WhereIsAI/github-issue-similarity huggingface dataset:
Issue 1:
A minor observation about an otherwise great feature! Using the Insiders build, the following Lua code causes weird folding:

function someFunc()

local longString = [[this is a long
string that spans multiple lines
and has some weird folding]]

print longString
end

You'll notice that a + shows up on the same "function someFunc()" line and on the last line of the longString declaration. The first + folds up to the declaration line and the second + folds the rest of the function.

Issue 2:
The current implementation of folding uses an indentation based folding strategy that is unaware of the language it works on. Knowledge on the underlying language allows us to solve the following requests:

[folding] Cannot code fold empty functions #3349

[folding] Collapse ending brace to the same line #3352

[folding] Folded block comments should show */ #3350

[folding] should not fold whitespace after function #3353

[folding] Add code folding for markdown based on heading level #3347

[folding] [lua] Weird code folding with Lua #3602

[folding] Collapse code block ignore comments inside this block #3957

[folding] Code Folding: support golang multiline strings #5994

[folding] Optionally fold chained method calls #6991
Issue 1:
Hello, This is a step by step of what I remember happening leading up to the duplication of the file.

Opened a Wordpress theme package in Atom, when I was finished I closed that window (I did not close atom).

Selected 'Open...' from the menu and opened a different project.

Opened a few pages and made some edits.

Opened settings and saw that a theme was out of date so I updated it.

Pasted some info from a website into a file and saved it.

It was when hitting 'control s' that I noticed a new file appear (page.php):

This file was from the previous Wordpress project and it was now in the new project.

I can't recall if I had been editing that file or if it was open when I closed the previous project.

I have since upgraded to version 0.132.0 (The request to upgrade was in the menu) So I guess the version was the one before this.

The theme I updated was Monokai (https://atom.io/packages/monokai).

Issue 2:
Halp ticket:

support/95b88d8ac5d011e388dbf5393005f408

Please make Atom ask before downloading an update or make the auto-update configurable.

I am working on the road a lot and it ruins my mobile data package.

So, asking before downloading an update or providing an option to turn off auto-updating might be helpful in these cases, I suppose.
Issue 1:
http://getbootstrap.com/customize/

I have this error while compilating a custom packet

Ruh roh! Could not parse less files.
.btn-group-xs > .btn { .btn-xs(); }
.btn-group-sm > .btn { .btn-sm(); }

I try to create packet with only:

Grid system, Forms, Button groups, Input groups, Labels, Alerts

Issue 2:
Could not parse less files.

.btn-group-xs > .btn { .btn-xs(); }

.btn-group-sm > .btn { .btn-sm(); }
Issue 1:
Describe the issue:

numpy 1.20 encouraged specifying plain bool as a dtype as an equivalent to np.bool_, but these aliases don't behave the same as the explicit numpy versions. mypy infers the dtype as "Any" instead. See the example below, where I expected both lines to output the same type.

Reproduce the code example:

import numpy as np

def what_the() -> None:
reveal_type(np.arange(10, dtype=bool))
reveal_type(np.arange(10, dtype=np.bool_))

Error message:

No error, but output from mypy 0.961:

show_type2.py:4: note: Revealed type is "numpy.ndarray[Any, numpy.dtype[Any]]"
show_type2.py:5: note: Revealed type is "numpy.ndarray[Any, numpy.dtype[numpy.bool_]]"

### NumPy/Python version information:

1.23.0 3.10.5 (main, Jun 11 2022, 16:53:24) [GCC 9.4.0]

Issue 2:
Back in #17719 the first steps were taken into introducing static typing support for array dtypes.

Since the dtype has a substantial effect on the semantics of an array, there is a lot of type-safety

to be gained if the various function-annotations in numpy can actually utilize this information.

Examples of this would be the rejection of string-arrays for arithmetic operations, or inferring the

output dtype of mixed float/integer operations.

The Plan

With this in mind I'd ideally like to implement some basic dtype support throughout the main numpy

namespace (xref #16546) before the release of 1.22.

Now, what does "basic" mean in this context? Namely, any array-/dtype-like that can be parametrized

w.r.t. np.generic. Notably this excludes builtin scalar types and character codes (literal strings), as the

only way of implementing the latter two is via excessive use of overloads.

