WhereIsAI/github-issue-similarity huggingface dataset:
Issue 1:
import scrapy

class MyItem(scrapy.Item):
item1 = scrapy.Field()
item_url = scrapy.Field()
item_address = scrapy.Field()
item_list = scrapy.Field()

class MyLoader(scrapy.contrib.loader.ItemLoader):
default_output_processor = TakeFirst()
default_input_processor = MapCompose(unicode.strip)
item_list_in = Identity()
item_list_out = Identity()

class MySpider(scrapy.Spider):

name = 'myspider'
start_urls = ['some-url']

def parse(self, response):
uls = response.xpath('//div[@id="someID"]//ul')
for ul in uls:
url = ul.xpath('.//li[1]/a/@href').extract()[0]
loader = MyLoader(MyItem(), ul)
loader.add_xpath('item1', './/li[1]/a/text()')
loader.add_value('item_url', url)
request = scrapy.Request(url, callback=self.parse_more)
request.meta['item'] = loader.load_item()
yield request

def parse_more(self, response):
loader = MyLoader(response.meta['item'], response=response)
item_list = response.css('.nav-buttons.someclass').xpath('.//a/img/@alt').extract()
loader.add_xpath('item_address', '//address//div[@class="address-info"]/text()')
loader.add_value('item_list', item_list)
#import pdb; pdb.set_trace()
if loader.get_output_value('item_address') == "some address":
loader.add_value('address_code', 'FR')
yield loader.load_item()

In pdb, for example, if we inspect a loader instance

(pdb) loader.load_item()
{'item_address': 'someval', ....}
(pdb) type(loader.get_output_value('item_address'))
<type: Nonetype>

As a result, the if-block is never executing.

Issue 2:
Hi, following code snippet does not work as i expected:

In [2]: from scrapy.contrib.loader import ItemLoader

In [3]:

In [3]: from scrapy.item import Item, Field

In [4]:

In [4]: class Product(Item):
...: name = Field()
...: comment = Field()
...:

In [5]: p = Product({'name': 'xx1', 'comment': 'hi!'})

In [6]: p
Out[6]: {'comment': 'hi!', 'name': 'xx1'}

In [7]: l = ItemLoader(item=p)

In [8]: l.get_output_value('name')
Out[8]: []

In [9]: l.context
Out[9]: {'item': {'comment': 'hi!', 'name': 'xx1'}, 'response': None, 'selector': None}

I would expect to see a result of 'xx1' from get_output_value() function intuitively. Am i right about my expectation here?

Regarding the code at item loader (https://github.com/scrapy/scrapy/blob/master/scrapy/contrib/loader/__init__.py#L88), is it correct to check the 'context' or 'item' of the loader if relevant field_name is not available in the loader?
Issue 1:
I'm having a problem.

I can't write the @-symbol anywhere in the atom interface. I'm on Windows7. To write a @ I need to press AltGr+Q (I use a QWERTZ-keyboard, AltGr is the right Alt key). I can write the euro-symbol (€) which is AltGr+E.

Also when I want to write the -symbol it collapses comments. (I don't know if this is related.) To write a ** I have to press AltGr+?.

Issue 2:
Atom doesn't handle keyboards with Right Alt (i.e. AltGr). This is a very basic mistake. I have a Hungarian keyboard layout, I can only write specific keys like "|" (pipe) by pressing Right Alt (AltGr) and then W, which Atom doesn't recognize and treat like Alt-W or something?

The point is there are some layouts like greek, hungarian, czech with special keys, which an EDITOR should handle the right way!!

E.g. I can't write CTRL-", because it writes the number "2" instead. I think this key might be interpreted like CTRL-ALT sequence, but not sure either.

Anyway, without it, it's just not an editor.

Like Windows Media player; you can't pause a movie with it hitting space bar...

From: support/9f98abca9f4a11e3955c6445541b1a76
Issue 1:
ISSUE TYPE

Feature Idea

COMPONENT NAME

module_utils/powershell.ps1

ANSIBLE VERSION

v2.4

SUMMARY

The win_nssm module implements a parameter using a "dict" type as value. This is currently unimplemented (and taken care of by the win_nssm module itself). We should move this support to powershell.ps1, and support both the YAML syntax, as well as the legacy "key=value" string syntax.

(For the win_nssm module, there's also a dependencies parameter that currently expects a comma-separated string, which should become of type "list" too.)

And the win_nssm module does not have integration tests, nor does it have check-mode support.

