Keras Custom Loss/Model Compilation

Please make sure that this is a bug. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:bug_template
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):
Windows 10 1909
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
No
TensorFlow installed from (source or binary):
Binary, Pip
TensorFlow version (use command below):
1.14
Python version:
3.7.3
Bazel version (if compiling from source):
GCC/Compiler version (if compiling from source):
CUDA/cuDNN version:
10/7.6.4
GPU model and memory:
RTX 2080 with 8GB VRAM, 16GB DRAM DDR4

You can collect some of this information using our environment capture
script
You can also obtain the TensorFlow version with: 1. TF 1.0: python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)" 2. TF 2.0: python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"
for 1: unknown 1.14.0
Describe the current behavior
Model compilation fails with the following error.
Traceback (most recent call last):
  File ".\vgg_loss.py", line 103, in <module>
    main()
  File ".\vgg_loss.py", line 97, in main
    model.compile(optimizer='adam', loss=some_loss, metrics=['accuracy'])
  File "C:\Users\Intel\Anaconda3\lib\site-packages\tensorflow\python\training\tracking\base.py",
line 457, in _method_wrapper
    result = method(self, *args, **kwargs)
  File "C:\Users\Intel\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py",
line 337, in compile
    self._compile_weights_loss_and_weighted_metrics()
  File "C:\Users\Intel\Anaconda3\lib\site-packages\tensorflow\python\training\tracking\base.py",
line 457, in _method_wrapper
    result = method(self, *args, **kwargs)
  File "C:\Users\Intel\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py",
line 1710, in _compile_weights_loss_and_weighted_metrics
    self.total_loss = self._prepare_total_loss(masks)
otal_loss
    per_sample_losses = loss_fn.call(y_true, y_pred)
  File "C:\Users\Intel\Anaconda3\lib\site-packages\tensorflow\python\keras\losses.py", line 215,
in call
    return self.fn(y_true, y_pred, **self._fn_kwargs)
  File ".\vgg_loss.py", line 86, in some_loss
    return mse(vgg_model.predict(y_pred, steps=1), vgg_model.predict(y_true, steps=1))
  File "C:\Users\Intel\Anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py",
line 1078, in predict       callbacks=callbacks)
    batch_outs = f(actual_inputs)
    run_metadata=self.run_metadata)
    run_metadata_ptr)
tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found.
   (0) Invalid argument: You must feed a value for placeholder tensor 'input_node' with dtype
     float
and shape [1,512,512,3]
          [[{{node input_node}}]]
          [[block4_conv3/Relu/_217]]
   (1) Invalid argument: You must feed a value for placeholder tensor 'input_node' with dtype float
 and shape [1,512,512,3]
          [[{{node input_node}}]]
 0 successful operations.
 0 derived errors ignored.

Describe the expected behavior
The code should compile properly.
Code to reproduce the issue
Provide a reproducible test case that is the bare minimum necessary to generate the problem.
I am attaching the link to the code.
Also download the vgg model whose link is provided in the code. I am also referencing here again.
Be sure to run it as
python vgg_loss.py -p <path_to_vgg_model>

Other info / logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.

I was able to replicate issue with given code in local for TF-1.14. Thank you!

I didn't check myself, but does this issue occur in TF2.0? I'm using TF1.14 only because model compilation makes a dummy call to the loss function which involves empty tensors and there are core tf elements, which in 2.0 are eagerly executed by default, so ends up throwing empty tensor errors.

I tried it using Tensorflow 2.0 and this was the error:-
Traceback (most recent call last):   File "vgg_loss.py", line 103, in <module>     main()   File "vgg_loss.py", line 97, in main     model.compile(optimizer='adam', loss=some_loss, metrics=['accuracy'])   File "C:\Users\Intel\Anaconda3\envs\tensorflowgpu\lib\site-packages\tensorflow_core\python\training\tracking\base.py", line 457, in _method_wrapper     result = method(self, *args, **kwargs)   File "C:\Users\Intel\Anaconda3\envs\tensorflowgpu\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 373, in compile     self._compile_weights_loss_and_weighted_metrics()   File "C:\Users\Intel\Anaconda3\envs\tensorflowgpu\lib\site-packages\tensorflow_core\python\training\tracking\base.py", line 457, in _method_wrapper     result = method(self, *args, **kwargs)   File "C:\Users\Intel\Anaconda3\envs\tensorflowgpu\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 1653, in _compile_weights_loss_and_weighted_metrics     self.total_loss = self._prepare_total_loss(masks)   File "C:\Users\Intel\Anaconda3\envs\tensorflowgpu\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 1713, in _prepare_total_loss     per_sample_losses = loss_fn.call(y_true, y_pred)   File "C:\Users\Intel\Anaconda3\envs\tensorflowgpu\lib\site-packages\tensorflow_core\python\keras\losses.py", line 221, in call     return self.fn(y_true, y_pred, **self._fn_kwargs)   File "vgg_loss.py", line 86, in some_loss     return mse(vgg_model.predict(y_pred, steps=1), vgg_model.predict(y_true, steps=1))   File "C:\Users\Intel\Anaconda3\envs\tensorflowgpu\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 909, in predict     use_multiprocessing=use_multiprocessing)   File "C:\Users\Intel\Anaconda3\envs\tensorflowgpu\lib\site-packages\tensorflow_core\python\keras\engine\training_arrays.py", line 722, in predict     callbacks=callbacks)   File "C:\Users\Intel\Anaconda3\envs\tensorflowgpu\lib\site-packages\tensorflow_core\python\keras\engine\training_arrays.py", line 299, in model_iteration     batch_outs = f(actual_inputs)   File "C:\Users\Intel\Anaconda3\envs\tensorflowgpu\lib\site-packages\tensorflow_core\python\keras\backend.py", line 3748, in __call__     [x._numpy() for x in outputs],  # pylint: disable=protected-access   File "C:\Users\Intel\Anaconda3\envs\tensorflowgpu\lib\site-packages\tensorflow_core\python\keras\backend.py", line 3748, in <listcomp>     [x._numpy() for x in outputs],  # pylint: disable=protected-access AttributeError: 'Tensor' object has no attribute '_numpy'

I met similar issues in tf=1.14.0 of above. But I can give a workaround solution.
First, for tf=1.14.0 of above you have to explicit assign y_true by label_ref=tf.keras.layers.Input. Otherwise, tf is not able compute shape.
            # workaround solution
            if LooseVersion(tf.__version__) < LooseVersion('1.14.0'):
                label_mask  = y_true
                pre_softmax = clf_out
            else:
                label_mask  = label_ref
                pre_softmax = clf_out

Second, label_ref should be the input and feed to model.
        # workaround solution
        if LooseVersion(tf.__version__) < LooseVersion('1.14.0'):
            input_list = [clf_input]
        else:
            input_list = [clf_input, label_ref]

Third, you have to adjust the output of the data pipeline and the input for evaluation
        # workaround solution
        if LooseVersion(tf.__version__) < LooseVersion('1.14.0'):
            outputX = dataX
        else:
            outputX = (dataX, dataY)

    # workaround solution
    if LooseVersion(tf.__version__) < LooseVersion('1.14.0'):
        model.evaluate(testX, testY, verbose=2, batch_size=batch_size)
    else:
        model.evaluate( (testX, testY), testY, verbose=2, batch_size=batch_size)

Finallu, for tf=2.x you have to disable eager_mode at the begin of the program.
import tensorflow as tf

# disable eager model for tf=2.x
if LooseVersion(tf.__version__) >= LooseVersion('2.0.0'):
    tf.compat.v1.disable_eager_execution()

The following is the minimal testcase which works well in tf=1.13.1 or above:
#%%
from distutils.version import LooseVersion
import numpy as np
import tensorflow as tf

# disable eager model for tf=2.x
if LooseVersion(tf.__version__) >= LooseVersion('2.0.0'):
    tf.compat.v1.disable_eager_execution()

batch_size = 100
#%%
def download_data():

    # get raw data
    (trainX, trainY), (testX, testY) = tf.keras.datasets.cifar10.load_data()
    trainX = trainX.astype(np.float32)
    testX  = testX.astype(np.float32)

    # ont-hot
    trainY = tf.keras.utils.to_categorical(trainY, 10)
    testY  = tf.keras.utils.to_categorical(testY , 10)

    # get validation sets
    training_size = 45000
    validX = trainX[training_size:,:]
    validY = trainY[training_size:,:]

    trainX = trainX[:training_size,:]
    trainY = trainY[:training_size,:]

    return trainX, trainY, validX, validY, testX, testY

#%%
def data_pipeline(dataX, dataY):

    # create dataset API
    def preprocess_fn(dataX, dataY):

        dataX = tf.image.random_flip_left_right(dataX)

