WhereIsAI/github-issue-similarity huggingface dataset:
Issue 1:
Hi -

I'm running into a problem where I've got a few icons in the upper right of the browser. They have tooltips on them, but the right-most icon's tooltip gets horizontally compressed, rather than staying full width and the tooltip itself moving.

This is pretty hard to describe... but I hope you can imagine what I'm talking about. The docs/examples don't illustrate the problem, but whenever the tooltip is too close to the edge of the browser window, it just changes the horizontal width, keeping the tooltip "centered" as much as possible.

I think this could be fixed if the body of the tooltip slid the opposite direction of the side of the window that it would otherwise overflow.

I'll try to come up with a picture example...anyone have any solutions?

Thanks,

Matt

Issue 2:
Hi -

I'm running into a problem where I've got a few icons in the upper right of the browser. They have tooltips on them, but the right-most icon's tooltip gets horizontally compressed, rather than staying full width and the tooltip itself moving.

This is pretty hard to describe... but I hope you can imagine what I'm talking about. The docs/examples don't illustrate the problem, but whenever the tooltip is too close to the edge of the browser window, it just changes the horizontal width, keeping the tooltip "centered" as much as possible.

I think this could be fixed if the body of the tooltip slid the opposite direction of the side of the window that it would otherwise overflow.

I'll try to come up with a picture example...anyone have any solutions?

Thanks,

Matt
Issue 1:
Symfony\Component\Validator\Constraints\All have not protection from send array into it and throw php error.

Also if I try use with

Symfony\Component\Validator\Constraints\Choose(array('',

new Symfony\Component\Validator\Constraints\All()))

This nesting just not working if I send array into it.

Issue 2:
I've run into a problem while validating date value:

$errors = $validator->validateValue($date, new Constraints\Date());

If $date is not a string (or object with __toString() method), code above will throw UnexpectedTypeException.

it's not what I expected to see, but I can understand the reasons laying behind this behaviour.

But after further investigation, I've found out that Constraints\Range (and maybe others) in the same situation just adds violation ("This value should be a valid number") without throwing the exception. So what is the correct behaviour for such cases? Should they both throw UnexpectedTypeException or add violation?
Issue 1:
Yes I know this has been discussed already :) but I'd like to start the discussion again with a nice summary of the arguments that were expressed:

@fabpot in #5748: For me, PHPunit is a tool and I'm not comfortable (yet) about adding this tool dep into the composer file.

PHPUnit is not just a tool, it's also a framework: we use PHPUnit classes to write the tests. So it's not just about downloading and running a phar, it's also about installing the code so that we can use it like any framework/library.

As a more concrete example, PHPUnit 4.5 has added important new features to the test framework and only by requiring it in Composer can you make sure people install the latest version.

@stof in #12136: Adding phpunit as a dev requirement means that each Travis build has to install 15 more composer packages (phpunit is not a small dependency)

@pborreli mentions this in #5748: "indeed, build takes 1 second more approx, which is not much"

Another reason I've read:

When working with VMs the global include path is not accessible on the host machine

For me autocompletion & co is completely broken because the IDE can't find the PHPUnit framework classes. It's much harder to work with tests. And by adding the global tools (i.e. global composer install) to PhpStorm I now have a lot of duplicated classes, which trips the IDE even more.

So hopefully we can open this discussion again and add PHPUnit to require-dev in composer.json :)?

Issue 2:
Just as a reminder for 4.0 to not forget #17379.

This PR makes Request::getMimeType(), Request::getFormat() and Request::setFormat() static.

@Tobion can you add to this issue the 4.0 milestone please?
Issue 1:
Do you want to request a feature or report a bug?

Potential bug

What is the current behavior?

Render react app produces warning in development mode:

Warning: getDefaultProps is only used on classic React.createClass definitions. Use a static property named `defaultProps` instead.

If the current behavior is a bug, please provide the steps to reproduce and if possible a minimal demo of the problem via https://jsfiddle.net or similar (template for React 16: https://jsfiddle.net/Luktwrdm/, template for React 15: https://jsfiddle.net/hmbg7e9w/).

create a new project Project A that uses create-react-class to create a Component ComponentX

build the project into a library (minify all js assets)

create another project Project B and include Project A as a dependency

Create another React Component const ComponentY extends React.Component, and use ComponentX from Project A

bundle Project B with production build

create a third project Project C, and include Project B as a dependency

build a react app that uses ComponentY from Project B

warnings!!!!

What is the expected behavior?

there should not have any warnings regarding create-react-class from a third party dependency

Which versions of React, and which browser / OS are affected by this issue? Did this work in previous versions of React?

