WhereIsAI/github-issue-similarity huggingface dataset:
Issue 1:
I got a problem that i don't really understand why this is happen.

MATCH (n:USER) where n.email = 'email@hotmail.com' return count(n);

This return me 1.

MATCH (n:USER{email:'email@hotmail.com'}) return count(n);

This return me 1.

MATCH (n:USER) return count(n);

This return me 1150012.

MATCH (n:USER) where n.email <> 'email@hotmail.com' return count(n);

This return me 1150012.

Have you any idea why this can happen ?

Thx

Issue 2:
Hi

I am regularly using MERGE to apply data onto an existing graph. Today I am encountering a new error on a previous working MERGE. I believe this to be a bug.

I am receiving error "More than one item in org.neo4j.helpers.collection.IteratorUtil$12@44c217b1, first:55110598, second:55114825" when trying to merge.

There is a constraint ON :Impression(hash) ONLINE (for uniqueness constraint)

Trying a MERGE as simple as "MERGE (a:Impression {hash:'e612b20ac8dc47b1b08e1fbc67b5a3a2'}) ON CREATE SET a:Test ON MATCH SET a:Test" Causes the issue.

Running "MATCH (a:Impression {hash:'e612b20ac8dc47b1b08e1fbc67b5a3a2'}) RETURN a" now returns 22 nodes.

Running 2.3.2 ENTERPRISE

Ubuntu 14.04

Using Cypher, testing in Neo4j browser

My database is 500GB, I could provide ssh access to a copy of it, if need be.

Thank for your help.
Issue 1:
I just noticed there are two copies of the following in bootstrap.css v3.0.2

.container:before,
.container:after {
display: table;
content: " ";
}

.container:after {
clear: both;
}

Issue 2:
show on bootstrap.css

https://github.com/twbs/bootstrap/blob/3.0.0-wip/dist/css/bootstrap.css#L581

https://github.com/twbs/bootstrap/blob/3.0.0-wip/dist/css/bootstrap.css#L723

https://github.com/twbs/bootstrap/blob/3.0.0-wip/dist/css/bootstrap.css#L743

https://github.com/twbs/bootstrap/blob/3.0.0-wip/dist/css/bootstrap.css#L2023

https://github.com/twbs/bootstrap/blob/3.0.0-wip/dist/css/bootstrap.css#L2433

https://github.com/twbs/bootstrap/blob/3.0.0-wip/dist/css/bootstrap.css#L2617

https://github.com/twbs/bootstrap/blob/3.0.0-wip/dist/css/bootstrap.css#L2637

https://github.com/twbs/bootstrap/blob/3.0.0-wip/dist/css/bootstrap.css#L3032

https://github.com/twbs/bootstrap/blob/3.0.0-wip/dist/css/bootstrap.css#L3296

https://github.com/twbs/bootstrap/blob/3.0.0-wip/dist/css/bootstrap.css#L3453
Issue 1:
[alt + click] for custom multiple cursors does not work on Linux.

Issue 2:
On most Linux desktops alt+click will allow you drag a window by clicking anywhere over it. This conflicts with the multiple cursors binding.

It's still possible to obtain multiple cursors with commands like insertCursorAbove or insertCursorAtEndOfEachLineSelected, but this affects multiple people, and it's already covered in some articles with a workaround (which I'm copying here for Gnome based systems: gsettings set org.gnome.desktop.wm.preferences mouse-button-modifier "<Super>" change <Super> with your preferred key )

It's also acknowledged in VScode's documentation that this shortcut might conflict even on Windows or Macosx...

Note: Your graphics card provider might overwrite these default shortcuts.

So, fixing this for Linux users might also be useful elsewhere. For comparison, the Atom editor uses ctrl+click instead of alt+click for this shortcut.