With this in mind, I realistically only expect dtype-support for builtin scalar types (e.g. func(..., dtype=float))

to-be added with the help of a mypy plugin, e.g. via injecting a type-check-only method into the likes of

builtins.int that holds some sort of explicit reference to np.int_.

Examples

Two examples wherein the dtype can be automatically inferred:

from typing import TYPE_CHECKING
import numpy as np

AR_1 = np.array(np.float64(1))
AR_2 = np.array(1, dtype=np.float64)

if TYPE_CHECKING:
reveal_type(AR_1) # note: Revealed type is "numpy.ndarray[Any, numpy.dtype[numpy.floating*[numpy.typing._64Bit*]]]"
reveal_type(AR_2) # note: Revealed type is "numpy.ndarray[Any, numpy.dtype[numpy.floating*[numpy.typing._64Bit*]]]"

Three examples wherein dtype-support is substantially more difficult to implement.

AR_3 = np.array(1.0)
AR_4 = np.array(1, dtype=float)
AR_5 = np.array(1, dtype="f8")

if TYPE_CHECKING:
reveal_type(AR_3) # note: Revealed type is "numpy.ndarray[Any, numpy.dtype[Any]]"
reveal_type(AR_4) # note: Revealed type is "numpy.ndarray[Any, numpy.dtype[Any]]"
reveal_type(AR_5) # note: Revealed type is "numpy.ndarray[Any, numpy.dtype[Any]]"

In the latter three cases one can always manually declare the dtype of the array:

import numpy.typing as npt

AR_6: npt.NDArray[np.float64] = np.array(1.0)

if TYPE_CHECKING:
reveal_type(AR_6) # note: Revealed type is "numpy.ndarray[Any, numpy.dtype[numpy.floating*[numpy.typing._64Bit*]]]"
Issue 1:
You need to double-transpose vectors to make them into 1-column matrices so that

you can add them to 1-column matrices or multiply them with one-row matrices (on

either side).

Issue 2:
julia> X = [ i^2 - j | i=1:10, j=1:10 ];

julia> typeof(X)
Array{Int64,2}

julia> X[:,1]
10x1 Int64 Array
0
3
8
15
24
35
48
63
80
99
Issue 1:
In Bootstrap 3.0.3, when I use the "table table-condensed table-bordered table-striped" classes on a table, the table-striped defeats all the table contextual classes (.success, .warning, .danger, .active) in its rows or cells.

When only the table-striped class is removed, the contextual classes then work perfectly within the rest of the table-level classes listed above.

I tried substituting the BS CSS "table-striped" rule so it would colorize the even rows instead of the odd, but it still fails.

Is this a bug or by design?

Issue 2:
i have table with class .table-striped. it has 3 rows with tr.danger. only the middle one is red, the other two are default color.

when i remove .table-striped, it works correctly
Issue 1:
TypeScript Version:

1.9.0-dev / nightly (1.9.0-dev.20160311)

Code

class ModelA<T extends BaseData>
{
public prop : T;

public constructor($prop : T) { }

public Foo<TData, TModel>(
$fn1 : ($x : T) => TData,
$fn2 : ($x : TData) => TModel) { }

public Foo1<TData, TModel extends ModelA<any>>(
$fn1 : ($x : T) => TData,
$fn2 : ($x : TData) => TModel) { }

public Foo2<TData extends BaseData, TModel extends ModelA<TData>>(
$fn1 : ($x : T) => TData,
$fn2 : ($x : TData) => TModel) { }
}

class ModelB extends ModelA<Data> { }

class BaseData
{
public a : string;
}

class Data extends BaseData
{
public b : Data;
}

class P
{
public static Run()
{
var modelA = new ModelA<Data>(new Data());

modelA.Foo(x1 => x1.b, x2 => new ModelB(x2));

modelA.Foo1(x1 => x1.b, x2 => new ModelB(x2));

// Why is this not working??? inferred type for x2 : BaseData
modelA.Foo2(x1 => x1.b, x2 => new ModelB(x2)); // Error
}
}

Expected behavior:

modelA.Foo2 call should infer the type.

Actual behavior:

Is not inferring the actual type.

