug is in this repository or in SQLAlchemy. That said, when trying to use `sqlalchemy` with the `asyncpg` driver to connect to CockroachDB, [this function](https://github.com/sqlalchemy/sqlalchemy/blob/master/lib/sqlalchemy/dialects/postgresql/asyncpg.py#L1015-L1039) causes some problems. The [`json` block](https://github.com/sqlalchemy/sqlalchemy/blob/master/lib/sqlalchemy/dialects/postgresql/asyncpg.py#L1026-L1032) causes `asyncpg` to emit a `ValueError: unknown type: pg_catalog.json`, and, when commented out, the [`jsonb` block](https://github.com/sqlalchemy/sqlalchemy/blob/master/lib/sqlalchemy/dialects/postgresql/asyncpg.py#L1033-L1039) emits `asyncpg.exceptions._base.InterfaceError: cannot use custom codec on non-scalar type pg_catalog.jsonb`. I wrote [this patch](https://github.com/SoftwareSheriff/sqlalchemy/commit/c9a386c0a6b401838a87b678cf97543b8b0e263c) for SQLAlchemy which successfully works around these two problems, but I feel like it probably breaks something else and also that there's just no way that this right way to fix this. | 1medium
|
Title: TypeError: cannot unpack non-iterable NoneType object
Body: ## generating video: 1 => ./storage/tasks/a240439d-b011-484d-8aa8-a223665691ee/final-1.mp4
2024-04-12 16:01:33 | INFO | "./app/services/video.py:183": generate_video - start, video size: 1080 x 1920
2024-04-12 16:01:33 | INFO | "./app/services/video.py:184": generate_video - โ video: ./storage/tasks/a240439d-b011-484d-8aa8-a223665691ee/combined-1.mp4
2024-04-12 16:01:33 | INFO | "./app/services/video.py:185": generate_video - โก audio: ./storage/tasks/a240439d-b011-484d-8aa8-a223665691ee/audio.mp3
2024-04-12 16:01:33 | INFO | "./app/services/video.py:186": generate_video - โข subtitle: ./storage/tasks/a240439d-b011-484d-8aa8-a223665691ee/subtitle.srt
2024-04-12 16:01:33 | INFO | "./app/services/video.py:187": generate_video - โฃ output: ./storage/tasks/a240439d-b011-484d-8aa8-a223665691ee/final-1.mp4
2024-04-12 16:01:33 | INFO | "./app/services/video.py:202": generate_video - using font: ./resource/fonts/STHeitiLight.ttc
2024-04-12 16:01:34.052 Uncaught app exception
Traceback (most recent call last):
File "/opt/anaconda3/envs/MoneyPrinterTurbo/lib/python3.10/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 542, in _run_script
exec(code, module.__dict__)
File "/Users/xunuoqing/Downloads/MoneyPrinterTurbo-main/webui/Main.py", line 432, in <module>
result = tm.start(task_id=task_id, params=params)
File "/Users/xunuoqing/Downloads/MoneyPrinterTurbo-main/app/services/task.py", line 155, in start
video.generate_video(video_path=combined_video_path,
File "/Users/xunuoqing/Downloads/MoneyPrinterTurbo-main/app/services/video.py", line 238, in generate_video
sub = SubtitlesClip(subtitles=subtitle_path, encoding='utf-8')
File "/opt/anaconda3/envs/MoneyPrinterTurbo/lib/python3.10/site-packages/moviepy/video/tools/subtitles.py", line 69, in __init__
self.duration = max([tb for ((ta, tb), txt) in self.subtitles])
File "/opt/anaconda3/envs/MoneyPrinterTurbo/lib/python3.10/site-packages/moviepy/video/tools/subtitles.py", line 69, in <listcomp>
self.duration = max([tb for ((ta, tb), txt) in self.subtitles])
TypeError: cannot unpack non-iterable NoneType object
[audio.mp3.json](https://github.com/harry0703/MoneyPrinterTurbo/files/14956044/audio.mp3.json)
[script.json](https://github.com/harry0703/MoneyPrinterTurbo/files/14956045/script.json)
[subtitle.srt.json](https://github.com/harry0703/MoneyPrinterTurbo/files/14956046/subtitle.srt.json)
| 1medium
|
Title: Pack rule operator exists / nexists should not have criteria pattern mandatory
Body: ## SUMMARY
When using exists / nexists rule operator, we need to provide a criteria pattern which is not correct, as the value of this pattern needs to be null / should not be there.
### STACKSTORM VERSION
st2 3.7.0, on Python 3.6.8
##### OS, environment, install method
Running on Docker in Mackbook Pro (x86)
## Steps to reproduce the problem
Use exists / nexists rule operator in criteria section. Omit the value of criteria pattern (as it is not needed for this operator).
```
criteria:
trigger.org_id:
type: exists
```
## Expected Results
The pack should get installed correctly as all information is available.
## Actual Results
Pack installation fails and we need to add a pattern field unnecessarily.
```
criteria:
trigger.org_id:
type: exists
pattern: "placeholder"
```
Making sure to follow these steps will guarantee the quickest resolution possible.
Thanks!
| 1medium
|
Title: Continuous Driver download problem
Body: Every time I run it it downloads the driver as in the image, does it have to do this every time?
<img width="1017" alt="image" src="https://github.com/seleniumbase/SeleniumBase/assets/88004617/99ee1baa-440f-41be-a930-fe71a8f2d1c8">
| 1medium
|
Title: Access to application
Body: **Describe the bug**
A clear and concise description of what the bug is. Please include timestamps and HTTP status codes.
If possible include the [httpie](https://httpie.org/) or `curl` request and response.
Please include the verbose flag. `-v`
**To Reproduce**
`httpie/curl` request to reproduce the behavior:
1. Getting Italy data at `v2/locations/IT` gives a 422.
2. Expected to same data as `/v2/locations?country_code=IT`
2. See httpie request & response below
**Expected behavior**
A clear and concise description of what you expected to happen.
**Screenshots or Requests**
If applicable, add screenshots or `httpie/curl`requests to help explain your problem.
```sh
$ http GET https://coronavirus-tracker-api.herokuapp.com/v2/locations/IT -v
GET /v2/locations/IT HTTP/1.1
Accept: */*
Accept-Encoding: gzip, deflate
Connection: keep-alive
Host: coronavirus-tracker-api.herokuapp.com
User-Agent: HTTPie/2.0.0
HTTP/1.1 422 Unprocessable Entity
Connection: keep-alive
Content-Length: 99
Content-Type: application/json
Date: Sat, 18 Apr 2020 12:50:29 GMT
Server: uvicorn
Via: 1.1 vegur
{
"detail": [
{
"loc": [
"path",
"id"
],
"msg": "value is not a valid integer",
"type": "type_error.integer"
}
]
}
```
**Additional context**
Add any other context about the problem here.
Does the other instance at https://covid-tracker-us.herokuapp.com/ produce the same result?
| 1medium
|
Title: Configure OAuthClient with OpenID Discovery configuration
Body: https://openid.net/specs/openid-connect-discovery-1_0.html#ProviderMetadata
| 1medium
|
Title: On the clang-format check of the project
Body: I noticed that horovod use [clang-format](https://clang.llvm.org/docs/ClangFormat.html) to format C++ code.
But when I check the project with clang-format-12, I still got many errors. The cmd I used as below:
```bash
#!/usr/bin/env bash
for src in $(find ./horovod -name "*.h" -or -name "*.cc")
do
clang-format-12 -style=file ${src}
done
```
And the output as attached [hvd_cf12.txt](https://github.com/horovod/horovod/files/8220236/hvd_cf12.txt).