        # workaround solution
        if LooseVersion(tf.__version__) < LooseVersion('1.14.0'):
            outputX = dataX
        else:
            outputX = (dataX, dataY)
        return outputX, dataY

    dataset = tf.data.Dataset.from_tensor_slices( (dataX, dataY) )
    dataset = dataset.shuffle(batch_size * 8)
    dataset = dataset.repeat()
    dataset = dataset.batch(batch_size)
    dataset = dataset.map(preprocess_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)
    dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)
    return dataset

#%%
class custom_model():
    def __init__(self):

        # custom loss
        def cw_loss(y_true, y_pred):

            # workaround solution
            if LooseVersion(tf.__version__) < LooseVersion('1.14.0'):
                label_mask  = y_true
                pre_softmax = clf_out
            else:
                label_mask  = label_ref
                pre_softmax = clf_out

            # API changed
            if LooseVersion(tf.__version__) < LooseVersion('1.14.0'):
                correct_logit = tf.reduce_sum(label_mask * pre_softmax, axis=1, keep_dims=True)
            else:
                correct_logit = tf.reduce_sum(label_mask * pre_softmax, axis=1, keepdims=True)

            distance = tf.nn.relu( pre_softmax - correct_logit + (1-label_mask) * 10)
            inactivate = tf.cast( tf.less_equal(distance, 1e-9), dtype=tf.float32)
            weight = tf.keras.layers.Activation('softmax')(-1e9*inactivate + distance)
            loss = tf.reduce_sum((1-label_mask) * distance * weight, axis=1)
            loss = tf.math.reduce_mean(loss)
            return loss

        # API changed
        if LooseVersion(tf.__version__) < LooseVersion('2.0.0'):
            model = tf.keras.applications.ResNet50(include_top=True, weights=None, input_shape=(32,32,3), pooling='max', classes=10)
        else:
            model = tf.keras.applications.resnet.ResNet50(include_top=True, weights=None, input_shape=(32,32,3), pooling='max', classes=10)

        clf_input = tf.keras.layers.Input(shape=(32,32,3), name="model/input")
        label_ref = tf.keras.layers.Input(shape=(10,) , name='label_ref')
        clf_out   = model(clf_input)

        # workaround solution
        if LooseVersion(tf.__version__) < LooseVersion('1.14.0'):
            input_list = [clf_input]
        else:
            input_list = [clf_input, label_ref]

        clf_model = tf.keras.models.Model(inputs=input_list, outputs=clf_out, name='clf_model')
        clf_model.compile(loss='categorical_crossentropy', optimizer='SGD', metrics=['accuracy', cw_loss])

        self.clf_model = clf_model

#%%
if __name__ == '__main__':

    # set GPU
    import os
    if os.environ.get("CUDA_VISIBLE_DEVICES") is None:
        os.environ["CUDA_VISIBLE_DEVICES"] = "0"

    # reset tf session
    tf.compat.v1.keras.backend.clear_session()
    gpu_options = tf.compat.v1.GPUOptions(allow_growth=True)
    sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))
    tf.compat.v1.keras.backend.set_session(sess)

    # prepare data
    trainX, trainY, validX, validY, testX, testY = download_data()
    train_gen = data_pipeline(trainX, trainY)
    valid_gen = data_pipeline(validX, validY)
    test_gen = data_pipeline(testX, testY)

    # build targeted model
    targeted_model = custom_model()
    model = targeted_model.clf_model

    # fit and evalutate
    model.fit(train_gen,
            steps_per_epoch = trainY.shape[0] // batch_size,
            validation_data = valid_gen,
            validation_steps= validY.shape[0] // batch_size,
            epochs=5,
            verbose=2)

    # workaround solution
    if LooseVersion(tf.__version__) < LooseVersion('1.14.0'):
        model.evaluate(testX, testY, verbose=2, batch_size=batch_size)
    else:
        model.evaluate( (testX, testY), testY, verbose=2, batch_size=batch_size)

The output in tf=1.13.1:
Epoch 1/5
 - 33s - loss: 2.4371 - acc: 0.2622 - cw_loss: 9.9063 - val_loss: 1.9639 - val_acc: 0.3262 - val_cw_loss: 9.8715
Epoch 2/5
 - 26s - loss: 1.8737 - acc: 0.3829 - cw_loss: 9.8150 - val_loss: 1.8466 - val_acc: 0.4072 - val_cw_loss: 9.8049
Epoch 3/5
 - 26s - loss: 1.6805 - acc: 0.4436 - cw_loss: 9.7643 - val_loss: 1.5574 - val_acc: 0.4428 - val_cw_loss: 9.7727
Epoch 4/5
 - 26s - loss: 1.5725 - acc: 0.4780 - cw_loss: 9.7303 - val_loss: 1.5726 - val_acc: 0.4788 - val_cw_loss: 9.7395
Epoch 5/5
 - 26s - loss: 1.5086 - acc: 0.5021 - cw_loss: 9.7073 - val_loss: 2.6803 - val_acc: 0.3964 - val_cw_loss: 9.8093
 - 4s - loss: 2.8093 - acc: 0.3939 - cw_loss: 9.8100

The output in tf=1.14.0:
Epoch 1/5
450/450 - 20s - loss: 2.5021 - acc: 0.2659 - cw_loss: 9.9067 - val_loss: 2.7677 - val_acc: 0.3236 - val_cw_loss: 9.8703
Epoch 2/5
450/450 - 13s - loss: 1.9023 - acc: 0.3856 - cw_loss: 9.8141 - val_loss: 2.2765 - val_acc: 0.4022 - val_cw_loss: 9.8072
Epoch 3/5
450/450 - 13s - loss: 1.6946 - acc: 0.4444 - cw_loss: 9.7639 - val_loss: 1.9999 - val_acc: 0.4464 - val_cw_loss: 9.7686
Epoch 4/5
450/450 - 13s - loss: 1.5590 - acc: 0.4810 - cw_loss: 9.7266 - val_loss: 1.5991 - val_acc: 0.4850 - val_cw_loss: 9.7293
Epoch 5/5
450/450 - 13s - loss: 1.4503 - acc: 0.5138 - cw_loss: 9.6943 - val_loss: 1.4578 - val_acc: 0.4950 - val_cw_loss: 9.6983
10000/10000 - 3s - loss: 1.4257 - acc: 0.5055 - cw_loss: 9.6953

The output in tf=2.0.0:
450/450 - 41s - loss: 2.3526 - accuracy: 0.3121 - cw_loss: 9.8773 - val_loss: 1.6616 - val_accuracy: 0.3846 - val_cw_loss: 9.8367
Epoch 2/5
450/450 - 30s - loss: 1.8045 - accuracy: 0.4361 - cw_loss: 9.7745 - val_loss: 2.3301 - val_accuracy: 0.4514 - val_cw_loss: 9.7658
Epoch 3/5
450/450 - 31s - loss: 1.6255 - accuracy: 0.4912 - cw_loss: 9.7207 - val_loss: 2.2725 - val_accuracy: 0.4530 - val_cw_loss: 9.7586
Epoch 4/5
450/450 - 30s - loss: 1.5215 - accuracy: 0.5255 - cw_loss: 9.6794 - val_loss: 5.8537 - val_accuracy: 0.4990 - val_cw_loss: 9.7133
Epoch 5/5
450/450 - 30s - loss: 1.4084 - accuracy: 0.5554 - cw_loss: 9.6447 - val_loss: 1.4579 - val_accuracy: 0.5442 - val_cw_loss: 9.6705
10000/10000 - 3s - loss: 1.5397 - accuracy: 0.5363 - cw_loss: 9.6739

Ok I'll try to give this a try and get back to you in a week.

Automatically closing due to lack of recent activity. Please update the issue when new information becomes available, and we will reopen the issue. Thanks!


add checks/warnings on ROI in STC

STC binarises an ROI and can be left with no voxels. We now warn/check about this.
Fixes #62

test failures due to outdated MacOS in azure. I've tried to fix that on master but then hit #64. Oh well. Merging as unrelated to this PR.


List overlay not rendering in 1.5

Hello, I am using version 1.5 rc1.
I have a list field with more than 100 items.
The problem is that, when I click the field to show the list of items, it only works if there's a scrollbar in the window, otherwise the overlay is not even inserted.
If there's a scrollbar in the window, it renders the overlay on top of the input.
The options object for the list field is like the following:
options: {                             items: items_list,                             openOnFocus: true,                             minLength: 0,                             match           : 'contains',                              markSearch: true                         }
In the following images I try to show the scenario. Inside the yellow square is the list field that is being clicked. Inside the green square is the place where the browser's scroll is supposed to be (or not).