Issue 2:
TypeScript Version:

1.8.0

Code

interface Class<T> {
new(): T;
}
declare function create1<T>(ctor: Class<T>): T;
declare function create2<T, C extends Class<T>>(ctor: C): T;

class A {}
let a1 = create1(A); // a: A --> OK
let a2 = create2(A); // a: {} --> Should be A

Context

The example above is simplified to illustrate the difference between create1 and create2. I need both type parameters for the use case I have in mind (React) because it returns a type which is parameterized by both T and C:

declare function createElement<T, C extends Class<T>>(type: C): Element<T, C>;
var e = createElement(A); // e: Element<{}, typeof A> --> Should be Element<A, typeof A>

declare function render<T>(e: Element<T, any>): T;
var a = render(e); // a: {} --> Should be A

Again, this is simplified, but the motivation is to improve the return type inference of ReactDOM.render.
Issue 1:
deno: 0.21.0

v8: 7.9.304

typescript: 3.6.3

Run this code in both node.js and deno:

let o = {};
o.a = 1;
o.b = 2;
o.c = 3;
o.d = 4;
o.e = 5;
o.f = 6;
console.log(o);

Node.js output: { a: 1, b: 2, c: 3, d: 4, e: 5, f: 6 }

deno output: { a, b, c, d, e, f }

So, if there are six or more properties, deno's console.log doesn't show the property values.

Issue 2:
Match colors and semantics.

node's stringify has been tweaked and optimized over many years, changing anything is likely surprising in a bad way.
Issue 1:
Julia has kick ass features for computation.

However what is the point of computing so much if the data cannot be visualized/inspected ?

Current 2d plotting capabilities are nice, but no real matlab/scilab/scipy competitor would be credible without some kind of 3d plotting.

One way of going would be to "port" mayavi to Julia.

http://github.enthought.com/mayavi/mayavi/auto/examples.html

Mayavi builds on top of VTK, so the 3d system itself would not be reinvented from scratch.

I think this would be relevant feature to have for release 2.0...

ps: 2d or 3d plotting systems in Julia should have SVG/PDF output, so it can be used in scientific publishing...

Issue 2:
I would like to discuss moving the Julia buildbots to something more maintainable than buildbot. It's worked well for us for many years, but the siloing of configuration into a separate repository, combined with the realtively slow pace of development as compared to many other competitors (and also the amount of cruft we've built up to get it as far as it is today) means it's time to move to something new.

Anything we use must have the following features:

Multi-platform; it must support runners on all platforms Julia itself is built on. This encompasses:

Linux: x86_64 (glibc and musl), i686, armv7l, aarch64, ppc64le

Windows: x86_64, i686

MacOS: x86_64, aarch64

FreeBSD: x86_64

I would like the build configuration to live in an easily-modifiable format, such as a .yml file in the Julia repository. It's nice if we can test out different configurations just by making a PR against a repository somewhere.

Possible options include:

GitLab CI (Note: currently missing linux ppc64le support)

buildkite

Azure Pipelines (Note: incomplete runner support)

GitHub Actions (Note: incomplete runner support)

I unfortunately likely won't have time to do this all by myself, but if we could get 1-2 community members interested in learning more about CI/CD and who want to help have a hand in bringing the Julia CI story into a better age, I'll be happy to work alongside them.
Issue 1:
Hi,

If I use a custom distDir:

the following error appears in the console: http://myUrl/_next/-/page/_error / 404 not found

hot reloading does not work properly anymore

If I go back to the default, it goes away.

my next.config.js file:

// see source file server/config.js
module.exports = {
webpack: null,
poweredByHeader: false,
distDir: '.build',
assetPrefix: ''
}

Thanks,

Paul

Issue 2:
I cannot get the custom distDir to work. Whenever I add a next.config.js file and set the distDir as described in the documentation

module.exports = {
distDir: 'build'
};

the build is created in that directory, but there is always an exception being thrown in the browser when loading the page.