May I know did I use the wrong clang-format version, or the cmd is not right?
| 1medium
|
Title: possible to escape already jsonified string ?
Body: I am working with pandas objects that have a very fast to_json() method to serialise pandas.Series.
I need to serialize a dict with some elements which are pandas objects, e.g.:
`{ "data": pandas.Series(...) }`
I would like to be able to transform the dict as
`{ "data": SerializedJSON("[1,3,4, ...]") }`
with
```
class SerializedJSON(str):
"""An object of type SerializedJSON is a string that will be injected as such in the finale JSON string"""
pass
```
so that orjson would not escape again this string.
Does this make sense ? Would it be useful to add this SerializedJSON ?
| 1medium
|
Title: How to change a response timezone based on a query parameter?
Body: Hi, I want to know how to change a response timezone based on a query parmeter like https:://example.com?timezone=utc , https://example.com?timezone=Asia/Tokyo ,or https://exampl.com?timezone=Asia/Kolkata?
I think the answer of https://github.com/vitalik/django-ninja/issues/786 is related to this problem,
but I can't understand how to change timezone dynamicaly.
```
from ninja.renderers import JSONRenderer
from ninja.responses import NinjaJSONEncoder
class MyJsonEncoder(NinjaJSONEncoder):
def default(self, o):
if isinstance(o, datetime.datetime):
###############################
#####how to set timezone dynamically?
##################################
return o.astimezone().isoformat()
return super().default(o)
class MyJsonRenderer(JSONRenderer):
encoder_class = NinjaJSONEncoder
api = NinjaAPI(renderer=MyJsonRenderer())
``` | 1medium
|
Title: Resolve incorrect handling of configuration overrides in pydantic
Body: https://github.com/pydantic/pydantic-settings/issues/180 - we probably want to make the sources in [`_settings_build_values`](https://github.com/pydantic/pydantic-settings/blob/8c5a45e43cca4e88a6d65fcb280529499fc6200a/pydantic_settings/main.py#L146) convert the keys to field names from aliases in case `populate_by_name` is set.
Note that we'll need to wait for upstream to release this | 1medium
|
Title: Feature request: Support client side keepalives
Body: <!-- Enter your issue details below this comment. -->
I have been using version 0.20.1. I was able to configure server side TCP keepalives via the server_settings dict object, but I can't seem to find the place where I can set client side keepalives from either a connection pool or a connection object. Does it make sense for an async library like asyncpg to support something like this?
Thanks and Regards,
Keith
| 1medium
|
Title: python custom ops tutorial stopped working in PyTorch 2.7 RC1
Body: Get PyTorch 2.7 RC1. Repro in next comment.
Error looks like:
```py
Traceback (most recent call last):
File "/home/rzou/dev/2.7/pco.py", line 124, in <module>
cropped_img = f(img)
^^^^^^
File "/home/rzou/dev/2.7/env/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py", line 655, in _fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/rzou/dev/2.7/pco.py", line 120, in f
@torch.compile(fullgraph=True)
File "/home/rzou/dev/2.7/env/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py", line 838, in _fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/rzou/dev/2.7/env/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py", line 1201, in forward
return compiled_fn(full_args)
^^^^^^^^^^^^^^^^^^^^^^
File "/home/rzou/dev/2.7/env/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py", line
328, in runtime_wrapper
all_outs = call_func_at_runtime_with_args(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/rzou/dev/2.7/env/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/utils.py", line 126, in cal
l_func_at_runtime_with_args
out = normalize_as_list(f(args))
^^^^^^^
File "/home/rzou/dev/2.7/env/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py", line
689, in inner_fn
outs = compiled_fn(args)
^^^^^^^^^^^^^^^^^
File "/home/rzou/dev/2.7/env/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py", line
495, in wrapper
return compiled_fn(runtime_args)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/rzou/dev/2.7/env/lib/python3.11/site-packages/torch/_inductor/output_code.py", line 460, in __call__
return self.current_callable(inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/tmp/torchinductor_rzou/oy/coy5shd4xlyzvhkrwtaiad5zxz7jhd654636vqhwxsyeux5q27d7.py", line 42, in call
assert_size_stride(buf1, (3, 40, 40), (1600, 40, 1))
AssertionError: expected size 3==3, stride 1==1600 at dim=0; expected size 40==40, stride 120==40 at dim=1; expected s
ize 40==40, stride 3==1 at dim=2
This error most often comes from a incorrect fake (aka meta) kernel for a custom op.
Use torch.library.opcheck to test your custom op.
See https://pytorch.org/docs/stable/library.html#torch.library.opcheck
```
cc @ezyang @gchanan @kadeng @msaroufim @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @muchulee8 @amjames @chauhang @aakhundov | 2hard
|
Title: ๆฅ้
Body: ### Expected Behavior
ๆฅ้
### Actual Behavior
Trimap did not contain background values
### Steps to Reproduce
Trimap did not contain background values
### Debug Logs
```powershell
Trimap did not contain background values
```
### Other
_No response_ | 1medium
|
Title: TypeError raised from hidden_tag() on Jinja 3.0.0rc1
Body: # Requirements
```
click==8.0.0rc1
Flask==2.0.0rc1
Flask-WTF==0.14.3
itsdangerous==2.0.0rc2
Jinja2==3.0.0rc1
MarkupSafe==2.0.0rc2
Werkzeug==2.0.0rc4
WTForms==2.3.3
```
# Example
```
import os
from flask import Flask, render_template_string
from flask_wtf import FlaskForm
from wtforms import StringField
from wtforms.validators import DataRequired
from flask_wtf.csrf import CSRFProtect
class MyForm(FlaskForm):
name = StringField('name', validators=[DataRequired()])
app = Flask(__name__)
app.config['SECRET_KEY'] = os.urandom(24).hex()
csrf = CSRFProtect(app)
@app.route('/')
def hello_world():
form = MyForm()
return render_template_string('''
<form method="POST" action="/">
{{ form.hidden_tag() }}
{{ form.name.label }} {{ form.name(size=20) }}
<input type="submit" value="Go">
</form>
''', form=form)
if __name__ == '__main__':
app.run()
```
# Traceback
```
Traceback (most recent call last):
File "/.../pre-venv/lib/python3.9/site-packages/flask/app.py", line 1971, in __call__
return self.wsgi_app(environ, start_response)
File "/.../pre-venv/lib/python3.9/site-packages/flask/app.py", line 1956, in wsgi_app
response = self.handle_exception(e)
File "/.../pre-venv/lib/python3.9/site-packages/flask/app.py", line 1953, in wsgi_app
response = self.full_dispatch_request()
File "/.../pre-venv/lib/python3.9/site-packages/flask/app.py", line 1454, in full_dispatch_request
rv = self.handle_user_exception(e)
File "/.../pre-venv/lib/python3.9/site-packages/flask/app.py", line 1452, in full_dispatch_request
rv = self.dispatch_request()
File "/.../pre-venv/lib/python3.9/site-packages/flask/app.py", line 1438, in dispatch_request
return self.view_functions[rule.endpoint](**req.view_args)
File "/.../app.py", line 21, in hello_world
return render_template_string('''
File "/.../pre-venv/lib/python3.9/site-packages/flask/templating.py", line 145, in render_template_string
return _render(ctx.app.jinja_env.from_string(source), context, ctx.app)
File "/.../pre-venv/lib/python3.9/site-packages/flask/templating.py", line 110, in _render
rv = template.render(context)
File "/.../pre-venv/lib/python3.9/site-packages/jinja2/environment.py", line 1127, in render
self.environment.handle_exception()
File "/.../pre-venv/lib/python3.9/site-packages/jinja2/environment.py", line 814, in handle_exception
raise rewrite_traceback_stack(source=source)
File "<template>", line 3, in top-level template code
File "/.../pre-venv/lib/python3.9/site-packages/flask_wtf/form.py", line 133, in hidden_tag
return Markup(
File "/.../pre-venv/lib/python3.9/site-packages/jinja2/utils.py", line 843, in __init__
super().__init__(*args, **kwargs)
TypeError: object.__init__() takes exactly one argument (the instance to initialize)
```
# Notes
Using `form.csrf_token` in the place of `hidden_tag()` in the template raises no exception.