(In this image I show how the overlay is shown when there's a scrollbar in the window. (e.g. when the browser's size is small enough)

(In this image I show how the overlay is not shown (not even rendered) if there's not a scrollbar)

(In this image I show how the overlay is shown if there's not a scrollbar but only when typing text)

I could not reproduce your issue, neither in my working environment, nor in a fiddle:
https://jsfiddle.net/voowqjae/
Tested on Win7 with Firefox and Chrome - both times with a browser window high enough to fit the overlay and with a smaller window that makes the overlay shrink to the maximum available height.

Thank you for your reply. I was able to reproduce the issue here: http://jsfiddle.net/dg4wo217/

I could still not reproduce the problem with the fiddle you provided.
The list opens for me fine whenever I click it, even if there's no scrollbar.

Okey can you check this one http://jsfiddle.net/6yqfpykz/

Now I can confirm the behaviour you're describing.
I've updated the fiddle to reduce the required code to reproduce the issue: http://jsfiddle.net/6yqfpykz/1/
Even though the first div is empty, it seems to be the culprit.

If you place the div on the right instead of the left, then it works well.

I can also notice that it also fails when the input is inside a "container" div.

In case anybody has this same issue and wants a workaround here it is:
Just add height: 101vh !important; to your body.

Closing issue: resolved


Emmet wrap with abbeviation preview shows tabs instead of spaces

Issue Type: Bug
Testing #44833
Steps

With "editor.renderWhitespace": "all"

In an html file
<div>
    a
    b
</div>

Select a + b

Run Wrap with abbreviation

Type div

bug
In the preview, see tabs for the indentation instead of spaces.

The inserted text correctly uses spaces however
VS Code version: Code - Insiders 1.22.0-insider (5fbcfff69a9af92efd823f18e4c9f4b8c4dbdf2a, 2018-03-27T20:55:46.655Z)
OS version: Darwin x64 17.4.0

System Info

Item
Value

CPUs
Intel(R) Core(TM) i7-4770HQ CPU @ 2.20GHz (8 x 2200)

Load (avg)
3, 2, 2

Memory (System)
16.00GB (0.09GB free)

Process Argv
/private/var/folders/_0/611900jn0yb43f1rc47_2wmh0000gp/T/AppTranslocation/03D24729-1C16-49B5-801F-6914290FC331/d/Visual Studio Code - Insiders.app/Contents/MacOS/Electron

Screen Reader
no

VM
20%

@mjbvz This seems harmless. Any scenario where this causes disruption?

This is actually two separate issues.
The one @mjbvz shows is because of how we define the text to wrap for the previews. The effect is that between the innermost element of the abbreviation and the text we're wrapping we always insert tabs. In here is where we use tabs directly but I think we could fetch configuration for the open editor and use tabs/correct amount of spaces accordingly.
A separate issue is when trying to wrap with a nested abbreviation, such as div>ul. The upstream module we use for expanding abbreviations adds the tab between the div and the ul blocks, and we change it if needed when inserting the result as a snippet. Since in the preview we are directly editing the open editor, I'm not sure if we can replace them easily from our side.
Maybe there's an option we can pass to the module so that we can force it to use a predefined amount of spaces.

The way to really fix this is not trivial and since the final snippet uses tabs/spaces as expected, I wouldnt want to spend much time here.


how about PAVE Penny pod, M-SHORAD turret, AIM-26 falcon, and quad missile rail?

I feel like you should add something to compliment the APKWS recently added, as well as the AIR-2 in BDA+. M-SHORAD I suggested for fun, as it combines ATGMs, short-range SAMs, and guns. I'm fine with or without it, it's just the PAVE Penny and AIM-26 are must-haves, in my eyes. (sorry if I sound bossy/demanding) also, please allow the twin missile rail to fire just one missile/bomb at a time. a quad missile rail for attack helicopters would also be quite nice, with the nodes at the bottom instead of on the side.

me again! can you add a twin rail that doesn't come preloaded with missiles, or at least add an option to fire one missile/bomb rather than 2 at a time from the existing part? also, a quad rail for attack helicopters would be nice, again, with the ability to launch just one missile at a time.
lastly, I would like to apologize if this is too much, bossy, and/or demanding.

sorry for closing it, I'm new to this.

oh! one more thing. 4-tube Skyguard-sparrow SAM turret and aspide missiles, please. sorry, again, if this is too much, demanding, and/or bossy.

sorry for closing it, I'm new to this.

Type 1130 CIWS from PLAN Type 052/055 Destroyer.

PART
{
// --- general parameters ---
name = LLL-T14ArmataHull
module = Part
author = LLL
// --- asset parameters ---
mesh = T-14-3.blend	//need convert to .mu file
scale = 1	// the scale is based on kerbalfield t-14 turret
rescaleFactor = 1
// --- node definitions ---
// definition format is Position X, Position Y, Position Z, Up X, Up Y, Up Z
node_stack_Turret = 0.0, 0.757, -1.098, 1.0, 0.0, 0.0, 1
node_stack_TrackLeft = -1.453, 0.424, -0.288, 0.0, 1.0, 0.0, 2
node_stack_TrackRight = 1.453, 0.424, -0.288, 0.0, 1.0, 0.0, 2
// node need to be adjust
// --- editor parameters ---
TechRequired = start
entryCost = 0
cost = 60000
category = none
subcategory = 0
title = RU T-14 Armata Tank hull
manufacturer = Uralvagonzavod (Научно-производственная корпорация «УралВагонЗавод»
description = The T-14 Armata (Russian: Т-14 «Армата», industrial designation Russian: Объект 148, 'Object 148'), is a Russian main battle tank (MBT) based on the Armata Universal Combat Platform.
// attachment rules: stack, srfAttach, allowStack, allowSrfAttach, allowCollision
attachRules = 1,1,1,1,1
// --- standard part parameters ---
mass = 35
dragModelType = default
maximum_drag = 0.01
minimum_drag = 0.01
angularDrag = 0
crashTolerance = 999999999
maxTemp = 3400
buoyancy = .4
vesselType = Rover
CrewCapacity = 3
MODULE
{
name = MissileFire
}
MODULE
{
name = BDModuleSurfaceAI
}
MODULE
{
name = RadarWarningReceiver
}
MODULE
{
name = ModuleWingCommander
}
MODULE
{
name = ModuleCommand
minimumCrew = 0
}
RESOURCE
{
name = ElectricCharge
amount = 50000
maxAmount = 50000
}
RESOURCE
{
name = CannonShells
amount = 100
maxAmount = 100
}
MODULE
{
name = ModuleReactionWheel
PitchTorque = 25
YawTorque = 25
RollTorque = 125
RESOURCE
{
name = ElectricCharge
rate = 0.5
}
}
MODULE
{
name = ModuleGenerator
isAlwaysActive = true
OUTPUT_RESOURCE
{
name = ElectricCharge
rate = 200
}
}
MODULE
{
name = HitpointTracker
ArmorThickness = 50
maxHitPoints = 35000
ExplodeMode = Never
}
}

Oh! one more thing. the American JDAM-ER


feat: circle visible windows

Describe your PR, what does it fix/add?
https://github.com/hyprwm/Hyprland/issues/6678
cyclenextvisible shoud works as cyclenext, until it reaches top-left window, then it should select last active window on other monitor
Is there anything you want to mention? (unchecked code, possible bugs, found problems, breaking compatibility, etc.)
there were strange pairs of loops with the same logic, I combined them into one, and also combined the conditions into one reusable function

(You titled this PR "circle" but I assume you mean "cycle"...)


pandoc_download leaves installer at working directory

The code does not remove the file from working directory.
So if code is run by Travis CI, the installer may get committed as well in the repository.
It should make sure that file is removed from the working directory after the run.

I'd like to work on this.

Do you have an example where this is happening?
The current sdist on pypi is fine: https://pypi.python.org/pypi/pypandoc

"Now, pypandoc leaves the installer in the current working directory after download, which is fine locally, but creates a problem when run on remote systems like Travis. The installer could get committed accidently to the repository. To solve this, I had to take a look at source code for pypandoc and call an internal method, which pypandoc basically uses to set the name of the installer. I use that method to find out the name of the file and then delete it after installation is over."
http://blog.fossasia.org/generating-a-documentation-site-from-markup-documents-with-sphinx-and-pandoc/

#135 is merged, so closing... I need to do a release...


