WhereIsAI/github-issue-similarity huggingface dataset:
Issue 1:
Filter Assetic 'closure' is broken in Symfony version 2.3.12.

In Symfony version 2.3.11 works correctly.

When you run the command 'php.exe app/console assetic:dump --env=prod --no-debug --verbose -vvv', the following error message appear:

[Assetic\Exception\FilterException]

An error occurred while running:

"D:/symfony/app/Resources/jre/bin/java.exe" "-jar" "D:/symfony/app/Resources/java/compiler.jar" "--compilation_level" "WHITESPACE_ONLY" "--js" "C:\Documents and Settings\felix\Configuración local\Temp\assAC0.tmp"

The command line actually running:

cmd /V:ON /E:ON /C "("D:/symfony/app/Resources/jre/bin/java.exe" "-jar" "D:/symfony/app/Resources/java/compiler.jar" "--compilation_level" "WHITESPACE_ONLY" "--js" "C:\Documents and Settings\felix\Configuración local\Temp\assAC0.tmp")" 1>C:\Documents and Settings\felix\Configuración local\Temp\sf_AC1.tmp 2>C:\Documents and Settings\felix\Configuración local\Temp\sf_AC2.tmp

In the file 'D:\symfony\vendor\symfony\symfony\src\Symfony\Component\Process\Process.php' missing double quotes:

public function start

...

foreach ($this->processPipes->getFiles() as $offset => $filename) {

//Without the double quotes fails.

$commandline .= ' '.$offset.'>"'.$filename.'"';

//$commandline .= ' '.$offset.'>'.$filename.'';

}

if (!isset($this->options['bypass_shell'])) {

$this->options['bypass_shell'] = false;

//$this->options['bypass_shell'] = true;

}

Also does not work if 'bypass_shell' is true.

Excuse my English.

Issue 2:
After updating Symfony 2.3.11 to 2.3.12 , no more process using the Process component object Symfony works!

an error code 1 is always returned !

It works if I comment line 238 of src/Symfony/Component/Process/process.php file.

Command executed from the command line :

php app/console assetic:dump --env=prod

result:

Dumping all prod assets.
Debug mode is off.

10:29:18 [file+] D:/wwwroot/project/app/../web/bundles/project/min/g
lobal.css

[Assetic\Exception\FilterException]
An error occurred while running:
"C:\Program Files\Java\jre7\bin\java.exe" "-jar" "D:/wwwroot/project/app/Re
sources/java/yuicompressor-2.4.7.jar" "--charset" "UTF-8" "-o" "C:\Document
s and Settings\adm\Local Settings\Temp\1\YUI36F.tmp" "--type" "css" "C:
\Documents and Settings\adm\Local Settings\Temp\1\YUI36E.tmp"

Input:
[...]

assetic:dump [--watch] [--force] [--period="..."] [write_to]
Issue 1:
Play rust link: http://is.gd/DVjBtk

macro_rules! a {(<$($($d:expr),*$($c:ident),*)*>) => (B<$($d,)*$($c,)*>);}a! (<>);

Issue 2:
Trying to compile this code containing an ambiguous macro causes the compiler to hang forever and max out memory:

macro_rules! make_vec(
(a $e1:expr $($(, a $e2:expr)*)*) => ([$e1 $($(, $e2)*)*]);
)

fn main() {
let _ = make_vec!(a 1, a 2, a 3);
}

rustc 0.5

host: x86_64-apple-darwin
Issue 1:
🐛 Bug

While convert an RNN to onnx, it report this error.

To Reproduce

Steps to reproduce the behavior:

1.While run this code on GPU this error will be reported.

2.But if i run this code on CPU there is no error reported

`

import torch

import torch.nn as nn

class StackedLSTM(nn.Module):
def __init__(self, num_layers, input_size, rnn_size, dropout):
super(StackedLSTM, self).__init__()
self.dropout = nn.Dropout(dropout)
self.num_layers = num_layers
self.layers = nn.ModuleList()

for _ in range(num_layers):
self.layers.append(nn.LSTMCell(input_size, rnn_size))
input_size = rnn_size