Also my assumption is that `hidden_tag()` does not require the existence of additional hidden form elements as I believe this works as expected on Flask 1.1.2.
Thank you for the consideration in preparation for Flask 2.0 | 1medium
|
Title: pipe.disable_model_cpu_offload
Body: **Is your feature request related to a problem? Please describe.**
If I enable the following in Gradio interface
sana_pipe.enable_model_cpu_offload()
and during next generation I want to disable cpu offload, how to do it? I mentioned Gradio specifically as command line inference will not have this problem unless after initializing pipe you generate multiple times with and without cpu offload.
I already searched but nothing found
https://github.com/search?q=repo%3Ahuggingface%2Fdiffusers%20disable_model_cpu_offload&type=code
**Describe the solution you'd like.**
Add method to disable for
1. enable_model_cpu_offload()
2. enable_sequential_cpu_offload()
**Describe alternatives you've considered.**
I will have to delete the pipe completely and load again for each inference in Gradio UI
Kindly suggest if any alternative solution.
```
import torch
from diffusers import SanaPipeline
pipe = SanaPipeline.from_pretrained(
"Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers", torch_dtype=torch.float32
)
pipe.to("cuda")
pipe.text_encoder.to(torch.bfloat16)
pipe.transformer = pipe.transformer.to(torch.bfloat16)
pipe.enable_model_cpu_offload()
image = pipe(prompt='a cyberpunk cat with a neon sign that says "Sana"')[0]
image[0].save("output.png")
pipe.disable_model_cpu_offload()
image = pipe(prompt='a cyberpunk cat with a neon sign that says "Sana 1"')[0]
image[0].save("output1.png")
```
P.S. How to delete a pipe completely so all models are removed completely and GPU memory is freed
I did checked documentation but unable to find find anything relevant
https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/sana/pipeline_sana.py
https://github.com/huggingface/diffusers/blob/4e44534845d35248436abf87688906f52e71b868/src/diffusers/pipelines/pipeline_utils.py
| 1medium
|
Title: Postgresql Problem
Body: Hello, I am using trying to connect PostgreSQL on my fastapi-admin project.
Is this possible? I'm getting an error about timeout:
File "/usr/local/lib/python3.9/asyncio/tasks.py", line 492, in wait_for
raise exceptions.TimeoutError() from exc
asyncio.exceptions.TimeoutError
On another hand, I would like to connect the project with a custom PostgreSQL DB. Is there any way to do it using my own tables?
| 1medium
|
Title: Missing Validation Set
Body: Is there a validation set used for choosing the best model before testing the accuracy on the test set?
From what I see from the code, the model with Best Rank 1 is chosen based on test set result. Won't this mean that the Best Rank 1 result is overfitting on the test set? | 1medium
|
Title: sample inference code for llama sft 7 model
Body: Hello,
Can you send me the inference sample python code snippet of llama sft 7 model that shows how to do inference with llama model?
I know about <assistant> <prompter> these special tokens from code are used to learn model differences between user & assistant prompts. But i want to know once user enters prompt/message, what is the pipeline for inference. like EOS tokens used, Stop sequences used. where & how to use special tokens to frame user & assistant responses.
I was looking through inference code but its quite complex to understand for new user.
| 1medium
|
Title: model.load(..., weights_only=True) dose not load batch normalization weights
Body: when I'm using model.load(..., weights_only=True), the weights correspond to BN layers are not loaded. that's why when I use model.predict the output of the classifier is completely different and incorrect. How can I load the weights of conv layers as well as BN layers but not input size and other optimization parameters?
Thanks | 1medium
|
Title: Allow applying patterns to single values
Body: Context: plotly.express.timeline
Looking at (and neighboring lines which perform analogous checks) https://github.com/plotly/plotly.py/blob/216fca2a7ed14d2e58f5423557b6e731e2a0f575/packages/python/plotly/plotly/express/_core.py#L973, there is no way to set `pattern_shape_sequence` to `['']` and set patterns for individual values within `pattern_shape_map`. All I want is to make one specific thing stand out without changing its color.
โฆunless there is another way to achieve that that Iโd missed.
| 1medium
|
Title: Support for LocalAI
Body: Hey :wave: LocalAI (https://github.com/mudler/LocalAI) author here - nice project!
**Is your feature request related to a problem? Please describe.**
I'd like to run this locally with LocalAI - only Ollama seems to be supported.
**Describe the solution you'd like**
LocalAI provides a drop-in API compatible with OpenAI - the only requirement is to be able to specify a base API url endpoint for the client to hit. If Scrapegraph could let the user specify a base url for OpenAI would be enough to make it compatible with LocalAI
**Describe alternatives you've considered**
N/A
**Additional context**
n/a
| 1medium
|
Title: Darkflow - YOLOv1 - Loss function
Body: Hi, Guys
Can someone explain how the loss function is working? Because from what I can tell it's being called cli.py builds the model, creates a framework and does a single pass to build the network. Then Train is called and calls the loss function and the training begins. Is this correct chain of events?
Also flow.py contains:
Please see the comment below.
```
def train(self):
loss_ph = self.framework.placeholders
loss_mva = None; profile = list()
batches = self.framework.shuffle()
# Function Pointer to loss function
loss_op = self.framework.loss
print("LOSS FUNCTION POINTER: ",loss_op)
for i, (x_batch, datum) in enumerate(batches):
if not i: self.say(train_stats.format(
self.FLAGS.lr, self.FLAGS.batch,
self.FLAGS.epoch, self.FLAGS.save
))
feed_dict = {
loss_ph[key]: datum[key]
for key in loss_ph }
feed_dict[self.inp] = x_batch
feed_dict.update(self.feed)
fetches = [self.train_op, loss_op]
if self.FLAGS.summary:
fetches.append(self.summary_op)
fetched = self.sess.run(fetches, feed_dict)
print(fetched)
loss = fetched[1]
print(loss)
# WHAT DOES THIS DO BELOW??????????
if loss_mva is None: loss_mva = loss
loss_mva = .9 * loss_mva + .1 * loss
step_now = self.FLAGS.load + i + 1
if self.FLAGS.summary:
self.writer.add_summary(fetched[2], step_now)
form = 'step {} - loss {} - moving ave loss {}'
self.say(form.format(step_now, loss, loss_mva))
profile += [(loss, loss_mva)]
ckpt = (i+1) % (self.FLAGS.save // self.FLAGS.batch)
args = [step_now, profile]
if not ckpt: _save_ckpt(self, *args)
if ckpt: _save_ckpt(self, *args)
```
| 1medium
|
Title: Fix failing builds: https://travis-ci.org/github/man-group/arctic/jobs/762598165
Body: On a quick glance, some failing tests seem to be returning None, both VersionStore and ChunkStore. Need to take a proper look at this, as it's blocking quite a few PRs | 2hard
|
Title: A question about loss backward
Body: Thank you for sharing the well organized code. I learned a lot from the code. Now I would like to ask you a question about loss.backward. For the classification problem, the final output of the model is a classification vector, such as 1 x M vector, your code is also targeted at the classification task, for a certain category, one of the categories corresponding to the classification score can be propagated backward. My question is, if the output of the model is not a 1 x M vector, but a matrix like N x M, and I want to take one of N and propagate backwards, how do I do that?