Map search not working on mobile

Subject: Map search not working on mobile
Description:
Using mapsearch on mobile
Results:
I tried to look up couchers near me on my mobile, but te search query doesn't seem to be accepted.
Beside that, my location is marked as not specific enough. (Location used: Neerpelt, Pelt, Limburg, Belgium)
Side note: I also expected to have a function (especially on mobile) for couchers near me.
Backend version: develop-8f250aa9
Frontend version: develop-8f250aa9
User Agent: Mozilla/5.0 (Linux; Android 5.1; HUAWEI LYO-L21) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/95.0.4638.74 Mobile Safari/537.36
Page: https://couchers.org/search?#loc=1/38.4/14.1
User: richee (2740)

See #2575


Not the same tensorflow graph for EN-DE translation

Description
I am trying to run Universal Transformers for the English to German translation task. I am getting the error that the tf graphs are not the same. I searched and found a similar closed issue but the linked solution to the issue is unavailable.
$ pip freeze | grep tensor
mesh-tensorflow==0.1.18
tensor2tensor==1.15.7
tensorboard==2.4.1
tensorboard-plugin-wit==1.8.0
tensorflow==2.4.1
tensorflow-addons==0.12.1
tensorflow-datasets==4.2.0
tensorflow-estimator==2.4.0
tensorflow-gan==2.0.0
tensorflow-hub==0.11.0
tensorflow-metadata==0.27.0
tensorflow-probability==0.7.0

For bugs: reproduction and error logs
t2t-trainer \
  --data_dir=$DATA_DIR \
  --problem=translate_ende_wmt32k \
  --model=universal_transformer \
  --hparams_set=universal_transformer_base \
  --output_dir=$TRAIN_DIR

Error logs:
2021-02-17 10:36:35.448784: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
WARNING:tensorflow:From /home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow_gan/python/estimator/tpu_gan_estimator.py:42: The name tf.esti
mator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead.

WARNING:tensorflow:From /home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow_gan/python/estimator/tpu_gan_estimator.py:42: The name tf.esti
mator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead.