Error when loading route: /_error
Error: Error when loading route: /_error
at HTMLScriptElement.script.onerror

What am I missing? This is reproducible with the simple hello world example.
Issue 1:
Worked around in CI by pinning to 1.8.5, see gh-9987

For the issue, see e.g. https://circleci.com/gh/scipy/scipy/12334. It ends with:

!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
!
./scipy-ref.tex:25: fontspec error: "font-not-found"
!
! The font "FreeSerif" cannot be found.
!
! See the fontspec documentation for further information.
!
! For immediate help type H <return>.
!...............................................

l.25 ]

No pages of output.
Transcript written on scipy-ref.log.
Latexmk: Log file says no output from latex
Latexmk: For rule 'pdflatex', no output was made
Latexmk: Errors, so I did not complete making targets
Collected error summary (may duplicate other messages):
pdflatex: Command for 'pdflatex' gave return code 256
Latexmk: Use the -f option to force complete processing,
unless error was exceeding maximum runs of latex/pdflatex.
Makefile:32: recipe for target 'scipy-ref.pdf' failed
make: *** [scipy-ref.pdf] Error 12
make: Leaving directory '/home/circleci/repo/doc/build/latex'
Exited with code 2

Issue 2:
Original ticket http://projects.scipy.org/scipy/ticket/1889 on 2013-04-10 by trac user avneesh, assigned to unknown.

Hello all,

I notice a somewhat bizarre issue when constructing sparse matrices by initializing with 3-tuples (row index, column index, value).

The following is a slight abstraction to what my exact code is, but it shows the behavior:

kNN = 10
dataset_size = 1661165
rowIdx = np.empty((kNN+1)*dataset_size)
colIdx = np.empty((kNN+1)*dataset_size)
vals = np.empty((kNN+1)*dataset_size)
for i, line in enumerate(data):
#perform certain operations
print vals.size, colIdx.size, rowIdx.size
print vals[np.nonzero(vals)].size
W = sp.csc_matrix((vals, (rowIdx, colIdx)), shape=(dataset_size, dataset_size))
print W.nnz

The printed outputs I get are the following:

18272815 18272815 18272815
18272815
18272465

Therefore, as you can see, there is a difference of 18272815-18272465 = 350 elements that should be non-zero in the resulting sparse matrix, but are not.

I have verified in the rowIdx and colIdx arrays that there are no duplicates, i.e., a given (rowIdx, colIdx) pair does not appear twice (otherwise two values would map to the same position in the sparse matrix). As per my understanding, I should get 18272815 elements in the resulting sparse matrix, but I fall 350 elements short.

Is this expected behavior? Am I doing something wrong?

I am running Linux x86-64-bit OpenSuSE 11.4, NumPy version 1.5.1, SciPy version 0.9.0, Python 2.7.
Issue 1:
With version 3.1.0, the HintManagerHolder.clear method is executed each time SQL is executed?

When using HintShardingAlgorithm, you need to set HintManagerHolder before executing SQL. I execute multiple SQL in Dao, or execute multiple Dao methods in a service. I want to set HintManagerHolder in the form of AOP interception before calling the method. Execute clear after the end, there is no way to achieve

Issue 2:
Question

Why DriverJDBCExecutor#executor do not handle the error result of branch thread execution？

DriverJDBCExecutor#executor：

JDBCExecutorCallback#execute：

In this way, the result of the overall sql execution is inconsistent with the expected result.

eg:

insert into xx_table (id,xxx,xxx) values (1,xx,xxx),(2,xxx,xxx);

This statement will route to two datasources, ds0, ds1.

If there is a row with primary key 1 in ds1, it will throw exception: Duplicate entry '1' for key 'PRIMARY';

The final result is: the sql routed to ds0 is successfully executed, and the sql execution of ds1 fails.

Why is it not processed as all failures, which leads to data inconsistency, and distributed transactions will not be rolled back
Issue 1:
Hi

I have a select box that has to contain the following options. Mention the same label for the options "audi" and "ford".

<select>
<option value="volvo">Volvo</option>
<option value="saab">Saab</option>
<option value="opel">Opel</option>
<option value="audi">Audi</option>
<option value="ford">Audi</option>
</select>

When I try to render this in Symfony 2.7, my FormType looks like this.