Your code: 1 x M -> value
I want: N x M -> a vector
Looking forward to your reply. | 1medium
|
Title: djceleryๅบ้.
Body:
![Uploading image.pngโฆ]()
Request Method: | GET
-- | --
https://csds.nkhdkj.com/admin/djcelery/periodictask/
2.1.8
TypeError
Object of type '__proxy__' is not JSON serializable
| 1medium
|
Title: Visual glitches with thick line(>1) on version 0.12.4
Body: <!-- In the following, please describe your issue in detail! -->
<!-- If some of the sections do not apply, just remove them. -->
### Short description
When setting line width thicker than 1, zooming in causes some line to fill up a large area
https://user-images.githubusercontent.com/66480156/156883464-b96a4b43-101e-46aa-afed-2ff300aae082.mp4
### Code to reproduce
That light green line was created this way
```python
import pyqtgraph as pyqtgraph
plot_item = pyqtgraph.PlotItem()
line = plot_item.plot(
pen=pyqtgraph.mkPen("#E2F200", width=2.5),
connect="finite",
),
```
### Expected behavior
Zooming in normally
### Real behavior
Light green line fills up a large space. No error occured.
### Tested environment(s)
* PyQtGraph version: 0.12.4
* Qt Python binding: Pyside6 6.2.3 Qt 6.2.3
* Python version: 3.10.2
* NumPy version: 1.22.2
* Operating system: Windows 11
* Installation method: pip
### Additional context
None
| 1medium
|
Title: [BUG] File not found in autotuner cache in multi-node setting on SLURM
Body: **Describe the bug**
I am training an LLM using DeepSpeed and 12 nodes a 8 V100s per node. My training is generally working well (thanks DeepSpeed), but when I run multiple training runs in parallel, I run into trouble.
I am getting these kinds of errors
```
Traceback (most recent call last):
File "/gpfs/u/scratch/ANFM/ANFMbchl/DeepSpeed/deepspeed/ops/transformer/inference/triton/matmul_ext.py", line 473, in matmul_ext_update_autotune_table
fp16_matmul._update_autotune_table()
fp16_matmul._update_autotune_table()
File "/gpfs/u/scratch/ANFM/ANFMbchl/DeepSpeed/deepspeed/ops/transformer/inference/triton/matmul_ext.py", line 450, in _update_autotune_table
File "/gpfs/u/scratch/ANFM/ANFMbchl/DeepSpeed/deepspeed/ops/transformer/inference/triton/matmul_ext.py", line 450, in _update_autotune_table
TritonMatmul._update_autotune_table(__class__.__name__ + "_2d_kernel", __class__._2d_kernel)
File "/gpfs/u/scratch/ANFM/ANFMbchl/DeepSpeed/deepspeed/ops/transformer/inference/triton/matmul_ext.py", line 179, in _update_autotune_table
TritonMatmul._update_autotune_table(__class__.__name__ + "_2d_kernel", __class__._2d_kernel)
File "/gpfs/u/scratch/ANFM/ANFMbchl/DeepSpeed/deepspeed/ops/transformer/inference/triton/matmul_ext.py", line 179, in _update_autotune_table
cache_manager.put(autotune_table)
File "/gpfs/u/scratch/ANFM/ANFMbchl/DeepSpeed/deepspeed/ops/transformer/inference/triton/matmul_ext.py", line 98, in put
cache_manager.put(autotune_table)
os.rename(self.file_path + ".tmp", self.file_path)
FileNotFoundError: [Errno 2] No such file or directory: '/gpfs/u/home/ANFM/ANFMbchl/scratch/1167439/.cache/Fp16Matmul_2d_kernel.pickle.tmp' -> '/gpfs/u/home/ANFM/ANFMbchl/scratch/1167439/.cache/Fp16Matmul_2d_kernel.pickle'
File "/gpfs/u/scratch/ANFM/ANFMbchl/DeepSpeed/deepspeed/ops/transformer/inference/triton/matmul_ext.py", line 98, in put
os.rename(self.file_path + ".tmp", self.file_path)
FileNotFoundError: [Errno 2] No such file or directory: '/gpfs/u/home/ANFM/ANFMbchl/scratch/.cache/Fp16Matmul_2d_kernel.pickle.tmp' -> '/gpfs/u/home/ANFM/ANFMbchl/scratch/.cache/Fp16Matmul_2d_kernel.pickle'
```
I thought that this is because maybe the directories are shared between the multiple runs, which can create race conditions.
My `TMPDIR`, `TRITON_CACHE_DIR`, and `TORCH_EXTENSIONS_DIR` are set as follows
```
export TMPDIR=$HOME/scratch/.cache
export TRITON_CACHE_DIR=$HOME/scratch/.cache
export TORCH_EXTENSIONS_DIR=$HOME/scratch/.cache/torch-extensions
```
To fix this, I tried to allocate one cache folder per run like so
```
export TMPDIR=$HOME/scratch/.cache
export TRITON_CACHE_DIR=$HOME/scratch/$SLURM_JOBID/.cache
export TORCH_EXTENSIONS_DIR=$HOME/scratch/$SLURM_JOBID/.cache/torch-extensions
mkdir -p $TRITON_CACHE_DIR
mkdir -p $TORCH_EXTENSIONS_DIR
```
but that also didn't work. Now I am getting this error
```
Traceback (most recent call last):
File "/gpfs/u/scratch/ANFM/ANFMbchl/DeepSpeed/deepspeed/ops/transformer/inference/triton/matmul_ext.py", line 473, in matmul_ext_update_autotune_table
fp16_matmul._update_autotune_table()
fp16_matmul._update_autotune_table()
File "/gpfs/u/scratch/ANFM/ANFMbchl/DeepSpeed/deepspeed/ops/transformer/inference/triton/matmul_ext.py", line 450, in _update_autotune_table
File "/gpfs/u/scratch/ANFM/ANFMbchl/DeepSpeed/deepspeed/ops/transformer/inference/triton/matmul_ext.py", line 450, in _update_autotune_table
TritonMatmul._update_autotune_table(__class__.__name__ + "_2d_kernel", __class__._2d_kernel)
File "/gpfs/u/scratch/ANFM/ANFMbchl/DeepSpeed/deepspeed/ops/transformer/inference/triton/matmul_ext.py", line 179, in _update_autotune_table
TritonMatmul._update_autotune_table(__class__.__name__ + "_2d_kernel", __class__._2d_kernel)
File "/gpfs/u/scratch/ANFM/ANFMbchl/DeepSpeed/deepspeed/ops/transformer/inference/triton/matmul_ext.py", line 179, in _update_autotune_table
cache_manager.put(autotune_table)
File "/gpfs/u/scratch/ANFM/ANFMbchl/DeepSpeed/deepspeed/ops/transformer/inference/triton/matmul_ext.py", line 98, in put
cache_manager.put(autotune_table)
os.rename(self.file_path + ".tmp", self.file_path)
FileNotFoundError: [Errno 2] No such file or directory: '/gpfs/u/home/ANFM/ANFMbchl/scratch/1167439/.cache/Fp16Matmul_2d_kernel.pickle.tmp' -> '/gpfs/u/home/ANFM/ANFMbchl/scratch/1167439/.cache/Fp16Matmul_2d_kernel.pickle'
File "/gpfs/u/scratch/ANFM/ANFMbchl/DeepSpeed/deepspeed/ops/transformer/inference/triton/matmul_ext.py", line 98, in put
os.rename(self.file_path + ".tmp", self.file_path)
FileNotFoundError: [Errno 2] No such file or directory: '/gpfs/u/home/ANFM/ANFMbchl/scratch/1167439/.cache/Fp16Matmul_2d_kernel.pickle.tmp' -> '/gpfs/u/home/ANFM/ANFMbchl/scratch/1167439/.cache/Fp16Matmul_2d_kernel.pickle'
```
**ds_report output**
```
[2024-06-12 03:08:15,154] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-06-12 03:08:15,765] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[WARNING] async_io requires the dev libaio .so object and headers but these were not found.