This relates to #25358

Issue 2:
ISSUE TYPE

Bug Report

ANSIBLE VERSION

ansible 1.9.6
configured module search path = None

CONFIGURATION

OS / ENVIRONMENT

SUMMARY

Passing variables to roles from inventory-defined variables always results in the |defaul('') case; even when the variable exists.

STEPS TO REPRODUCE

http://filebin.ca/2jzNVNZXgpJ1/inventory_all_vars.tar.gz

EXPECTED RESULTS

For the variables defined in the inventory file to be echoed.

ACTUAL RESULTS

All variables are equal to ""
Issue 1:
The weekly build with nightly wheels from numpy and pandas

has failed. Check the logs for any updates that need to be

made in matplotlib.

https://github.com/matplotlib/matplotlib/actions/runs/3301901359

Issue 2:
The weekly build with nightly wheels from numpy and pandas

has failed. Check the logs for any updates that need to be

made in matplotlib.

https://github.com/matplotlib/matplotlib/actions/runs/3163174215
Issue 1:
/home/wj/anaconda2/bin/python2.7 /home/wj/PycharmProjects/untitled2/1.py

Files already downloaded and verified

Files already downloaded and verified

truck ship cat frog

[1, 2000] loss: 2.224

[1, 4000] loss: 1.857

[1, 6000] loss: 1.713

[1, 8000] loss: 1.607

[1, 10000] loss: 1.555

[1, 12000] loss: 1.495

[2, 2000] loss: 1.423

[2, 4000] loss: 1.374

[2, 6000] loss: 1.373

[2, 8000] loss: 1.326

[2, 10000] loss: 1.309

[2, 12000] loss: 1.280

Finished Training

Exception NameError: "global name 'FileNotFoundError' is not defined" in <bound method _DataLoaderIter.del of <torch.utils.data.dataloader._DataLoaderIter object at 0x7fa8c97b3050>> ignored

('GroundTruth: ', ' cat ship ship plane')

('Predicted: ', ' cat car ship ship')

Accuracy of the network on the 10000 test images: 54 %

Accuracy of plane : 64 %

Accuracy of car : 69 %

Accuracy of bird : 40 %

Accuracy of cat : 35 %

Accuracy of deer : 53 %

Accuracy of dog : 42 %

Accuracy of frog : 76 %

Accuracy of horse : 44 %

Accuracy of ship : 64 %

Accuracy of truck : 51 %

cpu

Exception NameError: "global name 'FileNotFoundError' is not defined" in <bound method _DataLoaderIter.del of <torch.utils.data.dataloader._DataLoaderIter object at 0x7fa8aaaec810>> ignored

Process finished with exit code 0

Issue 2:
Traceback (most recent call last): File "trainval_net.py", line 322, in <module> rois_label = fasterRCNN(im_data, im_info, gt_boxes, num_boxes) File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py", line 491, in __call__ result = self.forward(*input, **kwargs) File "/home/faster-rcnn.pytorch/lib/model/faster_rcnn/faster_rcnn.py", line 50, in forward rois, rpn_loss_cls, rpn_loss_bbox = self.RCNN_rpn(base_feat, im_info, gt_boxes, num_boxes) File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py", line 491, in __call__ result = self.forward(*input, **kwargs) File "/home/faster-rcnn.pytorch/lib/model/rpn/rpn.py", line 87, in forward rpn_data = self.RPN_anchor_target((rpn_cls_score.data, gt_boxes, im_info, num_boxes)) File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py", line 491, in __call__ result = self.forward(*input, **kwargs) File "/home/faster-rcnn.pytorch/lib/model/rpn/anchor_target_layer.py", line 157, in forward positive_weights = 1.0 / num_examples File "/usr/local/lib/python2.7/dist-packages/torch/tensor.py", line 320, in __rdiv__ return self.reciprocal() * other RuntimeError: reciprocal is not implemented for type torch.cuda.LongTensor Exception NameError: "global name 'FileNotFoundError' is not defined" in <bound method _DataLoaderIter.__del__ of <torch.utils.data.dataloader._DataLoaderIter object at 0x7fb842e6e3d0>> ignored

This error is coming while implementing pytorch faster-rcnn repository. Any solutions please??
Issue 1:
Description of the new feature/enhancement

Include panes keybindings by default

Proposed technical implementation details (optional)

Current keybinding suggestion:

{ "command": "splitHorizontal", "keys": [ "alt+shift+-" ] },
{ "command": "splitVertical", "keys": [ "alt+shift+plus" ] },
{ "command": { "action": "moveFocus", "direction":"down" }, "keys": [ "alt+down" ] },
{ "command": { "action": "moveFocus", "direction":"left" }, "keys": [ "alt+left" ] },
{ "command": { "action": "moveFocus", "direction":"right" }, "keys": [ "alt+right" ] },
{ "command": { "action": "moveFocus", "direction":"up" }, "keys": [ "alt+up" ] },
{ "command": { "action": "resizePane", "direction":"down" }, "keys": [ "alt+shift+down" ] },
{ "command": { "action": "resizePane", "direction":"left" }, "keys": [ "alt+shift+left" ] },
{ "command": { "action": "resizePane", "direction":"right" }, "keys": [ "alt+shift+right" ] },
{ "command": { "action": "resizePane", "direction":"up" }, "keys": [ "alt+shift+up" ] },

Issue 2:
Environment

Windows build number: 10.0.18995.0
Windows Terminal version (if applicable): 0.5.2762.0

Steps to reproduce

Open Terminal app.

Expected behavior

Should not show a white border on the bottom of the window.

Actual behavior

Shows a 1 pixel thin white border on the bottom of the window.
Issue 1:
Hi,

I am interested in using scatter_add when the tensor being update is not a variable. Is this possible?

I am looking to do something like this:

X1_ph = tf.placeholder(tf.float32, shape=(None, 3))

ind_ph = tf.placeholder(tf.int32, shape=(None))

#Z = tf.Variable(tf.zeros([10, 3]))

Z = tf.zeros([10, N_feat])

X1 = np.array([[1,0.00,1],

[2,0.00,1],

[3,0.00,1],

[5,0.00,1.1],

[6,1.0,1.8]])

ind = [0, 1, 1, 0, 0]

Z = tf.scatter_add(Z, ind_ph, X1)

If I declare Z as a tf.Variable, I can do this, but I need to call this operation hundreds of thousands of times, and do not want to store any copies of Z once I am done with them. If I were to declare Z as a Variable, would there be any way to destroy Z once I am done with it (maybe with a garbage collector or something similar)? Thank you so much for your help!

Issue 2:
System information

Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes

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

Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device: No idea

TensorFlow installed from (source or binary): binary using pip3 install

TensorFlow version (use command below): 1.14

Python version: 3.6

Bazel version (if compiling from source):

GCC/Compiler version (if compiling from source):

CUDA/cuDNN version: cuda9.0, cudnn 7.6

GPU model and memory: RTX 2080Ti

When I run the following code using CPU:

result= tf.math.log(tf.constant([280.41303540865516]*100, dtype=tf.float32)).numpy()

I got:

[5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.6362634 5.636264 5.636264 5.636264 5.636264 ]

I found result[0] is not equal to result[-1]. the first one is

5.636263370513916 while the latter one is equal to 5.636263847351074.

I believe it is a bug.

Code to reproduce the issue

from __future__ import absolute_import, division, print_function, unicode_literals import tensorflow as tf from random import * tf.enable_eager_execution() with tf.device("/cpu:0"): result = tf.math.log(tf.constant([ 280.41303540865516]*100, dtype=tf.float32)).numpy() print("{} vs {}".format(result[0], result[-1]))

Seems GPU has no such issues.
Issue 1:
See screenshot for repro code, its much clearer.

Generated using grunt-typescript 0.6.1/typescript 1.4.1 with the following config:

typescript: {
base: {
src: ["src/**/*.ts"],
options: {
module: "commonjs",
target: "es5",
basePath: "src",
sourceMap: false,
declaration: false,
references:[
"typings/**/*.d.ts"
]
}
}
}

Assuming its unlikely that grunt-typescript will produce this kind of bug.

Issue 2:
Hi,

TS Version: 1.4

The following code:

var x: any;
/*
var bar;
*/

Emits the following JavaScript

var x;
/*
var bar;
var x: any;
/*
var bar;

Expected

var x;
/*
var bar;
*/
Issue 1:
I have searched the issues of this repository and believe that this is not a duplicate.

Sorry in advance if I didn't find any informations. If this behavior is actually possible, please specify it in the README.

Expected Behavior

Being able to use an IP (10.x.x.x) instead of localhost while using next dev.

Current Behavior

Dev server launch on localhost.

Issue 2:
I have searched the issues of this repository and believe that this is not a duplicate.