Rather than changing the default, making it customizable would already be a welcome improvement, but afaik unfortunately there's no way to change it. These are a couple of related default keybindings:

{ "key": "ctrl+shift+up", "command": "editor.action.insertCursorAbove",
"when": "editorTextFocus" },
{ "key": "shift+alt+up", "command": "editor.action.insertCursorAbove",
"when": "editorTextFocus" }

But since I cannot find any editor.action.insertCursor command, it seems that this option is lacking.
Issue 1:
Challenge use-tabindex-to-specify-the-order-of-keyboard-focus-for-several-elements has an issue.

User Agent is: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Ubuntu Chromium/55.0.2883.87 Chrome/55.0.2883.87 Safari/537.36.

Please describe how to reproduce this issue, and include links to screenshots if possible.

<body>
<header>
<h1>Even Deeper Thoughts with Master Camper Cat</h1>
<nav>
<ul>
<li><a href="">Home</a></li>
<li><a href="">Blog</a></li>
<li><a href="">Training</a></li>
</ul>
</nav>
</header>
<form>
<label for="search">Search:</label>

<input type="search" name="search" id="search" tabindex="1">
<input type="submit" name="submit" value="Submit" tabindex="2" id="submit">

</form>
<h2>Inspirational Quotes</h2>
<blockquote>
<p>&ldquo;There's no Theory of Evolution, just a list of creatures I've allowed to live.&rdquo;<br>
- Chuck Norris</p>
</blockquote>
<blockquote>
<p>&ldquo;Wise men say forgiveness is divine, but never pay full price for late pizza.&rdquo;<br>
- TMNT</p>
</blockquote>
<footer>&copy; 2016 Camper Cat</footer>
</body>

It shows me that the code is correct but the preview on the right shows an error

Issue 2:
Challenge add-an-accessible-date-picker has an issue.

User Agent is: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36.

Please describe how to reproduce this issue, and include links to screenshots if possible.

The code result includes additional lines of code

Browser: Google Chrome

<body>
<header>
<h1>Tournaments</h1>
</header>
<main>
<section>
<h2>Mortal Combat Tournament Survey</h2>
<form>
<p>Tell us the best date for the competition</p>
<label for="pickdate">Preferred Date (MM-DD-YYYY):</label>

<!-- Add your code below this line -->
<label for="date">Enter date (MM-DD-YYYY):</label>
<input type="date" id="pickdate" name="date">