def forward(self, input_feed, hidden):
h_0, c_0 = hidden
h_1, c_1 = [], []
for i, layer in enumerate(self.layers):
h_1_i, c_1_i = layer(input_feed, (h_0[i], c_0[i]))
input_feed = h_1_i
if i + 1 != self.num_layers:
input_feed = self.dropout(input_feed)
h_1 += [h_1_i]
c_1 += [c_1_i]
h_1 = torch.stack(h_1)
c_1 = torch.stack(c_1)
return input_feed, (h_1, c_1)

lstm = StackedLSTM(2, 580, 500, 0.3).cuda()
h_t = torch.randn(2, 10, 500).cuda()
h_c = torch.randn(2, 10, 500).cuda()
a = torch.randn(10, 580).cuda()
torch.onnx.export(lstm, (a, (h_t, h_c)), f='LSTM.onnx', opset_version=12, input_names=['input', 'h_t', 'h_c'], output_names=
['output', 'hidden_t', 'hidden_c'])

`

Expected behavior

Convert this RNN to onnx while runing on GPU.

Environment

PyTorch version: 1.7.0

Is debug build: True

CUDA used to build PyTorch: 10.2

ROCM used to build PyTorch: N/A

OS: Microsoft Windows 10 专业版

GCC version: (GCC) 9.2.0

Clang version: Could not collect

CMake version: version 3.16.2

Python version: 3.6 (64-bit runtime)

Is CUDA available: True

CUDA runtime version: 10.2.89

GPU models and configuration: GPU 0: GeForce GTX 860M

Nvidia driver version: 441.22

cuDNN version: C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\bin\cudnn64_7.dll

HIP runtime version: N/A

MIOpen runtime version: N/A

Versions of relevant libraries:

[pip3] numpy==1.19.2

[pip3] numpydoc==0.8.0

[pip3] torch==1.7.0

[pip3] torchaudio==0.7.0

[pip3] torchtext==0.6.0

[pip3] torchvision==0.8.1

[conda] blas 1.0 mkl

[conda] cpuonly 1.0 0 pytorch

[conda] mkl 2018.0.2 1

[conda] mkl-service 1.1.2 py36h57e144c_4

[conda] mkl_fft 1.0.1 py36h452e1ab_0

[conda] mkl_random 1.0.1 py36h9258bd6_0

[conda] numpy 1.19.2 pypi_0 pypi

[conda] numpydoc 0.8.0 py36_0

[conda] torch 1.7.0 pypi_0 pypi

[conda] torchaudio 0.7.0 pypi_0 pypi

[conda] torchtext 0.6.0 pypi_0 pypi

[conda] torchvision 0.8.1 pypi_0 pypi

cc @houseroad @spandantiwari @lara-hdr @BowenBao @neginraoof

Issue 2:
Hi guys:

I have build a seq2seq model using nn.LSTMCELL in it. And I want to export a trained one to onnx form. However, when I do this, I have an error says:

UserWarning: ONNX export failed on ATen operator _thnn_fused_lstm_cell because torch.onnx.symbolic_opset9._thnn_fused_lstm_cell does not exist
.format(op_name, opset_version, op_name))

I think this maybe due to the opset nort supporting nn.LSTMCELL.

Any one could help and share some advice?

Thank you！

cc @albanD @mruberry @houseroad @spandantiwari @lara-hdr @BowenBao @neginraoof @zou3519
Issue 1:
Describe the bug

A clear and concise description of what the bug is. If your problem is not a bug, please file under Support or Usage Question

To Reproduce

Code snippet to reproduce, ideally that will work by pasting into something like https://npm.runkit.com/axios, a hosted solution, or a repository that illustrates the issue. If your problem is not reproducible, please file under Support or Usage Question

// Example code here

Expected behavior

A clear and concise description of what you expected to happen.

Environment:

Axios Version [e.g. 0.18.0]

OS: [e.g. iOS 12.1.0, OSX 10.13.4]

Browser [e.g. Chrome, Safari]

Browser Version [e.g. 22]

Additional Library Versions [e.g. React 16.7, React Native 0.58.0]

Additional context/Screenshots

Add any other context about the problem here. If applicable, add screenshots to help explain.

Issue 2:
Summary

#1395 launched a fix that instance config may override global config, but it ignore custom configs.

So it will break something we'd like to attach into the config and access in interceptor (e.g. CookieJar).