React version 15.6.2

One quick example is installing adslot-ui as a dependency (https://www.npmjs.com/package/adslot-ui/tutorial), then create a react app that uses Carousel component (this component implements nuka-carousel, which uses create-react-class)

Issue 2:
As migrating from 15.4 to 15.5, I am rewrite original react components with create-react-class.

However, there is a warning.

react-15.5.4.js?bust=1497843639843:3287 Warning: getDefaultProps is only used on classic React.createClass definitions. Use a static property named `defaultProps` instead.

As I am really transferring from a classic React.createClass definition, I would like to ask is this warning appropriate? Or, this is a depreciation sames as the level of migrating to create-react-class?

Ooops, I get ticket #9999 :D
Issue 1:
Glide Version/Integration library (if any): 3.6.0

Device/Android Version: any

Issue details/Repro steps/Use case background:

How to repo:

I have a demo app with this bug here: https://github.com/yrizk/glide-centercrop-bug

but essentially...

no scale type declared in the xml

centerCrop() when loading image into an imageview.

make sure the app is cleared from memory

The image will start w/o the center crop, then if you scroll past or above it, then sometimes it comes back with the correct scale type.

on a separate note, the library is really good! thank you guys for building and supporting this

Issue 2:
i am using android list view and this is my code.....

when image load from server first time centerCrop not work then i am scroll list view then back same view then centerCrop work. but not work for all time. when scrolling fast and back to view then not work centerCrop().

RequestManager manager = Glide.with(MainActivity.cf.context);
DrawableTypeRequest<String> drawable = manager.load("http://192.168.0.108/vavidhya/"+id+".png");
drawable.centerCrop();
drawable.placeholder(R.drawable.event_list_item_back);
drawable.into(backImage);
Issue 1:
🐛 Bug

I have this assert using libtorch-win-shared-with-deps-debug-latest.zip loading a saved model:

cuda_dispatch_ptr INTERNAL ASSERT FAILED at
C:\w\1\s\windows\pytorch\aten\src\ATen/native/DispatchStub.h:74, please report a bug to
PyTorch. DispatchStub: missing CUDA kernel (operator () at
C:\w\1\s\windows\pytorch\aten\src\ATen/native/DispatchStub.h:74)
(no backtrace available)

It works with the release version libtorch-win-shared-with-deps-latest.zip

To Reproduce:

download the nightly build and compile with VS2019.

save a model to "cuda":

model = models.densenet121(pretrained=True)

# do forward...

traced_script_module.save("traced_resnet_model.pt")

load the model:

module = torch::jit::load("traced_resnet_model.pt");

PyTorch Version nightly build:

OS W10:

https://download.pytorch.org/libtorch/nightly/cu101/libtorch-win-shared-with-deps-debug-latest.zip

VS2019:

CUDA 10:

Issue 2:
🐛 Bug

When doing inference using LibTorch in C++ for semantic segmentation on the GPU, i get the following error in debug compilation mode:

cuda_dispatch_ptr INTERNAL ASSERT FAILED at C:\w\1\s\windows\pytorch\aten\src\ATen/native/DispatchStub.h:70, please report a bug to PyTorch. DispatchStub: missing CUDA kernel (operator () at C:\w\1\s\windows\pytorch\aten\src\ATen/native/DispatchStub.h:70)

(no backtrace available)

When I run it in release mode it does not crash and delivers the expected result. Also on the CPU, this assert is not shown.

Environment

PyTorch Version (e.g., 1.0): latest nightly

OS (e.g., Linux): WIndows 10, Visual Studio 19

How you installed PyTorch (conda, pip, source): downloaded prebuild libtorch

Build command you used (if compiling from source):

Python version:

CUDA/cuDNN version: the one included in the prebuild (cuda 10)

GPU models and configuration: Titan X

Any other relevant information:
Issue 1:
I have an SVG chart made using d3.js and I use bootstrap popover on an svg element (such as a point on a line).

This worked well in 2.1 (though not perfect as it would position on the top-left of the element).

However, after 2.2 the tooltip positions itself such that the left of the popover box is inline with where the trigger svg element is. The vertical position is off as well and depends on if you have scrolled your viewport. This probably has something to do with popovers now being inserted relative to your trigger element and not in body. Because of this last reason I have to move the popover after its rendered outside of the svg container (since an svg container can't display regular html).