<!-- Add your code above this line -->

<input type="submit" name="submit" value="Submit">
</form>
</section>
</main>
<footer>&copy; 2016 Camper Cat</footer>
</body>

```
Issue 1:
ISSUE TYPE

Bug Report

COMPONENT NAME

The shell module is ignoring proxy environment

I'm working on some server with limited internet connexion (need to use proxy settings)

On some module, i need to set environment otherwise i get a timeout.

It's a main issue for me as i don't want to have duplication code and/or specific code for limited internet machine (just need to specifigy http_proxy on them)

ANSIBLE VERSION

ansible 2.3.2.0
config file = /etc/ansible/ansible.cfg
configured module search path = Default w/o overrides
python version = 2.7.14 (default, Nov 3 2017, 10:55:25) [GCC 7.2.1 20170915 (Red Hat 7.2.1-2)]

CONFIGURATION

OS / ENVIRONMENT

Tested on remote server Ubuntu 14.04.4 LTS

SUMMARY

Same kinf of issue as for module apt_key ( #31691 )

STEPS TO REPRODUCE

groups_var.yml :

environment:
http_proxy: "http://172.31.255.254:8888"
https_proxy: "http://172.31.255.254:8888"

This tast is going in timeout :

- name: Ensure any remote plugins defined are present
shell: umask 0022; sensu-install -p ftp

If set this environment in tasks it's working as expected :

- name: Ensure any remote plugins defined are present
shell: umask 0022; sensu-install -p ftp
environment:
http_proxy: 'http://172.31.255.254:8888'
https_proxy: 'http://172.31.255.254:8888'

EXPECTED RESULTS

Pass thought proxy

ACTUAL RESULTS

Timeout as it's not use the proxy environment

Issue 2:
ISSUE TYPE

Bug Report

COMPONENT NAME

cron

ANSIBLE VERSION

ansible 2.3.1.0

CONFIGURATION

OS / ENVIRONMENT

FreeBSD on server, CentOS on client

SUMMARY

If cron job is on the last line of crontab, it gets duplicated on every play.

STEPS TO REPRODUCE

Apply cron job task while the target crontab is an empty (0 bytes) or nonexistent file.

Repeat play.

EXPECTED RESULTS

Cron job task is 'ok' on second run, no changes.

ACTUAL RESULTS

Cron job task appends duplicate job on every run.

If I manually add an empty line to the end of the crontab, after managed job, idempotency is restored.
Issue 1:
Currently to load pretrained tensor into an Embedding I am running:

embedding = torch.nn.Embedding(length, dim)
embedding.weight.data = pretrained_vectors_tensor

This first initializes a tensor of size length x dim which can be quite large and then throws all of that away. When creating the embedding there are two big tensors in memory which fill up my RAM, while there could be only one if there was a possibility to initialize weights from a parameter.

Issue 2:
Summary

Loading pre-trained embeddings is common practice with today's Embeddings. Most often than not, people use large pre-trained Embeddings such as Word2Vec, Glove or FastText with their models. As such there should be an easy and simple way of doing this so common operation.

Current Behaviour

To load a pre-trained embedding one has to do the following:

e = nn.Embedding(num_embedding, embedding_dim)
e.weight = nn.Parameter(pretrained_embedding)

This involves converting a Tensor to Variable and access the class' arguments to override them.

Suggested Behaviour

I propose doing this either with an explicit method, or an optional argument in the __init__ function.

This way you could either initialize an embedding as:

e = nn.Embedding(num_embedding, embedding_dim, pretrained=pretrained_embedding)

or

e = nn.Embedding(num_embedding, embedding_dim)
e.load_pretrained(pretrained_embedding)

Let me know what you think. I'll do a PR as soon I have some feedback from you guys.

Thank you
Issue 1:
plt.title("title")
plt.gca().twiny().set_xlabel("xlabel")
plt.tight_layout()

matplotlib 1.5.0.

Issue 2:
This is an issue that came up in http://trac.sagemath.org/13625

When subplot.xaxis.tick_top() is used, then the position of the plot title is not recomputed. Ideally, it should be shifted further up. Example code is as follows

from matplotlib.figure import Figure
figure = Figure()
subplot = figure.add_subplot(111)
subplot.plot(range(10),range(10))
subplot.xaxis.tick_top()
subplot.set_title('a title')
from matplotlib.backends.backend_agg import FigureCanvasAgg
figure.set_canvas(FigureCanvasAgg(figure))
figure.savefig('/tmp/a.png')
Issue 1:
Is there an existing issue for this?

I have searched the existing issues

This issue exists in the latest npm version

I am using the latest npm

Current Behavior

When running npm ci in a directory with an existing node_modules that contains a .bin directory, the command fails with the following exception:

43 verbose stack SystemError [ERR_FS_EISDIR]: Path is a directory: rm returned EISDIR (is a directory) {PATH_TO_PROJECT}/node_modules/.bin
43 verbose stack at new SystemError (node:internal/errors:250:5)
43 verbose stack at new NodeError (node:internal/errors:361:7)
43 verbose stack at node:internal/fs/utils:784:17
43 verbose stack at callback (/usr/local/lib/node_modules/npm/node_modules/graceful-fs/polyfills.js:306:20)
43 verbose stack at FSReqCallback.oncomplete (node:fs:191:5)

Expected Behavior

Expect npm ci to work regardless of if the node_modules directory already exists.

Steps To Reproduce

With this config

Sample package.json

{
"name": "esbuild-binary",
"version": "1.0.0",
"description": "",
"main": "index.js",
"author": "",
"license": "ISC",
"dependencies": {
"esbuild": "^0.14.13"
}
}

Generate lockfile then run npm ci

Environment

npm: 9.3.0

Node.js: 19.4.0

OS Name: MacOS Monterey 12.6.1

System Model Name: Macbook Pro

Issue 2:
I am using a link from

https://github.com/ionicthemes/ionic-facebook-login

and use this theme in app building first I have clone it then we use ionic serve and got error

ng.cmd run app:serve --host=localhost --port=8100

[ng] An unhandled exception occurred: Could not find module "@angular-devkit/build-angular" from "C:\Users\Avaneesh Shukla\Desktop\Avaneesh\facebook\ionic-facebook-login".

[ng] See "C:\Users\AVANEE~1\AppData\Local\Temp\ng-Dp5pqX\angular-errors.log" for further details.

[ERROR] ng has unexpectedly closed (exit code 127).

The Ionic CLI will exit. Please check any output above for error details.

then use npm install and got error

C:\Users\Avaneesh Shukla\Desktop\Avaneesh\facebook\ionic-facebook-login>npm install

npm ERR! cb() never called!

npm ERR! This is an error with npm itself. Please report this error at:

npm ERR! https://npm.community

npm ERR! A complete log of this run can be found in:

npm ERR! C:\Users\Avaneesh Shukla\AppData\Roaming\npm-cache_logs\2019-12-25T18_14_36_368Z-debug.log

this is happen after anguler new version came
Issue 1:
Current Behavior:

If a non-namespaced package is published with

npm publish --access restricted

npm ignored the user directive that access should be restricted and publishes the package.

Expected Behavior:

Attempting to publish a package which isn't namespaced with npm publish --access restricted should

warn the user that packages without namespaces cannot be given restricted access

exit with an error

Steps To Reproduce:

Create a non-namespaces NPM package

Publish is using npm publish --access restricted

Issue 2:
What / Why

Hey, I've npm publish'd a library to npm's public registry and it finished successfully, I received an email claiming it is published and when I search the exact name on npmjs.com it shows on the dropdown.

However, I don't see it on my user page and it's package page returns 404.

I also tried publishing another version, again, seems successful, received an email, the package page is still unavailable.

Package name: browser-jsonstore

Links:

Package page: https://www.npmjs.com/package/browser-jsonstore

My user page: https://www.npmjs.com/~bluzi

When

I published it yesterday, about 15 hours ago.

Where

npm publish registry

How

Current Behavior

npm publish works but the package page is missing

Steps to Reproduce

I assume it won't reproduce every time you'll run npm publish, but anyway:

Create a new package (npm init)

Publish it - npm publish

Try browsing into its package page on npmjs.com

Or

Go to npmjs.com

Type browser-jsonstore into the search bar

Wait for the dropdown to appear, see how it has a valid description, etc

Click on it

See how it returns Error 404

Expected Behavior

Package page should be available after publishing a package

Who

I'm not sure who is responsible for this kind of issues

References

n/a
Issue 1:
System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): no

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 16.04

TensorFlow installed from (source or binary): source

TensorFlow version (use command below): v1.3.1

Python version: 3.5

Bazel version (if compiling from source): 0.6.0

CUDA/cuDNN version:

GPU model and memory:

Exact command to reproduce: bazel build //tensorflow/tools/pip_package:build_pip_package

Describe the problem

With bazel version 0.6.0, building on the latest stable tagged release (v1.3.1) is not possible. I get the following error:

ERROR: /home/ubuntu/.cache/bazel/_bazel_ubuntu/8ea56cff279f39ec6c0003641e649819/external/io_bazel_rules_closure/closure/private/defs.bzl:27:16: The `set` constructor for depsets is deprecated and will be removed. Please use the `depset` constructor instead. You can temporarily enable the deprecated `set` constructor by passing the flag --incompatible_disallow_set_constructor=false