INFO:tensorflow:Loading hparams from existing json /home/mila/m/mittalsa/tensor2tensor/t2t_train/translate_ende_wmt32k/universal_transformer-universal_transformer_
base/hparams.json
I0217 10:36:58.888788<PHONE_NUMBER>96704 hparams_lib.py:64] Loading hparams from existing json /home/mila/m/mittalsa/tensor2tensor/t2t_train/translate_ende_wmt32k/uni
versal_transformer-universal_transformer_base/hparams.json
INFO:tensorflow:Configuring DataParallelism to replicate the model.
I0217 10:36:58.893124<PHONE_NUMBER>96704 trainer_lib.py:271] Configuring DataParallelism to replicate the model.
INFO:tensorflow:schedule=continuous_train_and_eval
I0217 10:36:58.893256<PHONE_NUMBER>96704 devices.py:76] schedule=continuous_train_and_eval
INFO:tensorflow:worker_gpu=1
I0217 10:36:58.893335<PHONE_NUMBER>96704 devices.py:77] worker_gpu=1
INFO:tensorflow:sync=False
I0217 10:36:58.893400<PHONE_NUMBER>96704 devices.py:78] sync=False
WARNING:tensorflow:Schedule=continuous_train_and_eval. Assuming that training is running on a single machine.
W0217 10:36:58.893464<PHONE_NUMBER>96704 devices.py:141] Schedule=continuous_train_and_eval. Assuming that training is running on a single machine.
INFO:tensorflow:datashard_devices: ['gpu:0']
I0217 10:36:58.894149<PHONE_NUMBER>96704 devices.py:170] datashard_devices: ['gpu:0']
INFO:tensorflow:caching_devices: None
I0217 10:36:58.894598<PHONE_NUMBER>96704 devices.py:171] caching_devices: None
INFO:tensorflow:ps_devices: ['gpu:0']
I0217 10:36:58.894993<PHONE_NUMBER>96704 devices.py:172] ps_devices: ['gpu:0']
INFO:tensorflow:Using config: {'_model_dir': '/home/mila/m/mittalsa/tensor2tensor/t2t_train/translate_ende_wmt32k/universal_transformer-universal_transformer_base', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 1000, '_save_checkpoints_secs': None, '_session_config': gpu_options {
  per_process_gpu_memory_fraction: 0.95
}
}
allow_soft_placement: true
graph_options {
  optimizer_options {
    global_jit_level: OFF
  }
}
isolate_session_state: true
, '_keep_checkpoint_max': 20, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, 'use_tpu': False, 't2t_device_info': {'num_async_replicas': 1}, 'data_parallelism': <tensor2tensor.utils.expert_utils.Parallelism object at 0x7f41a3b9b978>}
I0217 10:36:59.021266<PHONE_NUMBER>96704 estimator.py:191] Using config: {'_model_dir': '/home/mila/m/mittalsa/tensor2tensor/t2t_train/translate_ende_wmt32k/universal_transformer-universal_transformer_base', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 1000, '_save_checkpoints_secs': None, '_session_config': gpu_options {
  per_process_gpu_memory_fraction: 0.95
}
allow_soft_placement: true
graph_options {
  optimizer_options {
    global_jit_level: OFF
  }
}
isolate_session_state: true
, '_keep_checkpoint_max': 20, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_checkpoint_save_graph_def': True, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, 'use_tpu': False, 't2t_device_info': {'num_async_replicas': 1}, 'data_parallelism': <tensor2tensor.utils.expert_utils.Parallelism object at 0x7f41a3b9b978>}
WARNING:tensorflow:Estimator's model_fn (<function T2TModel.make_estimator_model_fn.<locals>.wrapping_model_fn at 0x7f41a3da70d0>) includes params argument, but params are not passed to Estimator.
W0217 10:36:59.021582<PHONE_NUMBER>96704 model_fn.py:629] Estimator's model_fn (<function T2TModel.make_estimator_model_fn.<locals>.wrapping_model_fn at 0x7f41a3da70d0>) includes params argument, but params are not passed to Estimator.
WARNING:tensorflow:ValidationMonitor only works with --schedule=train_and_evaluate
W0217 10:36:59.021708<PHONE_NUMBER>96704 trainer_lib.py:795] ValidationMonitor only works with --schedule=train_and_evaluate
INFO:tensorflow:Not using Distribute Coordinator.
I0217 10:36:59.036123<PHONE_NUMBER>96704 estimator_training.py:186] Not using Distribute Coordinator.
INFO:tensorflow:Running training and evaluation locally (non-distributed).
I0217 10:36:59.036407<PHONE_NUMBER>96704 training.py:645] Running training and evaluation locally (non-distributed).
INFO:tensorflow:Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 1000 or save_checkpoints_secs None.
I0217 10:36:59.036719<PHONE_NUMBER>96704 training.py:733] Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 1000 or save_checkpoints_secs None.
WARNING:tensorflow:From /home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
W0217 10:36:59.066916<PHONE_NUMBER>96704 deprecation.py:339] From /home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
2021-02-17 10:36:59.084372: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-02-17 10:36:59.089130: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2021-02-17 10:36:59.164164: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:b1:00.0 name: Quadro RTX 8000 computeCapability: 7.5
coreClock: 1.77GHz coreCount: 72 deviceMemorySize: 47.46GiB deviceMemoryBandwidth: 625.94GiB/s
2021-02-17 10:36:59.164344: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-02-17 10:36:59.172861: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
2021-02-17 10:36:59.173078: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
2021-02-17 10:36:59.178167: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-02-17 10:36:59.181463: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2021-02-17 10:36:59.188911: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2021-02-17 10:36:59.192324: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
2021-02-17 10:36:59.194594: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2021-02-17 10:36:59.203122: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
INFO:tensorflow:Reading data files from /home/mila/m/mittalsa/tensor2tensor/t2t_data/translate_ende_wmt32k-train*
I0217 10:36:59.222244<PHONE_NUMBER>96704 problem.py:653] Reading data files from /home/mila/m/mittalsa/tensor2tensor/t2t_data/translate_ende_wmt32k-train*
INFO:tensorflow:partition: 0 num_data_files: 100
I0217 10:36:59.262548<PHONE_NUMBER>96704 problem.py:679] partition: 0 num_data_files: 100
WARNING:tensorflow:From /home/mila/m/mittalsa/tensor2tensor/tensor2tensor/data_generators/problem.py:689: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`.
W0217 10:36:59.265164<PHONE_NUMBER>96704 deprecation.py:339] From /home/mila/m/mittalsa/tensor2tensor/tensor2tensor/data_generators/problem.py:689: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`.
WARNING:tensorflow:From /home/mila/m/mittalsa/tensor2tensor/tensor2tensor/utils/data_reader.py:276: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and:
`tf.data.TFRecordDataset(path)`
W0217 10:36:59.347080<PHONE_NUMBER>96704 deprecation.py:339] From /home/mila/m/mittalsa/tensor2tensor/tensor2tensor/utils/data_reader.py:276: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and:
`tf.data.TFRecordDataset(path)`
WARNING:tensorflow:From /home/mila/m/mittalsa/tensor2tensor/tensor2tensor/utils/data_reader.py:38: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W0217 10:36:59.483155<PHONE_NUMBER>96704 deprecation.py:339] From /home/mila/m/mittalsa/tensor2tensor/tensor2tensor/utils/data_reader.py:38: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W0217 10:36:59.483155<PHONE_NUMBER>96704 deprecation.py:339] From /home/mila/m/mittalsa/tensor2tensor/tensor2tensor/utils/data_reader.py:38: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
WARNING:tensorflow:From /home/mila/m/mittalsa/tensor2tensor/tensor2tensor/utils/data_reader.py:234: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W0217 10:37:00.108635<PHONE_NUMBER>96704 deprecation.py:339] From /home/mila/m/mittalsa/tensor2tensor/tensor2tensor/utils/data_reader.py:234: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
INFO:tensorflow:Calling model_fn.
I0217 10:37:00.293850<PHONE_NUMBER>96704 estimator.py:1162] Calling model_fn.
INFO:tensorflow:Setting T2TModel mode to 'train'
I0217 10:37:00.320769<PHONE_NUMBER>96704 t2t_model.py:2267] Setting T2TModel mode to 'train'
INFO:tensorflow:Using variable initializer: uniform_unit_scaling
I0217 10:37:01.047983<PHONE_NUMBER>96704 api.py:479] Using variable initializer: uniform_unit_scaling
INFO:tensorflow:Transforming feature 'inputs' with symbol_modality_33510_1024.bottom
I0217 10:37:07.056139<PHONE_NUMBER>96704 api.py:479] Transforming feature 'inputs' with symbol_modality_33510_1024.bottom
INFO:tensorflow:Transforming feature 'targets' with symbol_modality_33510_1024.targets_bottom
I0217 10:37:08.512664<PHONE_NUMBER>96704 api.py:479] Transforming feature 'targets' with symbol_modality_33510_1024.targets_bottom
INFO:tensorflow:Building model body
I0217 10:37:08.603129<PHONE_NUMBER>96704 api.py:479] Building model body
WARNING:tensorflow:From /home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/util/dispatch.py:201: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
W0217 10:37:09.968957<PHONE_NUMBER>96704 deprecation.py:537] From /home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/util/dispatch.py:201: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
I0217 10:37:11.088009<PHONE_NUMBER>96704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:11.111042<PHONE_NUMBER>96704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:11.127041<PHONE_NUMBER>96704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:11.190722<PHONE_NUMBER>96704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:11.235233<PHONE_NUMBER>96704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:11.259665<PHONE_NUMBER>96704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:13.880186<PHONE_NUMBER>96704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:13.896136<PHONE_NUMBER>96704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:13.911811<PHONE_NUMBER>96704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:13.972910<PHONE_NUMBER>96704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:14.009448<PHONE_NUMBER>96704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:14.025386<PHONE_NUMBER>96704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:14.041645<PHONE_NUMBER>96704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:14.103353<PHONE_NUMBER>96704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:14.140556<PHONE_NUMBER>96704 common_layers.py:51] Running in V2 mode, using Keras layers.
I0217 10:37:14.165198<PHONE_NUMBER>96704 common_layers.py:51] Running in V2 mode, using Keras layers.
INFO:tensorflow:Transforming body output with symbol_modality_33510_1024.top
I0217 10:37:16.450344<PHONE_NUMBER>96704 api.py:479] Transforming body output with symbol_modality_33510_1024.top
INFO:tensorflow:Base learning rate: 2.000000
I0217 10:37:20.661118<PHONE_NUMBER>96704 learning_rate.py:29] Base learning rate: 2.000000
INFO:tensorflow:Trainable Variables Total size: 63744000
I0217 10:37:20.667574<PHONE_NUMBER>96704 optimize.py:355] Trainable Variables Total size: 63744000
INFO:tensorflow:Non-trainable variables Total size: 5
I0217 10:37:20.669589<PHONE_NUMBER>96704 optimize.py:355] Non-trainable variables Total size: 5
INFO:tensorflow:Using optimizer adam
I0217 10:37:20.669764<PHONE_NUMBER>96704 optimize.py:200] Using optimizer adam
INFO:tensorflow:Done calling model_fn.
I0217 10:37:22.981385<PHONE_NUMBER>96704 estimator.py:1164] Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
I0217 10:37:22.982550<PHONE_NUMBER>96704 basic_session_run_hooks.py:546] Create CheckpointSaverHook.
Traceback (most recent call last):
  File "/home/mila/m/mittalsa/.conda/envs/t2t/bin/t2t-trainer", line 33, in <module>
    tf.app.run(main)
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/platform/app.py", line 40, in run
    _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/absl/app.py", line 300, in run
    _run_main(main, args)
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/absl/app.py", line 251, in _run_main
    sys.exit(main(argv))
  File "/home/mila/m/mittalsa/.conda/envs/t2t/bin/t2t-trainer", line 28, in main
    t2t_trainer.main(argv)
  File "/home/mila/m/mittalsa/tensor2tensor/tensor2tensor/bin/t2t_trainer.py", line 418, in main
    execute_schedule(exp)
  File "/home/mila/m/mittalsa/tensor2tensor/tensor2tensor/bin/t2t_trainer.py", line 371, in execute_schedule
    getattr(exp, FLAGS.schedule)()
  File "/home/mila/m/mittalsa/tensor2tensor/tensor2tensor/utils/trainer_lib.py", line 468, in continuous_train_and_eval
    self._eval_spec)
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/training.py", line 505, in train_and_evaluate
    return executor.run()
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/training.py", line 646, in run
    return self.run_local()
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/training.py", line 747, in run_local
    saving_listeners=saving_listeners)
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 349, in train
    loss = self._train_model(input_fn, hooks, saving_listeners)
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 1175, in _train_model
    return self._train_model_default(input_fn, hooks, saving_listeners)
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 1208, in _train_model_default
    saving_listeners)
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 1510, in _train_with_estimator_spec
    save_graph_def=self._config.checkpoint_save_graph_def) as mon_sess:
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 604, in MonitoredTrainingSession
    stop_grace_period_secs=stop_grace_period_secs)
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 1038, in __init__
    stop_grace_period_secs=stop_grace_period_secs)
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 749, in __init__
    self._sess = _RecoverableSession(self._coordinated_creator)
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 1231, in __init__
    _WrappedSession.__init__(self, self._create_session())
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 1236, in _create_session
    return self._sess_creator.create_session()
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 902, in create_session
    self.tf_sess = self._session_creator.create_session()
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 660, in create_session
    self._scaffold.finalize()
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 232, in finalize
    summary.merge_all)
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/training/monitored_session.py", line 297, in get_or_default
    op = default_constructor()
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/summary/summary.py", line 406, in merge_all
    return merge(summary_ops, name=name)
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/summary/summary.py", line 370, in merge
    with _ops.name_scope(name, 'Merge', inputs):
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 6495, in __enter__
    g_from_inputs = _get_graph_from_inputs(self._values)
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 6130, in _get_graph_from_inputs
    _assert_same_graph(original_graph_element, graph_element)
  File "/home/mila/m/mittalsa/.conda/envs/t2t/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 6065, in _assert_same_graph
    (item, original_item, graph, original_graph))
ValueError: Tensor("rec_layer_0/self_attention/multihead_attention/dot_product_attention/attention:0", shape=(), dtype=string, device=/device:GPU:0) must be from the same graph as Tensor("universal_transformer_hparams:0", shape=(), dtype=string) (graphs are FuncGraph(name=universal_transformer_parallel_0_5_universal_transformer_universal_transformer_body_encoder_universal_transformer_basic_foldl_while_body_988_rewritten, id=139918132471568) and <tensorflow.python.framework.ops.Graph object at 0x7f41a3d894e0>).

Solved in #1849
https://github.com/tensorflow/tensor2tensor/issues/1849


Add stylelint-order

Fixes #1
Instead of sorting alphabetically, which isn't very useful, I decided to sort properties semantically. This will help readability as you can quickly see what properties changes the CSS the most. The general order is: existence, box model, position, float, flexbox/grid/column, transform, visibility, style, interactivity. This is something I've been thinking about for a long time.
Thoughts?

// @kevva @CodeTheory @SamVerschueren @vadimdemedes

I used this model before but it's confusing and requires some thought when writing. Having a linter that tells you when it's wrong helps but when reading the code it might be harder to find what you're looking for.

It’s autofixable. So autofix on save would make it an afterthought.

Yes, writing it is no problem with a linter, but reading it is.

I don't really have any particular thought on this one. To be honest, I never thought about ordering my CSS properties. So whatever comes out of this, it will always be better because it enforces consistency.

This actually seems good for me. I often instinctively write CSS in blocks of what the properties do and then when I edit CSS, I add/edit around the properties that do the same sort of thing.
I think this will make scanning and changing CSS easier. 👍

Cool. Let's try it out then. Can always change it after some usage.