$builder->add('brand', 'choice', array(
'choices' => array(
'volvo' => 'Volvo',
'saab' => 'Saab',
'opel' => 'Opel',
'audi' => 'Audi',
'ford' => 'Audi'
)
));

I would assume all 5 fields are going to get rendered. In fact, only 4 get rendered and the view looks like this:

<select id="...">
<option value="volvo">Volvo</option>
<option value="saab">Saab</option>
<option value="opel">Opel</option>
<option value="ford">Audi</option>
</select>

It seems the option with value "audi" is overriden by the option with value "ford". I don't know if this is standard behaviour or a bug, but it's quite annoying. Can any of you help me?

Thanks in advance!

Issue 2:
Q
A

Bug report?
no

Feature request?
yes

BC Break report?
?

RFC?
?

Symfony version
3.2.7

In the documentation for data transformers, the input is validated in the IssueToNumberTransformer class, and the error message is set in the TaskType class. What if there are multiple types of invalid input, can there be a way to have multiple error messages?

I know this can be done with Symfony\Component\Validator\Constraints in the Entity class, but since validation must also take place in the data transformer, I think there should be a way to have more control over the error messages.
Issue 1:
Please make sure that this is a build/installation issue. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:build_template

System information

OS : Windows 10

TensorFlow installed from (source or binary): from pip

TensorFlow version: 1.11.0

Python version: 3.6

Installed using virtualenv? pip? conda?: conda

Bazel version (if compiling from source): No

GCC/Compiler version (if compiling from source):

CUDA/cuDNN version: 10

GPU model and memory: Nvidia 1050Ti

Describe the problem

Recently I tried updating my tensor flow and later on, my tensorflow-gpu stopped working.

Now I have downgraded to 1.11.0 but then still my tensorflow-gpu is not working.

nvidia-smi is working fine.

+-----------------------------------------------------------------------------+

| NVIDIA-SMI 416.34 Driver Version: 416.34 CUDA Version: 10.0 |

|-------------------------------+----------------------+----------------------+

| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |

| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |

|===============================+======================+======================|

| 0 GeForce GTX 105... WDDM | 00000000:01:00.0 Off | N/A |

| N/A 48C P8 N/A / N/A | 78MiB / 4096MiB | 0% Default |

+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+

| Processes: GPU Memory |

| GPU PID Type Process name Usage |

|==================================================|

| No running processes found |

+-----------------------------------------------------------------------------+

tf.test.is_gpu_available(

cuda_only=False,

min_cuda_compute_capability=None

)

I ran this, its coming false

then ,

I ran this sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))

Device mapping: no known devices.

2018-11-26 17:27:10.277030: I tensorflow/core/common_runtime/direct_session.cc:291] Device mapping:

it's coming empty, I read lot of threads usually people had error with cuda, by installing that it worked for lot of people.

But then in my case things were working fine earlier, now it got messed up and cuda is installed properly.

why should I do?

Issue 2:
Hi:

my program have bug in these code line:

word_embeddings = tf.scatter_nd_update(var_output, error_word_f, sum_all)
word_embeddings_2 = tf.nn.dropout(word_embeddings, self.dropout_pl)

The error hint as follows:

ValueError: Tensor conversion requested dtype float32_ref for Tensor with dtype float32: 'Tensor("dropout:0", shape=(), dtype=float32)

it looks like word_embeddings 's dtype is float32_ref but actual the function tf.nn.dropout need word_embeddings dtype float32 ,how can i convert word_embeddings's dtype float32_ref to float32 before run tf.nn.dropout(word_embeddings, self.dropout_pl)?
Issue 1:
https://babeljs.io/repl/#?experimental=true&evaluate=true&loose=false&spec=false&playground=false&code=%40test(()%20%3D%3E%20123)%0Aclass%20A%20%7B%7D%0A%0A%40test(()%20%3D%3E%20%7B%20return%20123%20%7D)%0Aclass%20B%20%7B%7D%0A%0A%40test(function()%20%7B%20return%20123%20%7D)%0Aclass%20%D0%A1%20%7B%7D

Issue 2:
Compare the output of these two to show the difference.

https://babeljs.io/repl/#?experimental=true&evaluate=true&loose=false&spec=false&code=%40decorate((arg)%20%3D%3E%20null)%0Aclass%20Example1%20%7B%0A%7D%0A%0A%40decorate(arg%20%3D%3E%20null)%0Aclass%20Example2%20%7B%0A%7D

@decorate((arg) => null)
class Example1 {
}

@decorate(arg => null)
class Example2 {
}