ERROR: error loading package '': Extension file 'closure/private/defs.bzl' has errors
ERROR: error loading package '': Extension file 'closure/private/defs.bzl' has errors
INFO: Elapsed time: 0.569s
FAILED: Build did NOT complete successfully (0 packages loaded)

When I pass the flag mentioned in the error message (--incompatible_disallow_set_constructor=false) to the pip package build command, I get a similar message:

ERROR: /home/ubuntu/.cache/bazel/_bazel_ubuntu/8ea56cff279f39ec6c0003641e649819/external/org_python_pypi_backports_weakref/BUILD.bazel:17:1: no such package '@org_python_license//': The `set` constructor for depsets is deprecated and will be removed. Please use the `depset` constructor instead. You can temporarily enable the deprecated `set` constructor by passing the flag --incompatible_disallow_set_constructor=false and referenced by '@org_python_pypi_backports_weakref//:license'
ERROR: Analysis of target '//tensorflow/tools/pip_package:build_pip_package' failed; build aborted
INFO: Elapsed time: 0.626s
FAILED: Build did NOT complete successfully (28 packages loaded)
currently loading: tensorflow/core

This problem does not exist on master. It would be very useful to have even a tiny stable release (v1.3.2 or something) to include this fix for those of us that like to build against a stable release.

Issue 2:
Issue to trace effort of swig interface for java. Started implementation - will update with progress. If anyone has any comments/tips - please feel welcome to join the discussion!
Issue 1:
Problem

Recently, it was found that there is no option to draw bright dot spherical symbols in 2D during drawing. I don't know how to solve this problem

Proposed solution

No response

Issue 2:
Feature Request

I would like to know if Matplotlib could provide the functionality to plot using markers with gradient filling in scatter / plot, such that the markers in an 2D plot may mimic the appearance of a glossy 3D object, as the sample png attached (the gradient is post processed by Inkscape, however).

Currently it might be possible to mimick the gradient by adding multiple numbers of marker layers, with calculated size, or either use offsetbox by sticking images, but neither of them is compatible with legend.

I guess this could be a feature based on the backend, as this might not be simply achieved by specifying the facecolor of the marker. I would like to know if there is a specific backend (e.g. svg) which could achieve such behavior. Thank you!
Issue 1:
System information

-Google Colab

-GPU

-Python 3

-Tensorflow tf-nightly-2.0-preview

When I write the code:

from __future__ import absolute_import, division, print_function

!pip install tf-nightly-2.0-preview

import tensorflow as tf

tf.enable_eager_execution()

It gives me the error AttributeError: 'module' object has no attribute 'enable_eager_execution'

whereas when I simply run the code as:

from __future__ import absolute_import, division, print_function

!pip install tf-nightly-2.0-preview

import tensorflow as tf

tf.executing_eagerly()

It returns 'True', which means that eager execution is working by default without even enabling it before.

If I proceed without using tf.enable_eager_execution() in this collab tutorial https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/r2/tutorials/generative/image_captioning.ipynb#scrollTo=U8l4RJ0XRPEm

In the cell Caching the features extracted from InceptionV3,

the last line with "np.save(path_of_feature, bf.numpy())" gives me an error saying "Tensor has no attribute called numpy()"

If I see the type() of batch_features in the line:

batch_features = image_features_extract_model(img) line is returning a graph tensor, instead of an eager tensor. It's not supposed to do that. Does this means that eager execution is not yet enabled or is this a bug.

Issue 2:
System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): NO

OS Platform and Distribution (e.g., Linux Ubuntu 16.04):Colab

Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:

TensorFlow installed from (source or binary): pip

TensorFlow version (use command below): 2.0

Python version:3.x

Describe the current behavior

When running the code in

Caching the features extracted from InceptionV3

AttributeError: 'Tensor' object has no attribute 'numpy'

Describe the expected behavior

Code to reproduce the issue

Provide a reproducible test case that is the bare minimum necessary to generate the problem.