@CodeTheory Let me know if you see any missing CSS properties or something weird about the order ;)

FYI I hate this so much 😄
Good thing I can disable it :)

@bfred-it Why do you hate it? Have you tried it with auto-fix in the editor?

It doesn't let me group features logically
1b.  It's just too strict. I really like it in theory, but in practice, it just ends up being annoying. I'll disable it for now and we'll see what to do about it.

@bfred-it, you could still achieve that by using the same style as normalize.css, although I'm not a huge fan of that myself.
I still believe alphabetical order is easier to write (and read) while keeping your CSS consistent.


403... Again

https://github.com/wix-incubator/DeviantArt-API/issues/251
Same issue as last time. 403 error:
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd">
<META HTTP-EQUIV="Content-Type" CONTENT="text/html; charset=iso-8859-1">
<TITLE>ERROR: The request could not be satisfied</TITLE>

403 ERROR

The request could not be satisfied.

Request blocked.
We can't connect to the server for this app or website at this time. There might be too much traffic or a configuration error. Try again later, or contact the app or website owner.

If you provide content to customers through CloudFront, you can find steps to troubleshoot and help prevent this error by reviewing the CloudFront documentation.

Generated by cloudfront (CloudFront)
Request ID: UTdnpRGOFNqWrsh-z-wVw93Pgu8slZYppLVrEf7NeHHfsCEPdeweQw==

<ADDRESS>
</ADDRESS>

We can only suggest that you use IP address with better reputation
This will keep happening periodically as DDOS protection service ramps up in the response to the attacks
We are not aware how you can clear the reputation of IP address you are using

If anyone else is running into this, I have come up with a solution.
Cloudflare Warp running as a proxy.
warp-cli register
warp-cli proxy port <your preferred proxy port on <IP_ADDRESS>>
warp-cli mode proxy
warp-cli connect

Test
curl --proxy http://<IP_ADDRESS>:<port> https://www.cloudflare.com/cdn-cgi/trace/
You should see warp=on in the reply.
Then proxy all your API/website calls through this.
For more details: https://blog.cloudflare.com/announcing-warp-for-linux-and-proxy-mode/ (note: document is a little out of date)

We can only suggest that you use IP address with better reputation

@sasha-lerner I am hosting my application on Azure and paying the monthly fees accordingly.  Can you go into a little more detail on what you mean by this?
It would seem that you would account for the IP addresses of your registered application clients and remove them from these mitigations.
If you have determined that the same IP addresses of your registered client applications are causing the DDoS, you should disable the client registrations rather than blocking whole swaths of IP addresses and catching innocent bystanders in the process.
I have 7 different Oauth2 providers and none of them have ever gone out of service like DeviantArt has twice now for our application over the past 2 years.  This is rather concerning obviously something you should really be improving.

I appreciate the additional context @SomewhatDamaged.  I am not sure how effective blocking traffic/IPs are when you can simply workaround it... what's stopping the bad actors from doing the same as you have prescribed? 🤔
In any case, I am glad it's been resolved on our side for the meantime.  Onto the next fire...


Implements missing unit tests for BgeeDBUtils

In GitLab by @fbastian on Jul 14, 2015, 13:27
Some public methods of org.bgee.pipeline.BgeeDBUtils miss a unit test. Add all missing tests.

In GitLab by @vrechdelaval on Jul 20, 2015, 15:51
Resolved in issue#6


Update to newer dependencies

Unable to install (using poetry) without downgrades to requests and pandas
My project uses requests 2.23 and pandas 1.0.1, but pandas_profiling appears to be restricted to requests 2.22 and pandas 0.25.
Are there plans to test on more recent requests and pandas releases?

Same here, here is the output:

Pandas-profiling is now compatible with pandas 1+. For more information, please have a look at the v2.6.0 release notes.


Fixed out of order headers for default output

Type of Change

[ ] Bug fix (non-breaking change, fixes # )
[ ] New feature (non-breaking change, adds functionality, fixes # )
[ ] Breaking change (effects multiple commands or functionality, fixes # )
[ ] Ran manual Pester test and has passed (`.\tests\manual.pester.ps1)
[ ] Adding code coverage to existing functionality
[ ] Pester test is included
[ ] If new file reference added for test, has is been added to github.com/sqlcollaborative/appveyor-lab ?
[ ] Nunit test is included
[ ] Documentation
[ ] Build system

Purpose

I found that the default output looked funny on Get-DbaAgentOperator.  Found that the ComputerName, InstanceName, SqlInstance was not in same order as all other commands.
Approach

Corrected output.
Commands to test

See screenshot.
Screenshots

Learning

merci! 🏆


weedcoco "Upload failed" error

I haven't had this one before, this is the same file that worked in the same browser yesterday. I've tried in both MS Edge/Chrome and both report the same error.

I can't find anything in the logs to explain this :(
The basic explanation I'd guess is that the django server (or the reverse proxy server, or your connection to it) was down. Is it still failing?

I don't know whether your access was affected by some hosting maintenance overnight...

Tried again this morning from my computer and a colleague's and still getting that error that it failed to upload. Any idea why that would be happening? It only started failing post changing the image hash size

I hope it's now fixed. Needed to change some config that was reset with the maintenance by ICT :|


[NoQA] Add orderWeight to tag view and tag edit routes

Details

Fixed Issues

$ https://github.com/Expensify/App/issues/42056
PROPOSAL:
Tests

Preconditions:

Accounting beta enabled
Workspace connected to QBO

Go to workspace accounting and import Classes and Customers/Projects
Go to workspace tags
Click Classes then click on any subtag
Verify that the RHP for the selected subtag settings is opened
Go back to workspace tags
Click Customers/Projects then click on any subtag
Verify that the RHP for the selected subtag settings is opened

[X] Verify that no errors appear in the JS console

Offline tests

Same as Tests
QA Steps

Same as Tests

[X] Verify that no errors appear in the JS console

PR Author Checklist

[X] I linked the correct issue in the ### Fixed Issues section above
[X] I wrote clear testing steps that cover the changes made in this PR

[X] I added steps for local testing in the Tests section
[X] I added steps for the expected offline behavior in the Offline steps section
[X] I added steps for Staging and/or Production testing in the QA steps section
[X] I added steps to cover failure scenarios (i.e. verify an input displays the correct error message if the entered data is not correct)
[X] I turned off my network connection and tested it while offline to ensure it matches the expected behavior (i.e. verify the default avatar icon is displayed if app is offline)
[X] I tested this PR with a High Traffic account against the staging or production API to ensure there are no regressions (e.g. long loading states that impact usability).

[X] I included screenshots or videos for tests on all platforms
[X] I ran the tests on all platforms & verified they passed on:

[X] Android: Native
[X] Android: mWeb Chrome
[X] iOS: Native
[X] iOS: mWeb Safari
[X] MacOS: Chrome / Safari
[X] MacOS: Desktop

[X] I verified there are no console errors (if there's a console error not related to the PR, report it or open an issue for it to be fixed)
[X] I followed proper code patterns (see Reviewing the code)

[X] I verified that any callback methods that were added or modified are named for what the method does and never what callback they handle (i.e. toggleReport and not onIconClick)
[X] I verified that the left part of a conditional rendering a React component is a boolean and NOT a string, e.g. myBool && <MyComponent />.
[X] I verified that comments were added to code that is not self explanatory
[X] I verified that any new or modified comments were clear, correct English, and explained "why" the code was doing something instead of only explaining "what" the code was doing.
[X] I verified any copy / text shown in the product is localized by adding it to src/languages/* files and using the translation method

[X] If any non-english text was added/modified, I verified the translation was requested/reviewed in #expensify-open-source and it was approved by an internal Expensify engineer. Link to Slack message:

[X] I verified all numbers, amounts, dates and phone numbers shown in the product are using the localization methods
[X] I verified any copy / text that was added to the app is grammatically correct in English. It adheres to proper capitalization guidelines (note: only the first word of header/labels should be capitalized), and is either coming verbatim from figma or has been approved by marketing (in order to get marketing approval, ask the Bug Zero team member to add the Waiting for copy label to the issue)
[X] I verified proper file naming conventions were followed for any new files or renamed files. All non-platform specific files are named after what they export and are not named "index.js". All platform-specific files are named for the platform the code supports as outlined in the README.
[X] I verified the JSDocs style guidelines (in STYLE.md) were followed

[X] If a new code pattern is added I verified it was agreed to be used by multiple Expensify engineers
[X] I followed the guidelines as stated in the Review Guidelines
[X] I tested other components that can be impacted by my changes (i.e. if the PR modifies a shared library or component like Avatar, I verified the components using Avatar are working as expected)
[X] I verified all code is DRY (the PR doesn't include any logic written more than once, with the exception of tests)
[X] I verified any variables that can be defined as constants (ie. in CONST.js or at the top of the file that uses the constant) are defined as such
[X] I verified that if a function's arguments changed that all usages have also been updated correctly
[X] If any new file was added I verified that:

[X] The file has a description of what it does and/or why is needed at the top of the file if the code is not self explanatory

[X] If a new CSS style is added I verified that:

[X] A similar style doesn't already exist
[X] The style can't be created with an existing StyleUtils function (i.e. StyleUtils.getBackgroundAndBorderStyle(theme.componentBG))

[X] If the PR modifies code that runs when editing or sending messages, I tested and verified there is no unexpected behavior for all supported markdown - URLs, single line code, code blocks, quotes, headings, bold, strikethrough, and italic.
[X] If the PR modifies a generic component, I tested and verified that those changes do not break usages of that component in the rest of the App (i.e. if a shared library or component like Avatar is modified, I verified that Avatar is working as expected in all cases)
[X] If the PR modifies a component related to any of the existing Storybook stories, I tested and verified all stories for that component are still working as expected.
[X] If the PR modifies a component or page that can be accessed by a direct deeplink, I verified that the code functions as expected when the deeplink is used - from a logged in and logged out account.
[X] If the PR modifies the UI (e.g. new buttons, new UI components, changing the padding/spacing/sizing, moving components, etc) or modifies the form input styles:

[X] I verified that all the inputs inside a form are aligned with each other.
[X] I added Design label and/or tagged @Expensify/design so the design team can review the changes.

[X] If a new page is added, I verified it's using the ScrollView component to make it scrollable when more elements are added to the page.
[X] If the main branch was merged into this PR after a review, I tested again and verified the outcome was still expected according to the Test steps.

Screenshots/Videos

Android: Native

https://github.com/Expensify/App/assets/16493223/8ee37095-2150-49d6-8a84-04bccbcee26c

Android: mWeb Chrome

Networking issue

iOS: Native

https://github.com/Expensify/App/assets/16493223/0bcabb17-e797-4c52-b29c-5b098ca8e770

iOS: mWeb Safari

https://github.com/Expensify/App/assets/16493223/2b9e3117-dfbe-44ab-9ca1-69d5c29bde19

MacOS: Chrome / Safari

https://github.com/Expensify/App/assets/16493223/77055042-681c-43cc-a7f9-75bc8443cc2a

MacOS: Desktop

https://github.com/Expensify/App/assets/16493223/804ca9cc-d2da-4bbc-b1a7-99b018e07e7a

@s77rt @hayata-suenaga do we need a c+ review here?

@s77rt, could you make a PR against this branch -> https://github.com/Expensify/App/tree/qbo-freeze-branch? 🙇

Done!

Thank you!

🚀 Deployed to staging by https://github.com/hayata-suenaga in version: 1.4.77-11 🚀

platform
result

🤖 android 🤖
failure ❌

🖥 desktop 🖥
success ✅

🍎 iOS 🍎
success ✅

🕸 web 🕸
success ✅

🚀 Deployed to staging by https://github.com/hayata-suenaga in version: 1.4.77-11 🚀

platform
result

🤖 android 🤖
success ✅

🖥 desktop 🖥
success ✅

🍎 iOS 🍎
success ✅

🕸 web 🕸
success ✅

🚀 Deployed to production by https://github.com/mountiny in version: 1.4.78-5 🚀

platform
result

🤖 android 🤖
success ✅

🖥 desktop 🖥
success ✅

🍎 iOS 🍎
success ✅

🕸 web 🕸
success ✅

🚀 Deployed to production by https://github.com/Julesssss in version: 1.4.79-11 🚀

platform
result

🤖 android 🤖
success ✅

🖥 desktop 🖥
success ✅

🍎 iOS 🍎
success ✅

🕸 web 🕸
success ✅


BUG - Misaligned Boxes

BUG - Misaligned Boxes
The boxes are misaligned again, likely a css issue, to be explored
Requirements

Realign the boxes

Boxes are now aligned as intended


Make BITPOS in Bitmap handling stop_given

See https://github.com/apache/kvrocks/issues/2084

cc @PragmaTwice @git-hulk


add partOf GA tracking

Who is this for?
Analysts (@taceybadgerbrook) wanting to be able to analyse data grouped by partOf.
partOf includes series and seasons.
What is it doing for them?
This adds a new parameter to the data fetching methods of the relevant pages, similar to the catalogue Pageview.
I am going to go through and convert these pages to TS, and to use the getServerSideProps next. This was done first to unblock @taceybadgerbrook.

@jamesgorrie does this mean I could set the value of partOf to be OnHappiness, etc?

@jamesgorrie does this mean I could set the value of partOf to be OnHappiness, etc?

Nope, it means that if something is partOf On happiness, dimension3 will get set to YEY3ZBAAACEASBjA. "On happiness" could change over time, whereas the ID will remane unchanged.

@taceybadgerbrook anything that is partOf a series or season will have those IDs put into that dimensions, comma-separated.

So does this mean that I will have to update the comma-separated list for the custom dimension in Data Studio whenever I find Editorial have added more content to a season?

I'm not too sure how this links up with data studio.
What this does is populate the custom dimension3 (now called Part of) in GA with the IDs of anything that something is part of.
This should allow you to filter the by the seasons ID e.g. YEY3ZBAAACEASBjA. When something new is added by editorial to a season or series, it will populate this automatically.
This allows you to:

get all pageviews from anything that is part of YEY3ZBAAACEASBjA
any other group metrics around that grouping

Should we be changing "Series" to "Part of" in Cardigan/Page types?

@jamesgorrie Testing this out, I've found this doesn't pick up page views for the season pages themselves.

@taceybadgerbrook no, as a seson isn't partOf itself.
The query page.path=/seasons/{seasonId} or dimension3={seasonId} should capture all of it?

@jamesgorrie that makes sense to me. The definition is doing it's job. I'll have to create a table to collect data on the season pages themselves but this is still a whole lot easier than forever updating filters.
I think you can close this ticket.


Lower the frequency of checking for builds of already released versions

Signed-off-by: Peter Nied<EMAIL_ADDRESS>Description
Builds that have already been released should not be checked so often, following https://github.com/opensearch-project/opensearch-build/tree/main/jenkins#add-periodically-run-jobs set cron to H 1 * * *
Check List

[X] Commits are signed per the DCO using --signoff

By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license.
For more information on following Developer Certificate of Origin and signing off your commits, please check here.

Codecov Report

Merging #1475 (7d261e1) into main (c177c5a) will decrease coverage by 0.16%.
The diff coverage is n/a.

:exclamation: Current head 7d261e1 differs from pull request most recent head 5504004. Consider uploading reports for the commit 5504004 to get more accurate results

@@             Coverage Diff              @@
##               main    #1475      +/-   ##
============================================
- Coverage     94.45%   94.29%   -0.17%
  Complexity       12       12
============================================
  Files           141      141
  Lines          3084     3084
  Branches          8        8
============================================
- Hits           2913     2908       -5
- Misses          164      169       +5
  Partials          7        7

Impacted Files
Coverage Δ

src/system/temporary_directory.py
83.87% <0.00%> (-12.91%)
:arrow_down:

src/system/os.py
92.30% <0.00%> (-7.70%)
:arrow_down:

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Fixed last tick overlap for time scale

First off, thanks for all the great work on this library!
This is a fix for #2599. It hides the last label unless it's equally spaced.

It seems that this PR has been open for a while, what do you think about it @etimberg @tannerlinsley @simonbrunel? To me this just seems like a less complete version of #2600

Agreed

Closing as #2600 seems to be a better implementation of the same solution

No worries, hopefully #2600 can be resolved soon.
I'm sure everyone is busy, but it'd be great if someone could take a look at #2748 as well.  It fixes a bug labeled P0.

Any chance you'll revisit this? There hasn't been progress on #2600 for 2 months, and there are decent number of open issues around it. #3069 #2774

Hi, I'd be happy to take anything that will fix it! #2600 seems dead.

Thanks for this fix @ankane, this is issue still hasn't been resolved and your solution seems to work very nicely!

Hey everyone,
Still experiencing the issue.

No ticks should overlap each other. We're currently focused on 3.0, which has numerous bug fixes. If you can reproduce with 3.0.0-alpha please file a new bug with an interactive example to reproduce and we'll take a look

Should they overlap in 2.9.3? This is the version I'm using.

Overlapping labels always seems like a bad experience to me. So they "shouldn't", but that being said I personally don't have time to make fixes to 2.x anymore. I know that 3.x does have some fixes such as https://github.com/chartjs/Chart.js/pull/6927 that have the potential to fix this issue though I'm not sure if it does or not without testing

In my case they overlap when I set min/max. I simply removed boundary ticks. I am a beginner so I can hardly contribute to solving this. Hopefully this will be resolved in 3.x!


[UWP][Rendering] AccessViolationException on ImageSet when url is invalid

Platform
What platform is your issue or question related to?