Expected Behavior

I try to do

Run production build use custom server with express

Jest Test

Current Behavior

I can run next in development mode with node server.js

Jest Test ( Jest is ok, run normally)

After next build, I try to start custom server with express with

NODE_ENV=production node server.js

This is log

TypeError: Cannot read property 'toLowerCase' of undefined
at a.renderDOM (/home/naijab/freelance/neogamer/frontend/gamerui/src/node_modules/react-dom/cjs/react-dom-server.node.production.min.js:36:391)
at a.render (/home/naijab/freelance/neogamer/frontend/gamerui/src/node_modules/react-dom/cjs/react-dom-server.node.production.min.js:36:228)
at a.read (/home/naijab/freelance/neogamer/frontend/gamerui/src/node_modules/react-dom/cjs/react-dom-server.node.production.min.js:35:250)
at renderToStaticMarkup (/home/naijab/freelance/neogamer/frontend/gamerui/src/node_modules/react-dom/cjs/react-dom-server.node.production.min.js:44:75)
at _callee3$ (/home/naijab/freelance/neogamer/frontend/gamerui/src/node_modules/next/dist/server/render.js:223:100)
at tryCatch (/home/naijab/freelance/neogamer/frontend/gamerui/src/node_modules/regenerator-runtime/runtime.js:62:40)
at Generator.invoke [as _invoke] (/home/naijab/freelance/neogamer/frontend/gamerui/src/node_modules/regenerator-runtime/runtime.js:296:22)
at Generator.prototype.(anonymous function) [as next] (/home/naijab/freelance/neogamer/frontend/gamerui/src/node_modules/regenerator-runtime/runtime.js:114:21)
at step (/home/naijab/freelance/neogamer/frontend/gamerui/src/node_modules/babel-runtime/helpers/asyncToGenerator.js:17:30)
at /home/naijab/freelance/neogamer/frontend/gamerui/src/node_modules/babel-runtime/helpers/asyncToGenerator.js:28:13
at <anonymous>

Steps to Reproduce (for bugs)

yarn build --> next build

yarn start --> NODE_ENV=production node server.js

Point a browser to http://localhost:3000

Show 500 errors flash An unexpected error has occurred.

Context

When start production build, internal server errors cause an unwanted flash of content.

Your Environment

Tech
Version

next
5.0

node
8.9

OS
Ubuntu

browser
Chrome 64

etc
Issue 1:
Original ticket http://projects.scipy.org/numpy/ticket/2155 on 2012-06-11 by trac user r0k3, assigned to @pierregm.

One gets an error while trying to pickle an object that contains a MaskedConstant.

Issue 2:
I've tried to build numpy with ATLAS 3.10. This version of ATLAS produces instead of libf77blas.so, liblapack.so, and other libraries just lib{s,t}atlas.so. Unfortunately numpy is not able to accept this change (which results into numpy not being linked against ATLAS' libs).

I would provide patch for this but code which performs these tests is a little bit hard to read for me.

Simple workaround for this is to create original files by creating symlinks:

ln -s libtatlas.so* libptcblas.so
ln -s libtatlas.so* libptf77blas.so
# and so on
Issue 1:
Series should be standardized with DataFrame and provide the same structural infos methods, for example .info()

Issue 2:
xref PR #17077

Now that a cache keyword has been added to to_datetime, ideally the default should be set to cache='infer' which would inspect the input data to determine whether caching would be a more efficient conversion.

From some research (here and here), date strings, especially ones with timezones offsets, can benefit from conversion with a cache of dates. The rules of thumb of whether to convert with a cache should be based on a combination of input data type, proportion of duplicate values, and number of dates to convert.

Additionally, I'd be nice to resolve existing to_datetime performance issues (e.g. #17410) just so the rules of thumb informing the inference step are not misguided by these issues.
Issue 1:
🚀 Feature

Currently, the DataParallel-based approach for distributed training does not support complex tensors. Some collective Operations (operations involving communication between multiple nodes in a distributed training setup) that are a part of the torch.distributed package are currently in the process of supporting complex-valued tensors. Since underlying collective libraries such as NCCL do not natively support complex types, we instead use constant-time views, such as torch.view_as_real and torch.view_as_complex.

Since the DataParallel package uses a collective called broadcast_coalesced in order to duplicate the model across nodes, complex tensor support must be added to broadcast_coalesced in order to enable distributed training with complex tensors using DataParallel.