Issue 2:
Anyone else?
Issue 1:
System information (version)

OpenCV => 4.1.0

Operating System / Platform => Ubuntu 18.10

Compiler => Emscripten

Detailed description

I am wanting to use the findHomography function in the webassembly build of opencv. However, no bindings are provided for the calib3d module. By the comment in the build script this is intentional.

However, I wanted to start a discussion on whether it is feasible to include this module, and if so what would be needed to be done to get this working.

Issue 2:
System information (version)

OpenCV => 3.4.9

Operating System / Platform => archlinux 64bit

Compiler => gcc 9.2.0

Detailed description

I have just installed opencv from source, with bindings to python3.8. I chose Atlas for BLAS implementation. When compiling the python bindings I saw warnings of the kind

warning: libopenblas.so.3 not found

Which sounded weird to me, because I had explicitly chosen Atlas implementation in cmake. The compilation went ahead, so I didn't think much of it. Besides, Archlinux doesn't allow you to have two implementations of BLAS installed on the system, so there was no much I could do about it.

The issue became apparent when I tried to import cv2, it threw this error:

Traceback (most recent call last):

File "", line 1, in

File "cv2/init.py", line 8, in

import numpy

File "/usr/lib/python2.7/site-packages/numpy/init.py", line 145, in

from . import lib

File "/usr/lib/python2.7/site-packages/numpy/lib/init.py", line 9, in

from .index_tricks import *

File "/usr/lib/python2.7/site-packages/numpy/lib/index_tricks.py", line 13, in

import numpy.matrixlib as matrixlib

File "/usr/lib/python2.7/site-packages/numpy/matrixlib/init.py", line 6, in

from .defmatrix import *

File "/usr/lib/python2.7/site-packages/numpy/matrixlib/defmatrix.py", line 13, in

from numpy.linalg import matrix_power

File "/usr/lib/python2.7/site-packages/numpy/linalg/init.py", line 51, in

from .linalg import *

File "/usr/lib/python2.7/site-packages/numpy/linalg/linalg.py", line 35, in

from numpy.linalg import lapack_lite, _umath_linalg

ImportError: libopenblas.so.3: cannot open shared object file: No such file or directory

So even though I had chosen atlas implementation, cv2.so is requiring openblas

Steps to reproduce

-On archlinux, install python-numpy

-Compile from source opencv, choose atlas implementation for BLAS

-Select python3.8 as default executable (for python bindings)

import cv2 fails with above error
Issue 1:
matplotlib.tests.test_contour.test_corner_mask.test ... ok
/matplotlib/colorbar.py:539: VisibleDeprecationWarning: boolean index did not match indexed array along dimension 0; dimension is 5 but corresponding boolean dimension is 4
colors = np.asarray(colors)[igood]
/matplotlib/colorbar.py:539: VisibleDeprecationWarning: boolean index did not match indexed array along dimension 0; dimension is 5 but corresponding boolean dimension is 3
colors = np.asarray(colors)[igood]

Cc: @efiring: Any thoughts as a colorbar expert? I think the warning is due to a recent tightening in Numpy, but I'm not sure how to fix it.

Issue 2:
On Travis, the test_given_colors_levels_and_extends test is generating two VisibleDeprecationWarnings:

.../matplotlib/colorbar.py:539: VisibleDeprecationWarning:
boolean index did not match indexed array along dimension 0; dimension is 5 but corresponding boolean dimension is 4
colors = np.asarray(colors)[igood]
.../matplotlib/colorbar.py:539: VisibleDeprecationWarning:
boolean index did not match indexed array along dimension 0; dimension is 5 but corresponding boolean dimension is 3
colors = np.asarray(colors)[igood]

The first occurs on the axes that is not filled and extend='min'; the second with unfilled and extend='both'. The passed colour list is 5 elements long and the levels list is 4 or 3. The warning can easily be fixed by trimming the colour list, but I'm not sure exactly where that should be.

I guess I'm not entirely sure why one less level is passed for extend='both' when only unfilled. cc @pelson who wrote the test
Issue 1:
Lucene now has sandbox support for BigInteger (LUCENE-7043), and hopefully BigDecimal will follow soon. We should look at what needs to be done to support them in Elasticsearch.

I propose adding big_integer and big_decimal types which have to be specified explicitly - they shouldn't be a type which can be detected by dynamic mapping.

Many languages don't support big int/decimal. Javascript will convert to floats or throw an exception if a number is out of range. This can be worked around by always rendering these numbers in JSON as strings. We can possibly accept known bigints/bigdecimals as numbers but there are a few places where this could be a problem:

indexing a known big field (do we know ahead of time to parse a floating point as a BigDecimal?)

dynamic mapping (a floating point number could have lost precision before the field is defined as big_decimal)

ingest pipeline (ingest doesn't know about field mappings)

The above could be worked around by telling Jackson to parse floats and ints as BIG* (USE_BIG_DECIMAL_FOR_FLOATS and USE_BIG_INTEGER_FOR_INTS) but this may well generate a lot of garbage for what is an infrequent use case.