For example (https://codepen.io/anon/pen/gjZRdR?editors=1111):

const inst = axios.create();
inst.interceptors.request.use(config => {
console.log('should be "test", actual', config.testAttr);
})
inst.get('/', {
testAttr: 'test',
});

Context

axios version: v0.19.0-beta.1

Environment: node & chrome
Issue 1:
Description

i use minibatchkmeans and set k=2000, but, the number of clusters that minibatchkmeans returns is 1997, that is less than 2000. Then, i set k=1950, the minibatchkmeans return 1947, and it is less than 1950. I have over 16 million samples of datasets and each sample have 150 features.

Steps/Code to Reproduce

Expected Results

when set k =2000, return 2000

Actual Results

1947

Versions

sklean 0.20.1

system: centos6.3

scipy 1.1.0

numpy 1.14.5

Issue 2:
clf = DecisionTreeClassifier(min_samples_split=20,
min_samples_leaf=5,
max_features=None)

shape X (3220, 3375)
shape y (3220,)
calling clf.fit(X,y)
Exception ValueError: ValueError('Attempting to find a split with an empty sample_mask',) in 'sklearn.tree._tree.Tree.recursive_partition' ignored
Exception ValueError: ValueError('Attempting to find a split with an empty sample_mask',) in 'sklearn.tree._tree.Tree.recursive_partition' ignored
Exception ValueError: ValueError('Attempting to find a split with an empty sample_mask',) in 'sklearn.tree._tree.Tree.recursive_partition' ignored
Exception ValueError: ValueError('Attempting to find a split with an empty sample_mask',) in 'sklearn.tree._tree.Tree.recursive_partition' ignored
Exception ValueError: ValueError('Attempting to find a split with an empty sample_mask',) in 'sklearn.tree._tree.Tree.recursive_partition' ignored
Exception ValueError: ValueError('Attempting to find a split with an empty sample_mask',) in 'sklearn.tree._tree.Tree.recursive_partition' ignored
Exception ValueError: ValueError('Attempting to find a split with an empty sample_mask',) in 'sklearn.tree._tree.Tree.recursive_partition' ignored
Exception ValueError: ValueError('Attempting to find a split with an empty sample_mask',) in 'sklearn.tree._tree.Tree.recursive_partition' ignored
Exception ValueError: ValueError('Attempting to find a split with an empty sample_mask',) in 'sklearn.tree._tree.Tree.recursive_partition' ignored
Exception ValueError: ValueError('Attempting to find a split with an empty sample_mask',) in 'sklearn.tree._tree.Tree.recursive_partition' ignored
Exception ValueError: ValueError('Attempting to find a split with an empty sample_mask',) in 'sklearn.tree._tree.Tree.recursive_partition' ignored
Exception ValueError: ValueError('Attempting to find a split with an empty sample_mask',) in 'sklearn.tree._tree.Tree.recursive_partition' ignored
Exception ValueError: ValueError('Attempting to find a split with an empty sample_mask',) in 'sklearn.tree._tree.Tree.recursive_partition' ignored
Exception ValueError: ValueError('Attempting to find a split with an empty sample_mask',) in 'sklearn.tree._tree.Tree.recursive_partition' ignored

I tested this with the current git version. Using cython 0.16, the problem goes away.

Broken:

sudo make clean && sudo python2 setup.py install

Fixed:

sudo make clean && make cython && sudo python2 setup.py install
Issue 1:
I am exploring Apache SuperSet. I want to connect to mysql database on server i.e., EC2 instance. I have installed all the required libraries based on https://superset.incubator.apache.org/installation.html and launched the superset as well.

I did go through https://docs.sqlalchemy.org/en/12/core/engines.html#database-urls.

entered mysql://{username}:{password}@{ip address of ec2 instance}/{database name} in the URL

Although i have the library, yet when i try to connect the mysql it says "No module named 'MySQLdb'". (Yes i have installed mysql-client)

Please help me with how to proceed?

Issue 2:
In latest master, open METRICS popover, see missing saved metrics from the dropdown select.