References

#45760: Umbrella Issue for Complex Tensor Support

#46745: User Request for Complex Tensor Support in DataParallel

#45879: Example PR that added Complex Tensor Support to some collectives

cc @pietern @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @xush6528 @osalpekar @jiayisuse @agolynski

Issue 2:
❓ Questions and Help

Does JIT trace support the model with MKLDNN layout?

import torch
import torchvision
from torch.utils import mkldnn as mkldnn_utils

# An instance of your model.
model = torchvision.models.resnet18(pretrained=True)
# Switch the model to eval model
model.eval()

model = mkldnn_utils.to_mkldnn(model)

# An example input you would normally provide to your model's forward() method.
example = torch.rand(1, 3, 224, 224).to_mkldnn()

# Use torch.jit.trace to generate a torch.jit.ScriptModule via tracing.
traced_script_module = torch.jit.trace(model, example)

# Save the TorchScript model
traced_script_module.save("traced_resnet_model.pt")

Traceback (most recent call last):
File "pytorch.py", line 17, in <module>
traced_script_module = torch.jit.trace(model, example)
File "/home/ubuntu/.local/lib/python3.6/site-packages/torch/jit/__init__.py", line 955, in trace
check_tolerance, strict, _force_outplace, _module_class)
File "/home/ubuntu/.local/lib/python3.6/site-packages/torch/jit/__init__.py", line 1119, in trace_module
check_tolerance, strict, _force_outplace, True, _module_class)
File "/home/ubuntu/.local/lib/python3.6/site-packages/torch/autograd/grad_mode.py", line 15, in decorate_context
return func(*args, **kwargs)
File "/home/ubuntu/.local/lib/python3.6/site-packages/torch/jit/__init__.py", line 597, in _check_trace
copied_dict[name] = _clone_inputs(data)
File "/home/ubuntu/.local/lib/python3.6/site-packages/torch/jit/__init__.py", line 450, in _clone_inputs
clone_input, condition_msg="tensors")(args)
File "/home/ubuntu/.local/lib/python3.6/site-packages/torch/autograd/function.py", line 263, in _map
return fn(obj)
File "/home/ubuntu/.local/lib/python3.6/site-packages/torch/jit/__init__.py", line 443, in clone_input
v = a.detach().clone(memory_format=torch.preserve_format).requires_grad_(a.requires_grad)
RuntimeError: unsupported memory format option Preserve

cc @suo @gmagogsfm
Issue 1:
Elasticsearch version 5.0 Alpha-2

JVM version 1.8.0_91

OS version Windows 7

Description of the problem including expected versus actual behavior:

Lucene throws the following error when running the Date Range query below. Files to reproduce are attached.

IllegalArgumentException[field="inceptionDate" is unrecognized];

Caused by: java.lang.IllegalArgumentException: field="inceptionDate" is unrecognized

at org.apache.lucene.codecs.lucene60.Lucene60PointsReader.getBKDReader(Lucene60PointsReader.java:120).

Schema included in mapping.txt file

ES dump included in dump.txt

stack trace in log.txt

{

"query" : {

"range" : {

log.txt

mapping.txt

dump.txt

"inceptionDate" : {

"from" : "10/1/1980",

"to" : "12/31/2015",

"include_lower" : true,

"include_upper" : true,

"boost" : 1.0

}

}

},

"fields" : [ "orgType", "inceptionDate", "name", "id", "qualityRating", "version", "parentId", "descp" ]

}

Provide logs (if relevant):

Describe the feature:

Issue 2:
The static helpers on PointValues throws an exception if a leaf has points indexed but not for the given field.

When using this function in es the exception is never caught which could lead to error in search request or field stats request.

The problem has been fixed in Lucene: https://issues.apache.org/jira/browse/LUCENE-7257 but we agreed with @jpountz that we should have a temporary fix in es that we could remove afterward.

The proposal is to have a XPointValues which acts exactly like the PointValues with the LUCENE-7257 patch and to use this class to call all the static helpers in es.
Issue 1:
While i'm using proxies just get always this error.

I've try several times at least i resolved by removing the "s" in the https: proxy too as suggested in this topic:

I think this problem is related to the last version, because i've tried on an older one and it works great.

Solution Steps

import requests
...
self.proxies = {
'http': f'http://...:8080/',
'https': f'http://...:8080/'
}
def get(self, url):
request_url = self.base_url + url
return requests.get(request_url, headers=self.headers, proxies=self.proxies)

System Information

$ python -m requests.help

{
"chardet": {
"version": "4.0.0"
},
"cryptography": {
"version": ""
},
"idna": {
"version": "2.10"
},
"implementation": {
"name": "CPython",
"version": "3.9.5"
},
"platform": {
"release": "10",
"system": "Windows"
},
"pyOpenSSL": {
"openssl_version": "",
"version": null
},
"requests": {
"version": "2.25.1"
},
"system_ssl": {
"version": "101010bf"
},
"urllib3": {
"version": "1.26.5"
},
"using_pyopenssl": false
}

Issue 2:
With the upgrade to 2.5.3, my application suddenly broke. I've tracked it down to a loss of headers. The response includes two "WWW-Authenticate" headers, but only one is returned in the header list. This appears to be a urllib3 bug.