[WARNING] async_io: please install the libaio-devel package with yum
[WARNING] If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[WARNING] Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
No ROCm runtime is found, using ROCM_HOME='/opt/rocm-4.3.0'
[WARNING] NVIDIA Inference is only supported on Ampere and newer architectures
[WARNING] sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[WARNING] using untested triton version (2.3.0), only 1.0.0 is known to be compatible
--------------------------------------------------
DeepSpeed C++/CUDA extension op report
--------------------------------------------------
NOTE: Ops not installed will be just-in-time (JIT) compiled at
runtime if needed. Op compatibility means that your system
meet the required dependencies to JIT install the op.
--------------------------------------------------
JIT compiled ops requires ninja
ninja .................. [OKAY]
--------------------------------------------------
op name ................ installed .. compatible
--------------------------------------------------
[WARNING] async_io requires the dev libaio .so object and headers but these were not found.
[WARNING] async_io: please install the libaio-devel package with yum
[WARNING] If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
async_io ............... [NO] ....... [NO]
fused_adam ............. [NO] ....... [OKAY]
cpu_adam ............... [NO] ....... [OKAY]
cpu_adagrad ............ [NO] ....... [OKAY]
cpu_lion ............... [NO] ....... [OKAY]
[WARNING] Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
evoformer_attn ......... [NO] ....... [NO]
[WARNING] NVIDIA Inference is only supported on Ampere and newer architectures
fp_quantizer ........... [NO] ....... [NO]
fused_lamb ............. [NO] ....... [OKAY]
fused_lion ............. [NO] ....... [OKAY]
inference_core_ops ..... [NO] ....... [OKAY]
cutlass_ops ............ [NO] ....... [OKAY]
transformer_inference .. [NO] ....... [OKAY]
quantizer .............. [NO] ....... [OKAY]
ragged_device_ops ...... [NO] ....... [OKAY]
ragged_ops ............. [NO] ....... [OKAY]
random_ltd ............. [NO] ....... [OKAY]
[WARNING] sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[WARNING] using untested triton version (2.3.0), only 1.0.0 is known to be compatible
sparse_attn ............ [NO] ....... [NO]
spatial_inference ...... [NO] ....... [OKAY]
transformer ............ [NO] ....... [OKAY]
stochastic_transformer . [NO] ....... [OKAY]
--------------------------------------------------
DeepSpeed general environment info:
torch install path ............... ['/gpfs/u/home/ANFM/ANFMbchl/scratch/miniconda3/envs/torch-nightly/lib/python3.10/site-packages/torch']
torch version .................... 2.3.0+cu121
deepspeed install path ........... ['/gpfs/u/scratch/ANFM/ANFMbchl/DeepSpeed/deepspeed']
deepspeed info ................... 0.14.3+488a823, 488a823, master
torch cuda version ............... 12.1
torch hip version ................ None
nvcc version ..................... 12.1
deepspeed wheel compiled w. ...... torch 2.4, cuda 12.1
shared memory (/dev/shm) size .... 377.69 GB
```
| 2hard
|
Title: [Feature request] using pre-extracted SE files(se.pt)
Body: Can't I use xtts using pre-extracted SE files(se.pt)? I want to apply it and do a voice clone | 1medium
|
Title: [Question] How could I hide the "Finding best initial lr" message from pytorch_lightning when using Darts' Torch Forecasting Models?
Body: I am unable to hide the `Finding best initial lr` message when calling the [`lr_find` method](https://github.com/unit8co/darts/blob/c3a611236690f0704ced6078982adf20b0a33886/darts/models/forecasting/torch_forecasting_model.py#L1111) associated with Darts' Torch `Forecasting Models`, such as `BlockRNNModel`:
Based on my understanding, this message is generated by `pytorch-lightning`. In particular, by the [`on_train_batch_start` method](https://github.com/Lightning-AI/pytorch-lightning/blob/58ad56afece3ea7faec2f1b7f68d90195f316d78/src/lightning/pytorch/tuner/lr_finder.py#L384) from the [`_LRCallback` class](https://github.com/Lightning-AI/pytorch-lightning/blob/58ad56afece3ea7faec2f1b7f68d90195f316d78/src/lightning/pytorch/tuner/lr_finder.py#L349). At least in this specific case :thinking:
I have tried the following:
1. Including `verbose=False` when calling the `lr_find` method.
2. Passing a `TQDMProgressBar(refresh_rate=0)` instance through the `callbacks` list in the `pl_trainer_kwargs` dict passed to the `BlockRNNModel` constructor.
3. Including an `"enable_progress_bar": False` in the `pl_trainer_kwargs` dict passed to the `BlockRNNModel` constructor.
So far, no luck :disappointed:
I don't know if I have misunderstood something or I am missing some critical bit of information :grimacing:
Could you help me solve this issue? :pray:
Any feedback would be much appreciated :relaxed:
***
I am using:
- python 3.11.0
- darts 0.28.0
- pytorch_lightning 1.9.5
Let me know if there are any missing but relevant and clarifying details I should mention. | 1medium
|
Title: The DataFrame serialisation is slower than in v1
Body: Using python pandas. Version 1 i used this:
```python
def dbpop_influx(data, dbname, measurement, columns):
## constants:
dbclient = DataFrameClient(host='localhost', port=8086, username='root', password='root', database=dbname)
n_import_chunks = math.ceil(len(data) / 10000)
data_chunks = np.array_split(data, n_import_chunks)
for d in data_chunks:
dbclient.write_points(d, measurement, tag_columns = columns, protocol = 'line')
```
Takes 29 seconds (was looking to improve that speed with multiprocessing)
Version 2 i used this:
```python
_client = InfluxDBClient(url="http://localhost:9999", token=token, org="org")
_write_client = _client.write_api(write_options=WriteOptions(batch_size=10000,
flush_interval=10_000,
jitter_interval=0,
retry_interval=5_000))
start = time.time()
_write_client.write('data', record=imp_dat[0], data_frame_measurement_name='coinmarketcap_ohlcv',
data_frame_tag_columns=['quote_asset','base_asset'])
print(time.time() - start)
```
this takes 118 seconds...
data looks like:
@bednar | 1medium
|
Title: Civit AI flux model razor-8step-rapid-real not working with diffusers single file
Body: ### Describe the bug
We have this civit AI model: https://civitai.com/models/849864/razor-8step-rapid-real which we want to run using `from_single_file`, but it errors out
### Reproduction
1) First create your CivitAI API key by logging into civit ai and navigating to https://civitai.com/user/account
Then go to "API Keys" section in the bottom and create your key.