To reproduce:

Open any charts that use sample data(e.g. FCC2018 Survey)

Go to Edit dataset. in metric tab, create 3 new metrics and Save

Go back to the control panel, click the pre-filled saved metric in METRICS

Save tab, click dropdown, see list missing the original one,

Actual

Screen.Recording.2021-01-28.at.6.04.50.PM.mov

Expected
Issue 1:
Only happens when the user function mutates the input

Originally from: http://stackoverflow.com/questions/20691168/pandas-apply-to-data-frame-groupby/20705226#20705226

Could be auto-fixed or maybe just a better error report to the user (about the dups)

In [40]: df = DataFrame(dict(A = ['foo','foo','bar','bar'], B = np.random.randn(4)),index=[1,1,2,2])

In [41]: df
Out[41]:
A B
1 foo 0.971425
1 foo -1.151693
2 bar 1.265031
2 bar -0.219011

[4 rows x 2 columns]

In [42]: def f(x):
x['std'] = x['B'].std()
return x
....:

Cannot straight perform the apply

In [44]: df.groupby('A').apply(f)
ValueError: cannot reindex from a duplicate axis

By using unique indices everything is ok (so straightforward to fix)

In [45]: df.reset_index().groupby('A').apply(f).set_index('index')
Out[45]:
A B std
index
1 foo 0.971425 1.501271
1 foo -1.151693 1.501271
2 bar 1.265031 1.049376
2 bar -0.219011 1.049376

[4 rows x 3 columns]

Issue 2:
Me and several of my colleagues typically create output for variables that have greek letters. Inside pandas, we then call them "epsilon", "sBar" or similar. Right now we have to run a string replace on the output of to_latex() and then manually save it to a file.

It'd be nice to be able to give to_latex() a template such as ['epsilon':'$\epsilon$', 'sBar':'$\bar s$'] and then to_latex() would replace these in the output accordingly.
Issue 1:
Describe what you were doing when the bug occurred:

Clicked restart and start profiling

Finished profiling

Clicked on a commit and got the error below

Please do not remove the text below this line

DevTools version: 4.2.1-3816ae7c3

Call stack: at chrome-extension://fmkadmapgofadopljbjfkapdkoienihi/build/main.js:40:157108

at Map.forEach ()

at commitIndex (chrome-extension://fmkadmapgofadopljbjfkapdkoienihi/build/main.js:40:157054)

at e.getRankedChartData (chrome-extension://fmkadmapgofadopljbjfkapdkoienihi/build/main.js:40:157577)

at vl (chrome-extension://fmkadmapgofadopljbjfkapdkoienihi/build/main.js:40:314907)

at gi (chrome-extension://fmkadmapgofadopljbjfkapdkoienihi/build/main.js:32:59907)

at jl (chrome-extension://fmkadmapgofadopljbjfkapdkoienihi/build/main.js:32:107381)

at Lc (chrome-extension://fmkadmapgofadopljbjfkapdkoienihi/build/main.js:32:92715)

at Pc (chrome-extension://fmkadmapgofadopljbjfkapdkoienihi/build/main.js:32:92640)

at wc (chrome-extension://fmkadmapgofadopljbjfkapdkoienihi/build/main.js:32:89544)

Component stack: in vl

in div

in div

in div

in wo

in Unknown

in n

in Unknown

in div

in div

in Li

in $e

in dn

in Ca

in Pc

Issue 2:
PLEASE INCLUDE REPRO INSTRUCTIONS AND EXAMPLE CODE

I got this error when I click 'Ranked'.

Please do not remove the text below this line

DevTools version: 4.0.4-3c6a219

Call stack: at chrome-extension://fmkadmapgofadopljbjfkapdkoienihi/build/main.js:11:11441

at Map.forEach ()

at commitIndex (chrome-extension://fmkadmapgofadopljbjfkapdkoienihi/build/main.js:11:11387)

at e.getRankedChartData (chrome-extension://fmkadmapgofadopljbjfkapdkoienihi/build/main.js:11:11920)

at _i (chrome-extension://fmkadmapgofadopljbjfkapdkoienihi/build/main.js:56:277123)

at Ha (chrome-extension://fmkadmapgofadopljbjfkapdkoienihi/build/main.js:43:55890)

at Xl (chrome-extension://fmkadmapgofadopljbjfkapdkoienihi/build/main.js:43:98280)

at Hl (chrome-extension://fmkadmapgofadopljbjfkapdkoienihi/build/main.js:43:84255)

at Fl (chrome-extension://fmkadmapgofadopljbjfkapdkoienihi/build/main.js:43:81285)

at chrome-extension://fmkadmapgofadopljbjfkapdkoienihi/build/main.js:43:25363

Component stack: in _i

in div

in div

in div

in Or

in Unknown

in n

in Unknown

in div

in div

in Ha

in le

in ve

in ko

in Ul
Issue 1:
pytorch version: '0.1.12_2'

It is not so easy to describe the exact bug here, but I think it is sufficient to provide an example in which strange results can be reproduced.