for img, path in tqdm(image_dataset):

batch_features = image_features_extract_model(img)

batch_features = tf.reshape(batch_features,

(batch_features.shape[0], -1, batch_features.shape[3]))

for bf, p in zip(batch_features, path):

path_of_feature = p.numpy().decode("utf-8")

np.save(path_of_feature, bf.numpy())

Other info / logs

AttributeError Traceback (most recent call last)

in ()

15 for bf, p in zip(batch_features, path):

16 path_of_feature = p.numpy().decode("utf-8")

---> 17 np.save(path_of_feature, bf.numpy())

AttributeError: 'Tensor' object has no attribute 'numpy'
Issue 1:
This strange behavior came up in pandas-dev/pandas#19813:

In [23]: np.array([], dtype=object).sum()
Out[23]: False

In [26]: np.array([], dtype=object).prod()
Out[26]: True

It's almost as if NumPy picks a dtype at random (e.g., bool in this case) to use for computing the result. A more obviously and likely more consistent choice would be to use the identities for the appropriate ufuncs (i.e., np.add.identity and np.mul.identity), which are the integers 0 and 1, respectively.

Issue 2:
This is pretty weird:

>>> arr = np.array([1, 2, 3], dtype=object)
>>> np.add.reduce(arr)
6
>>> np.add.reduce(arr[:0])
False
>>> np.multiply.reduce(arr)
6
>>> np.multiply.reduce(arr[:0])
True

It happens for array results too:

>>> arr = np.array([[1], [2], [3]], dtype=object)
>>> np.add.reduce(arr[:0], axis=0)
array([False], dtype=object)
>>> np.multiply.reduce(arr[:0], axis=0)
array([True], dtype=object)
Issue 1:
System Info

(Possible duplicate: #10256)

I have written a custom tokenizer that builds on top of BertTokenizer (returns one extra list of ids that will later be embedded in a custom model). I have pushed it to Hub as well. Now, how can I allow others to use it? The code for the tokenizer is uploaded to Hub along with the code for the model (they are in the same file), but since I cannot register the tokenizer with AutoTokenizer like I can do for models (CustomModel.register_for_auto_class("AutoModel")), others cannot load this tokenizer, and hence use the model.

Is there a workaround for this?

Version: 4.27.4

Who can help?

@ArthurZucker @younesbelkada

Information

The official example scripts

My own modified scripts

Tasks

An officially supported task in the examples folder (such as GLUE/SQuAD, ...)

My own task or dataset (give details below)

Reproduction

The code for both the tokenizer and model can be found here: https://huggingface.co/mcgill-babylm/bert_ds10M_np512_nh2_nl2_hs128_postags_ungrouped/blob/main/pos_bert.py

I am able to load the model with no problems since I push it after registering it as follows

BertForMaskedLMWithPOSEmb.register_for_auto_class("AutoModel")
BertForMaskedLMWithPOSEmb.register_for_auto_class("AutoModelForMaskedLM")

Expected behavior

I should be able to register custom tokenizers with AutoTokenizer (which might be a new feature request) or work around it somehow to allow other users to use a custom tokenizer.

Issue 2:
Model description

I would like to implement a new model architecture.

Short description

RWKV v2 is an "RNN with transformer-level performance, without using attention. Similar to Apple's Attention Free Transformer. All trained models open-source. Inference is very fast (even on CPUs) and might work on cell phones. There's also a GPT-type implementation." -- (Hochreiter's description)

RWKV v2 is parallelizable because the time-decay of each channel is data-independent (and trainable). For example, in usual RNN you can adjust the time-decay of a channel from say 0.8 to 0.5 (these are called "gates"), while in RWKV v2 you simply move the information from a W-0.8-channel to a W-0.5-channel to achieve the same effect. RWKV can leverage GPUs, but doesn't need to.

Open source status

The model implementation is available

The model weights are available

Provide useful links for the implementation

Implementation and weights

There's an implementation at BlinkDL/RWKV-LM which also gives a detailed description of the model internals and some performance benchmarks. Model weights currently are being trained for a few datasets, including the Pile (see e.g. BlinkDL/RWKV-v2-RNN-Pile) and Danish Gigaword by me. Both will be openly available - some checkpoints for the Pile already are, even though it's an ongoing process.

Status

The model seems quite exciting and I'm able to replicate preliminary results. I'm already talking with @BlinkDL about the implementation. I'm happy to implement/port the model architecture (for both RNN and GPT variants), tokenizer, and tests myself (and have already started) and would appreciate help and advice.