2) Run the following command on terminal: `wget --show-progress -O model.safetensors "https://api.civitai.com/download/models/950841?token=YOUR_TOKEN"`
3) Try the code:
```
import torch
from diffusers import FluxPipeline
#wget --show-progress -O model.safetensors "https://api.civitai.com/download/models/950841?token="
pipe = FluxPipeline.from_single_file(
"model.safetensors",
torch_dtype=torch.bfloat16,
)
pipe.to("cuda")
prompt = "A cat holding a sign that says hello world"
image = pipe(
prompt,
height=1024,
width=1024,
guidance_scale=3.5,
num_inference_steps=50,
max_sequence_length=512,
generator=torch.Generator("cuda").manual_seed(0)
).images[0]
image.save("flux.png")
```
### Logs
```shell
(3.7) user@c6dbd33b-904f-4d4e-bc4e-f68f78a80315:~/runware/Ali/sd-base-api$ python ali.py
Fetching 16 files: 100%|โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 16/16 [00:00<00:00, 24745.16it/s]
Loading pipeline components...: 57%|โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ | 4/7 [00:16<00:12, 4.16s/it]You set `add_prefix_space`. The tokenizer needs to be converted from the slow tokenizers
Loading pipeline components...: 71%|โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ | 5/7 [00:16<00:06, 3.37s/it]
Traceback (most recent call last):
File "/home/user/runware/Ali/sd-base-api/ali.py", line 7, in <module>
pipe = FluxPipeline.from_single_file(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/user/anaconda3/envs/3.7/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/user/anaconda3/envs/3.7/lib/python3.12/site-packages/diffusers/loaders/single_file.py", line 509, in from_single_file
loaded_sub_model = load_single_file_sub_model(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/user/anaconda3/envs/3.7/lib/python3.12/site-packages/diffusers/loaders/single_file.py", line 104, in load_single_file_sub_model
loaded_sub_model = load_method(
^^^^^^^^^^^^
File "/home/user/anaconda3/envs/3.7/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/user/anaconda3/envs/3.7/lib/python3.12/site-packages/diffusers/loaders/single_file_model.py", line 343, in from_single_file
diffusers_format_checkpoint = checkpoint_mapping_fn(
^^^^^^^^^^^^^^^^^^^^^^
File "/home/user/anaconda3/envs/3.7/lib/python3.12/site-packages/diffusers/loaders/single_file_utils.py", line 2255, in convert_flux_transformer_checkpoint_to_diffusers
q, k, v, mlp = torch.split(checkpoint.pop(f"single_blocks.{i}.linear1.weight"), split_size, dim=0)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/user/anaconda3/envs/3.7/lib/python3.12/site-packages/torch/functional.py", line 207, in split
return tensor.split(split_size_or_sections, dim)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/user/anaconda3/envs/3.7/lib/python3.12/site-packages/torch/_tensor.py", line 983, in split
return torch._VF.split_with_sizes(self, split_size, dim)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: split_with_sizes expects split_sizes to sum exactly to 33030144 (input tensor's size at dimension 0), but got split_sizes=[3072, 3072, 3072, 12288]
```
### System Info
Copy-and-paste the text below in your GitHub issue and FILL OUT the two last points.
- ๐ค Diffusers version: 0.33.0.dev0
- Platform: Linux-5.15.0-134-generic-x86_64-with-glibc2.35
- Running on Google Colab?: No
- Python version: 3.12.9
- PyTorch version (GPU?): 2.5.1+cu124 (True)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Huggingface_hub version: 0.27.1
- Transformers version: 4.47.1
- Accelerate version: 1.2.1
- PEFT version: 0.14.0
- Bitsandbytes version: not installed
- Safetensors version: 0.5.0
- xFormers version: not installed
- Accelerator: NVIDIA A100-SXM4-80GB, 81920 MiB
- Using GPU in script?: Yes
- Using distributed or parallel set-up in script?: No
### Who can help?
@sayakpaul | 2hard
|
Title: Current version of chromedriver breaks pinned version of selenium
Body: Running the tests on my Ubuntu 20.4 server against tag `5d` resulted in
```
test_admin_home_page (test_selenium.SeleniumTestCase) ... skipped 'Web browser not available'
```
So I hacked `tests/test_selenium.py` to add code to re-raise the exception caught in `SeleniumTestCase.setUpClass()` which got me
```
ERROR: setUpClass (test_selenium.SeleniumTestCase)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/home/bkline/repos/flasky/tests/test_selenium.py", line 19, in setUpClass
cls.client = webdriver.Chrome(chrome_options=options)
File "/home/bkline/repos/flasky/venv/lib/python3.8/site-packages/selenium/webdriver/chrome/webdriver.py", line 65, in __init__
RemoteWebDriver.__init__(
File "/home/bkline/repos/flasky/venv/lib/python3.8/site-packages/selenium/webdriver/remote/webdriver.py", line 98, in __init__
self.start_session(desired_capabilities, browser_profile)
File "/home/bkline/repos/flasky/venv/lib/python3.8/site-packages/selenium/webdriver/remote/webdriver.py", line 188, in start_session
response = self.execute(Command.NEW_SESSION, parameters)
File "/home/bkline/repos/flasky/venv/lib/python3.8/site-packages/selenium/webdriver/remote/webdriver.py", line 256, in execute
self.error_handler.check_response(response)
File "/home/bkline/repos/flasky/venv/lib/python3.8/site-packages/selenium/webdriver/remote/errorhandler.py", line 194, in check_response
raise exception_class(message, screen, stacktrace)
selenium.common.exceptions.WebDriverException: Message: invalid argument: unrecognized capability: chromeOptions
```
A little more digging found some [advice](https://stackoverflow.com/questions/57995521/selenium-common-exceptions-webdriverexception-message-invalid-argument-unreco) which suggested upgrading `selenium` to accommodate changes to the behavior of the launcher in `chromedriver`. Sure enough, running `pip install -U selenium` got the selenium test back in the game. So it would seem that the requirements documents need to be updated to bump the version of `selenium`. I'm at 3.141.0 which is what PyPI is currently serving up. I haven't done the testing to determine the minimum version needed to avoid the breakage I ran into, but I'm not seeing any adverse side effects from this version.
```bash
(venv) $ grep selenium requirements/dev.txt
selenium==3.4.3
(venv) $ pip freeze | grep selenium
selenium==3.141.0
(venv) $ dpkg -l | grep chromium
ii chromium-browser 1:85.0.4183.83-0ubuntu0.20.04.2 amd64 Transitional package - chromium-browser -> chromium snap
ii chromium-chromedriver 1:85.0.4183.83-0ubuntu0.20.04.2 amd64 Transitional package - chromium-chromedriver -> chromium snap
``` | 1medium
|
Title: [BUG] For an nrms model, run_fast_eval does not return the correct prediction scores
Body: ### Description
I believe that for the nrms model, the output of `run_fast_eval` is incorrect. (See code [here](https://github.com/microsoft/recommenders/blob/98d661edc6a9965c7f42b76dc5317af3ae74d5e0/recommenders/models/newsrec/models/base_model.py#L399).)
### In which platform does it happen?
Jupyter Lab running in Ubuntu 20.04.4 LTS (Focal Fossa). Using Python version 3.8.10 with tensorflow version 2.8.0.
### How do we replicate the issue?
Run the following code and verify that the outputs are different (I believe they should be the same):
```
group_impr_indexes, group_labels, group_preds = model.run_slow_eval(valid_news_file, valid_behaviors_file)
group_impr_indexes, group_labels, group_preds = model.run_fast_eval(valid_news_file, valid_behaviors_file)
```
Also, notice that the group_preds output of `run_slow_eval` is in the range [0,1] (as expected) while the output group_preds of `run_fast_eval` is not.
### Expected behavior (i.e. solution)
As stated in the [NRMS paper](https://wuch15.github.io/paper/EMNLP2019-NRMS.pdf) (equation 11), the output should be click probabilities (in the range [0,1]). I think this can be fixed by adding a sigmoid function after the computation of the dot product on [line 416](https://github.com/microsoft/recommenders/blob/98d661edc6a9965c7f42b76dc5317af3ae74d5e0/recommenders/models/newsrec/models/base_model.py#L416).