Use the serialised version of Tensor in the attachment. Then run this snippet (also included in the attachment)

import torch
from torch.autograd import Variable

x_restored = Variable(torch.load('x.pth'), requires_grad=False)
x_res_repeated = x_restored.repeat(10, 1)

print(x_restored.eq(x_res_repeated[0]).data.numpy().sum())
print(x_restored.eq(x_res_repeated[0]).data.sum())
print(x_restored.eq(x_res_repeated[0]).sum())

The output

3200
3200
Variable containing:
128
[torch.ByteTensor of size 1]

I would expect the summation to return the same results regardless of whether we operate on numpy arrays, pytorch tensors or Variable holding a Tensor in the data field. (Which means it should return a Variable holding a Tensor with the right capacity to store the summed value.

However, this is unfortunately not the case.

bug_reproduce.zip

Issue 2:
Test script:

import torch
from torch.autograd import Variable

byte_tensor = torch.ByteTensor(256)
byte_tensor[:] = 1
byte_variable = Variable(byte_tensor)
# The results should be the same, but actually not
print('byte_tensor.sum():')
print(byte_tensor.sum())
print('byte_variable.sum():')
print(byte_variable.sum())

Results:

byte_tensor.sum():
256
byte_variable.sum():
Variable containing:
0
[torch.ByteTensor of size 1]

Looks like ByteTensor does handle overflow but Variable(ByteTensor) doesn't. Such inconsistency has caused some troubles when I'm calculating my model's accuracy.
Issue 1:
Issue Type:

Bug Report

Ansible Version:

1.7

Environment:

Centos 6.5

Summary:

Duplicate security group names (between different VPCs) cause issues with instance creation--

When I run the ec2 module to create an instance in a VPC, it doesn't restrict the Security Group search to that VPC. So if I have the same SG name in 2 different VPCs (maybe bad practice), it can find the wrong one (and fail).

Steps To Reproduce:

Create 2 VPCs with a subnet

Create a security group in each with the same name

Run: ansible localhost -m ec2 -a "image=ami-fb8e9292 instance_type=m3.medium keypair= group= wait=yes region=us-east-1 vpc_subnet_id=" -c local

If that works, try the other VPC/Subnet

Expected Results:

Identify the security group associated with the correct VPC/Subnet and create the instance

Actual Results:

localhost | FAILED >> {
"failed": true,
"msg": "Instance creation failed => InvalidParameter: Security group sg-xxxxxxx and subnet subnet-xxxxxxx belong to different networks."
}

Issue 2:
ISSUE TYPE

Bug Report

COMPONENT NAME

/lib/ansible/plugins/strategy/__init__.py

ANSIBLE VERSION

ansible 2.5.0b1
config file = None
configured module search path = [u'/home/ec2-user/.ansible/plugins/modules', u'/usr/share/ansible/plugins/modules']
ansible python module location = /usr/local/lib/python2.7/site-packages/ansible
executable location = /usr/local/bin/ansible
python version = 2.7.13 (default, Jan 31 2018, 00:17:36) [GCC 4.8.5 20150623 (Red Hat 4.8.5-11)]

CONFIGURATION

no config changes

OS / ENVIRONMENT

Amazon Linux (essentially CentOS 7)

SUMMARY

If a role has handlers defined in ROLE/handlers/main.yaml they are not loaded, however if the file extension is changed to yml the handlers are loaded. If both main.yml and main.yaml are present, ansible complains:

ERROR! found multiple main files at /home/ec2-user/ansible/roles/unicorn/handlers, only one allowed

which suggests to me that it is processing the main.yaml still. The reason I believe it is not loading the handlers is my notify's began failing with:

ERROR! The requested handler 'HANDLER NAME' was not found in either the main handlers list nor in the listening handlers list

I've been using the yaml extension for all of my other yaml files and they work just fine, this is the only case I have noticed where that file extension produced issues.
Issue 1:
The number one thing I miss from X Windows when using MS Windows is the X Windows selection buffer. If possible, having the selection buffer be managed separately from the clipboard buffer would be awesome.

This would go well with the Xmouse utility.