Alternatively, we could just say that Big* should always be passed in as strings if they are to maintain their precision.

Issue 2:
Elasticsearch version: Amazon Elasticsearch 2.3.2

Description of the problem including expected versus actual behavior:

Inserting same field to same index with different mapping don't create new mapping.

Field name change creates new mapping

Issue seems to be duplicate of closed #16348 #15138 #15568

Steps to reproduce:

$ curl -XPOST -d '{"timestamp": "2016/08/18 12:55:43"}' https://aws-elasticsearch/logstash-2016.08.18/test_map1
{"_index":"logstash-2016.08.18","_type":"test_map1","_id":"AVadvLddEO70PGmPaHHU","_version":1,"_shards":{"total":2,"successful":1,"failed":0},"created":true}

$ curl -XPOST -d '{"timestamp": "2016-08-18T12:42:06.623000+0000"}' https://aws-elasticsearch/logstash-2016.08.18/test_map2 ; echo
{"error":{"root_cause":[{"type":"mapper_parsing_exception","reason":"failed to parse [timestamp]"}],"type":"mapper_parsing_exception","reason":"failed to parse [timestamp]","caused_by":{"type":"illegal_argument_exception","reason":"Invalid format: \"2016-08-18T12:42:06.623000+0000\" is malformed at \"-08-18T12:42:06.623000+0000\""}},"status":400}

$ curl -XPOST -d '{"ttimestamp": "2016-08-18T12:42:06.623000+0000"}' https://aws-elasticsearch/logstash-2016.08.18/test_map2 ; echo
{"_index":"logstash-2016.08.18","_type":"test_map2","_id":"AVadx4_NEO70PGmPaHHc","_version":1,"_shards":{"total":2,"successful":1,"failed":0},"created":true}
Issue 1:
❓ Questions & Help

I am trying out a finetuned BERT model for token classification (--> https://huggingface.co/bert-base-cased-finetuned-conll03-english), but when I observe the model output (i.e. the logits after applying the softmax) and compare it with the true label_ids, they are totally uncorrelated (see pictures).

https://i.stack.imgur.com/gVyMn.png

https://i.stack.imgur.com/qS62L.png

Details

I assume that the finetuned model (bert-base-cased-finetuned-conll03-english) is correctly pretrained, but I don't seem to understand why its predictions are off. I think one issue is that the pretrained model has another labelling scheme than I made myself during data prep (so that the tag2name dict is different), but I don't know how I can find out what label-index map the model uses for its predictions. Even then it is not the case that the model consistently makes the same mistakes, it is outputting things quite randomly.

Any idea what the issue could be?

``

Issue 2:
🚀 Feature request

I am using Bert as a decoder (by setting is_decoder=True). However, during sequential decoding, there is no way of reusing the hidden states, so for every word to be generated we need to rerun the model on the ENTIRE decoded sequence, which renders decoding inefficient. Can you add something similar to the keyword past= in GPT2 model to BERT's forward function (https://github.com/huggingface/transformers/blob/master/src/transformers/modeling_bert.py#L938)?

Generalization of my Issue

More generally, to the best of my knowledge, there's no model in this library that simultaneously supports 1) cross attention (by feeding encoder_hidden_states= or memory=), and 2) reusing decoder states during sequential decoding (by feeding past=). 1) rules out models like GPT-2 and XLNet which only supports language modeling (although in theory we can just use a decoder to do translation, I want to use a separate encoder and decoder); and 2) rules out models like BERT and T5 which supports 1) but not 2). For example, the point of T5 is to use it for text-to-text translation problems, but since we cannot reuse hidden states, sequential decoding (beam search) would be extremely inefficient.

Example

In the provided summarization example, both 1) and 2) are supported. However, the decoder is defined in its own code (examples/summarization/modeling_bertabs.py) and cannot be used directly in the library. Besides, supporting incremental state update is a basic function that every decoder shall support.

Relevant Issues

I checked the suggested similar issues and did not find the same issue. Please let me know if my issue duplicates others'.
Issue 1:
Fixing #6339, the !important flag was added for .hide class.

.hide { display: none !important; }

This breaks the utility of .show() jQuery function that adds style="display: block" attribute to the selected elements, because !important is stronger than style attribute. I created a JSFIDDLE that demonstrates this.