### Other Comments
| 1medium
|
Title: Troubles regarding removing the progress bar after loop
Body: Hi,
I want to use tqdm in a nested loop and it would help if the progress bar for the inner loop can be removed after finishing the inner loop. I try to pass the parameter "leave=False" as doc said. It did remove the progress bar at the end, but leave a blank line, which means the next progress bar starts at a new line.
The mini example is listed as below.
```
from tqdm.auto import tqdm, trange
import time
for i in trange(4, leave=True):
for j in trange(5, total=5, leave=False):
time.sleep(0.5)
```
I wonder it may relate to my working environment (2 types):
1. JupyterLab 3.4.4, RHEL 8.8 (linux), jupyterlab 3.4.4, python 3.10.11, tqdm 4.65.0, ipywidgets 7.6.5
2. Pycharm using jupyter notebook 6.5.2, python 3.10.12, tqdm 4.65.0, ipywidgets 8.0.4
| 1medium
|
Title: Generate lists of pages based on tag metadata
Body: It would be useful if users could attach **tags** to certain pages, and then use these tags to generate lists of pages that fall under that tag. This is a common thing to do in blogging platforms, and may be useful here as well.
[ABlog kind-of supports this](https://ablog.readthedocs.io/en/latest/), but it uses a directive rather than page-level metadata.
Ideally, people would be able to include metadata at the top of their pages like:
```
tags: tag1, tag2
```
and then include a list of pages with those tags via something like:
````
# To generate a list of pages that have `tag1` in their metadata
```{tag-list} tag1
```
````
| 1medium
|
Title: Not able to index when using List[str] in custom document class
Body: System: `macOS 13.3.1`
Python version: `3.11.0`
IPython version: `8.10.0`
In the latest docarray version, when building hnsw index from the following simple document class:
```python
from docarray import BaseDoc
from docarray.index import HnswDocumentIndex
class MyDoc(BaseDoc):
test: List[str]
index = HnswDocumentIndex[MyDoc]()
```
The following error will pop up:
```
File ~/miniconda3/envs/test/lib/python3.11/site-packages/docarray/index/backends/hnswlib.py:76, in HnswDocumentIndex.__init__(self, db_config, **kwargs)
73 if db_config is not None and getattr(db_config, 'index_name'):
74 db_config.work_dir = db_config.index_name.replace("__", "/")
---> 76 super().__init__(db_config=db_config, **kwargs)
77 self._db_config = cast(HnswDocumentIndex.DBConfig, self._db_config)
78 self._work_dir = self._db_config.work_dir
File ~/miniconda3/envs/test/lib/python3.11/site-packages/docarray/index/abstract.py:111, in BaseDocIndex.__init__(self, db_config, subindex, **kwargs)
109 self._runtime_config = self.RuntimeConfig()
110 self._logger.info('Runtime config created')
--> 111 self._column_infos: Dict[str, _ColumnInfo] = self._create_column_infos(
112 self._schema
113 )
114 self._is_subindex = subindex
115 self._subindices: Dict[str, BaseDocIndex] = {}
File ~/miniconda3/envs/test/lib/python3.11/site-packages/docarray/index/abstract.py:880, in BaseDocIndex._create_column_infos(self, schema)
873 """Collects information about every column that is implied by a given schema.
874
875 :param schema: The schema (subclass of BaseDoc) to analyze and parse
876 columns from
877 :returns: A dictionary mapping from column names to column information.
878 """
879 column_infos: Dict[str, _ColumnInfo] = dict()
--> 880 for field_name, type_, field_ in self._flatten_schema(schema):
881 # Union types are handle in _flatten_schema
882 if issubclass(type_, AnyDocArray):
883 column_infos[field_name] = _ColumnInfo(
884 docarray_type=type_, db_type=None, config=dict(), n_dim=None
885 )
File ~/miniconda3/envs/test/lib/python3.11/site-packages/docarray/index/abstract.py:860, in BaseDocIndex._flatten_schema(cls, schema, name_prefix)
856 else:
857 raise ValueError(
858 f'Union type {t_} is not supported. Only Union of subclasses of AbstractTensor or Union[type, None] are supported.'
859 )
--> 860 elif issubclass(t_, BaseDoc):
861 names_types_fields.extend(
862 cls._flatten_schema(t_, name_prefix=inner_prefix)
863 )
864 elif issubclass(t_, AbstractTensor):
File <frozen abc>:123, in __subclasscheck__(cls, subclass)
TypeError: issubclass() arg 1 must be a class
```
This also happens in other typings like Sequence, Iterable and Tuple. | 2hard
|
Title: Saving Early Stopping Patience Value in last.pt Checkpoint
Body: ### Search before asking
- [X] I have searched the YOLOv5 [issues](https://github.com/ultralytics/yolov5/issues) and [discussions](https://github.com/ultralytics/yolov5/discussions) and found no similar questions.
### Question
Hello,
I have a question regarding the checkpointing mechanism in YOLOv5, specifically related to saving and resuming the training process.
When training a YOLOv5 model, the last.pt checkpoint saves the model's weights and optimizer state. However, it appears that training process parameters, such as the early stopping patience value, are not included in this checkpoint.
**If my training is interrupted and I restart from the last.pt checkpoint, does the **patience value reset to zero, or does it continue from the previously recorded value?****
### Additional
_No response_ | 1medium
|
Title: How to use pandarallel_apply with multiple arguments?
Body: I have code like below:
```
def similarity(txt1, txt2):
return xxxxxxx
vSimilarity = np.vectorize(similarity)
vSimilarity(df['var1'], df['var2'])
```
How to convert it to utilize parallel_apply? Below does not work.
`(df['var1'], df['var2']).parallel_apply(similarity)` | 1medium
|
Title: Alpine Linux
Body: Have you considered using alpine linux? | 3misc
|
Title: Connection Error When Using By-pass Proxies
Body: ### Describe the bug
I'm currently using Clash for Windows as my proxy tunnel, after exporting HTTP_PROXY and HTTPS_PROXY to the port that clash provides๐ค, it runs into a connection error saying "Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.19.1/metrics/seqeval/seqeval.py (ConnectionError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.19.1/metrics/seqeval/seqeval.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f969d391870>: Failed to establish a new connection: [Errno 111] Connection refused'))")))"
I have already read the documentation provided on the hugginface, but I think I didn't see the detailed instruction on how to set up proxies for this library.
### Steps to reproduce the bug
1. Turn on any proxy software like Clash / ShadosocksR etc.
2. export system varibles to the port provided by your proxy software in wsl (It's ok for other applications to use proxy expect dataset-library)
3. load any dataset from hugginface online
### Expected behavior
---------------------------------------------------------------------------
ConnectionError Traceback (most recent call last)
Cell In[33], [line 3](vscode-notebook-cell:?execution_count=33&line=3)
[1](vscode-notebook-cell:?execution_count=33&line=1) from datasets import load_metric
----> [3](vscode-notebook-cell:?execution_count=33&line=3) metric = load_metric("seqeval")
File ~/.local/lib/python3.10/site-packages/datasets/utils/deprecation_utils.py:46, in deprecated.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
[44](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/deprecation_utils.py:44) warnings.warn(warning_msg, category=FutureWarning, stacklevel=2)
[45](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/deprecation_utils.py:45) _emitted_deprecation_warnings.add(func_hash)
---> [46](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/deprecation_utils.py:46) return deprecated_function(*args, **kwargs)
File ~/.local/lib/python3.10/site-packages/datasets/load.py:2104, in load_metric(path, config_name, process_id, num_process, cache_dir, experiment_id, keep_in_memory, download_config, download_mode, revision, trust_remote_code, **metric_init_kwargs)
[2101](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2101) warnings.filterwarnings("ignore", message=".*https://huggingface.co/docs/evaluate$", category=FutureWarning)
[2103](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2103) download_mode = DownloadMode(download_mode or DownloadMode.REUSE_DATASET_IF_EXISTS)
-> [2104](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2104) metric_module = metric_module_factory(
[2105](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2105) path,
[2106](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2106) revision=revision,
[2107](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2107) download_config=download_config,
[2108](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2108) download_mode=download_mode,
[2109](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2109) trust_remote_code=trust_remote_code,
[2110](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2110) ).module_path
[2111](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2111) metric_cls = import_main_class(metric_module, dataset=False)
[2112](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2112) metric = metric_cls(
[2113](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2113) config_name=config_name,
[2114](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/load.py:2114) process_id=process_id,
...