Issue 2:
Environment

Windows build number: [run "ver" at a command prompt]
PowerToys version:
PowerToy module for which you are reporting the bug (if applicable):

Steps to reproduce

Expected behavior

Actual behavior

After upgrading from v0.18.0 to v0.18.1 Powertoys run bar doesn't launch.

Screenshots
Issue 1:
Are there plans to support the using keyword? In large projects there are usually set of modules called "utils" and it's pretty annoying to write import foo = utils.foo; for every used entity.

Issue 2:
We have some legacy .js files which we ported to .ts. These files are AMD modules, so we generate external AMD modules with tsc.

We have 2 files with the same name (app.js), one at the compilation root directory and another in a subdirectory. The inner file has the one at the root level as a dependency:

define(["app"], function(app){...});

As in RequireJS paths which don't start with . are treated a relative to the compilation root, it includes the root app.js correctly.

When porting this files to the equivalent Typescript:

import app = require("app");

The compiler started crashing with a Out of stack space error.

If we use a relative path: require("../app"), or we rename either file, the error goes away.

I think there are two issues here. On the one hand, I don't know if require expression above is wrong (suspect as it may seem). I expected tsc to follow the AMD resolution mechanism and map app to the app.ts in the compilation root but it seems to resolve to the current module, which would explain the out of stack error.

On the other hand, even if it were an invalid expression it should issue an error message not a compiler crash. So this one at least I believe to be a bug.
Issue 1:
It has been common to use the idiom of limiting a workgroup size by blocking on send to
a buffered chan, where the cap of the chan is set to the desired workgroup size. This
approach has also been used in the standard library.

However there has been discussion in both golang-nuts and golang-dev with comments that
suggest that this behaviour is not formally guaranteed to work (although it does in the
current runtime implementations).

I would be good to have either a guarantee that the idiom is guaranteed to continue to
work or that there is no guarantee.

For further details, see the thread here:
https://groups.google.com/d/topic/golang-nuts/wSRqJLvVTzo

Issue 2:
by schwehr:

While not required by any means, it would be really nice if go followed the more typical
source distribution convention. This would help speed go along towards more greater
adoption. e.g.

- go-1.1.3.tar.{gz,bz2,xz} that unpacks as go-1.1.3
- go-1.1.3/INSTALL file rather than having to dig down into go/doc/install-source.html
or at least just point from that top level INSTALL to the html file
Issue 1:
Hello,

I open the twbs variable.less file in visual studio and i have some syntax errors :-(

Please change variables.less //** to //-- or remove the star.

In CSS and Less this is /* Comment */

thanks

Issue 2:
I'm not sure why the comments in the variables.less got changed to this style //**, but it my opinion it was a brave move.

For me at least it prevents compilation when using the nodejs compiler lessc.

At also breaks the syntax highlighting in my IDE, Visual Studio 2013
Issue 1:
The encoding/binary package performs more slowly writing uint{8,16,32,64} data vs the corresponding int{8,16,32,64} data. The performance when writing *uints and *ints is almost identical.

See binary1.go in http://play.golang.org/p/--qpN-sy6o and binary1_test.go in http://play.golang.org/p/ypH8OTtYYx

Here is part of the output from running the benchmark test.

>go test binary1.go binary1_test.go -bench=.
testing: warning: no tests to run
PASS
BenchmarkWrite_int8 10000 171109 ns/op
BenchmarkWrite_uint8 5000 367821 ns/op
BenchmarkWrite_int8_p 10000 114706 ns/op
BenchmarkWrite_uint8_p 10000 115306 ns/op

The results for larger integers are similar.

The problem appears to be in the encoding/binary package's intDataSize function:

// intDataSize returns the size of the data required to represent the data when encoded.
// It returns zero if the type cannot be implemented by the fast path in Read or Write.
func intDataSize(data interface{}) int {
switch data := data.(type) {
case int8, *int8, *uint8:
return 1
case []int8:
return len(data)
case []uint8:
return len(data)

The first case of the switch does not include uint8 so the fast path is not used. The data size is looked up later using reflection instead of using this hard coded size. Larger unsigned ints are also missing later in the switch.

Issue 2:
The reflect slow path showed up in pprof for a uint64 data type:
https://code.google.com/r/cafxx-golang/source/detail?r=20d0244f15dc2584b540c90437f70ce7ab840219