While the !important flag cannot be removed from CSS only it's better to remove it from Bootstrap CSS and if the programmer wants to add it just add a new row in a CSS file.

Issue 2:
I think this is lazy implementation and it breaks when using $(elem).show() or $(elem).hide() using jQuery since the hide class get more importance since the badly (IMO) use of !important.

I think BT should not declare this so generics classes with !important, it makes difficult to customize. I don't like the idea of having another class for doing display: none because BT uses !important.

In my opinion, having a rule like : .hide { btn&, ul li&, etc.. { display:none; }} isn't much work and won't break anything. also footprint shouldn't increase a lot since the .hide pattern will be greatly gzipped.

right now, having a <div id="someId" class="hide"></div> will break $('#someId').show() function of jQuery.
Issue 1:
When implementing modal, the backdrop opacity was set to 1. I checked the css and found that the following

.modal-backdrop.fade {
opacity: 0;
filter: alpha(opacity=0);
}
.modal-backdrop.in {
opacity: 0.5;
filter: alpha(opacity=50);
}

...was being overridden by

.fade.in {
opacity: 1;
}

This resulted in a 100% solid black background. In order to get it working properly I added the following to my local site.css overrides:

.modal-backdrop.fade.in {
opacity: .5;
}

Issue 2:
Hi!

I just generated a customized version and the modal background is totally black (I also generated a version without any change to be sure it wasn't me that caused that):

The download link on the home page does have transparency:
Issue 1:
After installing docker, I attempted to run tensorflow in Jupyter. My run command was

LewIss-MacBook-Pro:MyTensorFlow lewleib$ docker run -it -p 8888:8888 -p 6006:6006 -v ~/Users/lewleib/MyTensorFlow:/notebooks tensorflow/tensorflow`

In the Jupyter notebook I ran

import tensorflow as tf
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello)

This resulted in

ModuleNotFoundError: No module named 'tensorflow'

I have tried starting in different directories and added prefix gcr.io with the same results. Thanks

-- | --

Issue 2:
Code :

import tensorflow as tf
def Conv3D(x, filter_shape, stride = 1, padding = 'VALID', dilation_rate = (1,1,1)):
W = tf.get_variable(name = 'weights', shape = filter_shape, initializer = tf.contrib.layers.xavier_initializer())
b = tf.get_variable(name = 'bias', shape = filter_shape[4], initializer = tf.constant_initializer(0.1))
conv_out = tf.nn.convolution(x, W, b, padding = padding, strides = [1,1,1], dilation_rate = dilation_rate)
ret_val = tf.nn.bias_add(conv_out,b)
return ret_val

def MaxPool3D(x, dilation_rate = (1,1,1)):
ret_val = tf.nn.pool(x, window_shape = [2,2,2], pooling_type = 'MAX', padding = 'VALID', dilation_rate = dilation_rate, strides = [1,1,1])
return ret_val

layer = {}
dilation = 1
inputs = tf.placeholder(tf.float32, (None, 80, 80, 80, 1))
layer[1] = Conv3D(inputs, [3,3,3,1,128], dilation_rate = (dilation, dilation, dilation))
layer[2] = Conv3D(layer[1], [3,3,3,128,64], dilation_rate = (dilation, dilation, dilation))
layer[3] = MaxPool3D(layer[2], dilation_rate = (dilation, dilation, dilation))
dilation = 2*dilation
layer[4] = Conv3D(layer[3], [3,3,3,64,128], dilation_rate = (dilation, dilation, dilation))
layer[5] = MaxPool3D(layer[4], dilation_rate = (dilation, dilation, dilation))
dilation = 2*dilation
layer[6] = Conv3D(layer[5], [3,3,3,128, 256], dilation_rate = (dilation, dilation, dilation))
layer[7] = MaxPool3D(layer[6], dilation_rate = (dilation, dilation, dilation))
dilation = 2*dilation
layer[8] = Conv3D(layer[7], [3,3,3,256,64], dilation_rate = (dilation, dilation, dilation))

inputs_np = np.random.randn(1,80,80,80,1)
sess = tf.Session()
logits1 = sess.run(layer[8], feed_dict = {inputs : inputs_np})
logits1_duplicate = sess.run(layer[8], feed_dict = {inputs : inputs_np})

sum_diff = np.sum(logits1 - logits1_duplicate)
print(' sum diff : ', sum_diff)

When I run this code, I get a non zero sum_diff. Why is the network's output not consistent with multiple runs ? I will appreciate some help