--> [633](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py:633) raise ConnectionError(f"Couldn't reach {url} ({repr(head_error)})")
[634](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py:634) elif response is not None:
[635](https://vscode-remote+wsl-002bubuntu-002d22-002e04.vscode-resource.vscode-cdn.net/home/noodle/Transformers-Tutorials/LayoutLMv3/~/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py:635) raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})")
ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.19.1/metrics/seqeval/seqeval.py (SSLError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.19.1/metrics/seqeval/seqeval.py (Caused by SSLError(SSLEOFError(8, '[SSL: UNEXPECTED_EOF_WHILE_READING] EOF occurred in violation of protocol (_ssl.c:1007)')))")))
### Environment info
- `datasets` version: 2.19.1
- Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.23.0
- PyArrow version: 16.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.2.0 | 1medium
|
Title: AttributeError: module 'keras._tf_keras.keras.layers' has no attribute 'LocallyConnected1D'
Body: AttributeError: module 'keras._tf_keras.keras.layers' has no attribute 'LocallyConnected1D'
| 2hard
|
Title: After resolving python circular import error, docs not working.
Body: Using odmantic 0.3.5 with this model:
person.py
```
from __future__ import annotations
from typing import Optional
from odmantic import Field , Model
# from pydantic import Field, BaseModel as Model <-- docs work fine but odmantic raise error
class Person(Model):
name: Optional[str] = Field(None, <-- some extra options --> )
rooms: "Optional[Room]" = Field(None, <-- some extra options --> )
from ..models.room import Room # nopep8
Person.update_forward_refs()
```
room.py
```
from __future__ import annotations
from typing import Optional
from odmantic import Field , Model
# from pydantic import Field, BaseModel as Model <-- docs work fine but odmantic raise error
class Room(Model):
name: Optional[str] = Field(None, <-- some extra options --> )
persons: "Optional[Person]" = Field(None, <-- some extra options --> )
from ..models.person import Person # nopep8
Room.update_forward_refs()
```
### Current Behavior
this [project](https://github.com/SSaeedHoseini/odmantic-crash-docs-example) reproduce the bug, you can execute ./run.sh and open the http://127.0.0.1:8000/docs#/
### Expected behavior
show docs.
### Environment
- ODMantic version: 0.3.5
- MongoDB version: 4.4.8
- Pydantic infos (output of `python -c "import pydantic.utils; print(pydantic.utils.version_info())`):
```
pydantic version: 1.8.2
pydantic compiled: True
install path: /xxx/lib/python3.9/site-packages/pydantic
python version: 3.9.5 (default, May 11 2021, 08:20:37) [GCC 10.3.0]
platform: Linux-5.11.0-34-generic-x86_64-with-glibc2.33
optional deps. installed: ['dotenv', 'typing-extensions']
```
**Additional context**
error raised from when open the docs:
```
Traceback (most recent call last):
File "/xxx/odmantic-crash-docs-example/.venv/lib/python3.9/site-packages/uvicorn/protocols/http/httptools_impl.py", line 375, in run_asgi
result = await app(self.scope, self.receive, self.send)
File "/xxx/odmantic-crash-docs-example/.venv/lib/python3.9/site-packages/uvicorn/middleware/proxy_headers.py", line 75, in __call__
return await self.app(scope, receive, send)
File "/xxx/odmantic-crash-docs-example/.venv/lib/python3.9/site-packages/fastapi/applications.py", line 208, in __call__
await super().__call__(scope, receive, send)
File "/xxx/odmantic-crash-docs-example/.venv/lib/python3.9/site-packages/starlette/applications.py", line 112, in __call__
await self.middleware_stack(scope, receive, send)
File "/xxx/odmantic-crash-docs-example/.venv/lib/python3.9/site-packages/starlette/middleware/errors.py", line 181, in __call__
raise exc from None
File "/xxx/odmantic-crash-docs-example/.venv/lib/python3.9/site-packages/starlette/middleware/errors.py", line 159, in __call__
await self.app(scope, receive, _send)
File "/xxx/odmantic-crash-docs-example/.venv/lib/python3.9/site-packages/starlette/exceptions.py", line 82, in __call__
raise exc from None
File "/xxx/odmantic-crash-docs-example/.venv/lib/python3.9/site-packages/starlette/exceptions.py", line 71, in __call__
await self.app(scope, receive, sender)
File "/xxx/odmantic-crash-docs-example/.venv/lib/python3.9/site-packages/starlette/routing.py", line 580, in __call__
await route.handle(scope, receive, send)
File "/xxx/odmantic-crash-docs-example/.venv/lib/python3.9/site-packages/starlette/routing.py", line 241, in handle
await self.app(scope, receive, send)
File "/xxx/odmantic-crash-docs-example/.venv/lib/python3.9/site-packages/starlette/routing.py", line 52, in app
response = await func(request)
File "/xxx/odmantic-crash-docs-example/.venv/lib/python3.9/site-packages/fastapi/applications.py", line 161, in openapi
return JSONResponse(self.openapi())
File "/xxx/odmantic-crash-docs-example/.venv/lib/python3.9/site-packages/fastapi/applications.py", line 136, in openapi
self.openapi_schema = get_openapi(
File "/xxx/odmantic-crash-docs-example/.venv/lib/python3.9/site-packages/fastapi/openapi/utils.py", line 387, in get_openapi
definitions = get_model_definitions(
File "/xxx/odmantic-crash-docs-example/.venv/lib/python3.9/site-packages/fastapi/utils.py", line 24, in get_model_definitions
m_schema, m_definitions, m_nested_models = model_process_schema(
File "pydantic/schema.py", line 548, in pydantic.schema.model_process_schema
File "pydantic/schema.py", line 589, in pydantic.schema.model_type_schema
File "pydantic/schema.py", line 241, in pydantic.schema.field_schema
File "pydantic/schema.py", line 495, in pydantic.schema.field_type_schema
File "pydantic/schema.py", line 839, in pydantic.schema.field_singleton_schema
File "/usr/lib/python3.9/abc.py", line 102, in __subclasscheck__
return _abc_subclasscheck(cls, subclass)
TypeError: issubclass() arg 1 must be a class
```
| 2hard
|
Title: HTTP Request Interception
Body: I'm trying the code below and it keeps giving me the following error: `No module named 'blinker._saferef'`
How can I fix it? Also it would be great if I could change the request's parameters/headers, like changing a certain header before continuing the request like in playwright/puppeteer.
```from seleniumbase import Driver
def intercept_response(request, response):
print(request.headers)
driver = Driver(wire=True)
try:
driver.response_interceptor = intercept_response
driver.get("https://wikipedia.org")
finally:
driver.quit()``` | 1medium
|
Title: Documentation / help: where is the config stored
Body: Running http --help should state where the config file is. The man page should also state that. It should also state the format of the config file.
| 0easy
|
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