UWP

Author or host
Host, custom host in a UWP application
Version of SDK
Nuget : AdaptiveCards.Rendering.Uwp 1.2.9
Details
When rendering an Imageset with an invalid url for an image, the rendering fails with a AccessViolationException, crashing the app as this error is a terminating exception.
UwpError.zip
 var cardJson = @"{
    ""$schema"": ""http://adaptivecards.io/schemas/adaptive-card.json"",
    ""type"": ""AdaptiveCard"",
    ""version"": ""1.2"",
    ""body"": [
        {
            ""type"": ""TextBlock"",
            ""text"": ""Images !"",
            ""size"": ""Medium"",
            ""weight"": ""Bolder""
        },
        {
            ""type"": ""ImageSet"",
            ""imageSize"": ""medium"",
            ""images"": [
                {
                    ""type"": ""Image"",
                    ""url"": ""-this-is-not-a-url-"",
                    ""size"": ""Medium""
                },
                {
                    ""type"": ""Image"",
                    ""url"": ""https://avatars0.githubusercontent.com/u/6154722?s=200&v=4"",
                    ""size"": ""Medium""
                },
                {
                    ""type"": ""Image"",
                    ""url"": ""https://avatars0.githubusercontent.com/u/6154722?s=200&v=4"",
                    ""size"": ""Medium""
                }
            ]
        }
    ]
}";

            var renderer = new AdaptiveCardRenderer();
            var card = AdaptiveCard.FromJsonString(cardJson);
            var renderedAdaptiveCard = renderer.RenderAdaptiveCard(card.AdaptiveCard);
            gridMain.Children.Add(renderedAdaptiveCard.FrameworkElement);

Sample project attached.

Current behavior is that if the first URL is invalid the image set is not rendered, but if other urls are invalid all other images are rendered. We are not currently reproducing an AV


Add some obr reset/clean/delete CLI option

basically an alias for rm -rf workspace/ view/

possibly related to #106

basically an alias for rm -rf workspace/ view/

maybe we could do rm -rf workspace/ view/ and immediately call obr init again. This however would to figure out the config.yaml.


Fix the account state panel

The account state has changed, so currently the panel is empty, because the current code filters the values.
It is now possible to store any value, not just resources, so rename the panel to just "Storage" (feel free to change to something similar, like "Account State", "Account Storage", etc)
Also, the keys are no longer types, but arbitrary identifiers.
Note: I quickly fixed this, please test it

Tested. Works.

@10thfloor CI says

error Your lockfile needs to be updated, but yarn was run with --frozen-lockfile.
Didn't change any dependencies though 🤔

circular-json was deprecated, I wonder if update the lockfile with yarn upgrade would solve the error above?


Add list variant for ButtonGroup

Fixes #1371
@interactivellama Would appreciate some feedback to check if this is going in the right direction :)
Additional description
Example of list variant:

CONTRIBUTOR checklist (do not remove)
Please complete for every pull request

[x] First-time contributors should sign the Contributor License Agreement.  If you haven't before, wait a few minutes and a bot will comment on this pull request with instructions.
[x] npm run lint:fix has been run and linting passes.
[x] Mocha, Jest (Storyshots), and components/component-docs.json CI tests pass (npm test).
[ ] Tests have been added for new props to prevent regressions in the future. See readme.
[x] Review the appropriate Storybook stories. Open http://localhost:9001/.
[ ] The Accessibility panel of each Storybook story has 0 violations (aXe). Open http://localhost:9001/.
[ ] Review tests are passing in the browser. Open http://localhost:8001/.
[ ] Review markup conforms to SLDS by looking at DOM snapshot strings.

REVIEWER checklist (do not remove)

[ ] TravisCI tests pass. This includes linting, Mocha, Jest, Storyshots, and components/component-docs.json tests.
[ ] Tests have been added for new props to prevent regressions in the future. See readme.
[ ] Review the appropriate Storybook stories. Open http://localhost:9001/.
[ ] The Accessibility panel of each Storybook story has 0 violations (aXe). Open http://localhost:9001/.
[ ] Review tests are passing in the browser. Open http://localhost:8001/.
[ ] Review markup conforms to SLDS by looking at DOM snapshot strings.

Required only if there are markup / UX changes

[ ] Add year-first date and commit SHA to last-slds-markup-review in package.json and push.
[ ] Request a review of the deployed Heroku app by the Salesforce UX Accessibility Team.
[ ] Add year-first review date, and commit SHA, last-accessibility-review, to package.json and push.
[ ] While the contributor's branch is checked out, run npm run local-update within locally cloned site repo to confirm the site will function correctly at the next release.

The children API should be the same as: https://github.com/salesforce/design-system-react/blob/master/components/tooltip/examples/button-group.jsx#L16
No li tags in the children, please. Please add tooltip wrapped buttons as you see in the above example. That was the original problem with the current component.
This one is a little difficult, and I did not write a good issue spec for it. I believe that this issue requires modifying Tooltip to allow the wrapping div to be an li when it is within a ButtonGroup.
I propose implementing this with: Button group should duplicating the Tooltip Component (clone element) and pass in a variant of list-item to Tooltip if one of Button Group's children is a tooltip (iterate over the children). This will cause the div wrapped around the button to become an li tag, then you will get the correct look and -1px border overlap.
<ul>
  <li> <-- Tooltip wrapper of variant `list-item` instead of standard `div`
    <button/> <-- Button component
  </li>
</ul>

Please note in your screen that you have double button borders, and that https://www.lightningdesignsystem.com/components/button-groups/ does not have double button borders (they overlap by 1 pixel).
Does that make sense?

Yup it does make sense to me 👍 Thanks for the feedback! Will look into it :)

Please re-open based on feedback if you would like to pursue fixing the issue.


Per-polygon custom colourisation

Due to transformation operations such as union, subtract wiping out colors of the object and colors of object not being saved per polygon but as an overall for the entire object, the way of rendering multiple objects "combined" would require a different approach

Currently, the canvas can only have a single Shape of a single colour:
render(colourize(shape, green));

Rendering multiple Shapes would thus require first joining them all together with union().

The initial plan was to use a custom colourize() to attach colours to each vertex rather than the specified Shape as a whole. However, if the regl-renderer library's transformation functions do not carry over these colours in full (eg if union resets all colours as a whole, or improperly applies colours where polygons meet), we will still have problems with colouring. It would have allowed for this to work:
render(union(
    colourize(shape, green),
    colourize(shape2, blue)
));

As opposed to only being able have colourize() work properly when done last:
render(colourize(
    union(shape, shape2),
    green
));

Currently looking at a change to the design of the colouring and rendering functions. In the new design, Shapes would first need to be store()d with the module, and render() would be called later down the line. This would mean that more than one Shape could be stored (thus they may be of different colours). Calling render() as appropriate would display all the stored Shapes together on a canvas along with the other Entities like the grid & axis. (And with #21, each call to render() would spawn its own canvas, before wiping the Shapes stored so far.)
Rendering Shapes would then work like so (also taking into account https://github.com/source-academy/js-slang/issues/1238):
store(shape);
store(shape2);
render();

Given the limitations of transformations on coloured Shapes, it would then make sense for colourising to be a final operation right before storage, like how it needs to be done just before the single render() at the moment. Thus, a replacement for colourize() would colour and then store a specified Shape:
store_as_colour(shape, green);
store_as_colour(shape2, blue);
store(shape3);
render();

For added convenience, render() could take in an optional Shape. It would be equivalent to storing the specified Shape before proceeding with the render:
render(shape);
// Equivalent to
store(shape);
render();

Still some things to iron out and test, but, implemented.


Remove alpha2 from apiVersion in cert example

while using the example to create a certificate kubectl aplly threw an error
error: resource mapping not found for name: "example-cert" namespace: "cert-manager" from "cert-manager/issuers/test-hetzner-cert.yaml": no matches for kind "Certificate" in version "cert-manager.io/v1alpha2" ensure CRDs are installed first
removing the alpa2 resolved the issue

Can you also fix the ClusterIssuer in that MR?

Line 42 in the README.md

@vadimkim can you merge those two small changes?


Bug: Conditional scope statement stopped working

Bicep version
Bicep CLI version 0.30.23
Describe the bug
I noticed today that conditional scope statements (e.g., for nested deployments) started failing in any AVM template that uses them. The issue surfaces as follows:
A previously valid deployment like
param storageAccountResourceId string = ''

module storageAccount_sbdc_rbac 'modules/nested_storageRoleAssignment.bicep' = if (isManagedIdentityInUse && !empty(storageAccountResourceId)) {
  name: '${server.name}-stau-rbac'
  scope: !empty(storageAccountResourceId)
    ? resourceGroup(split(storageAccountResourceId!, '/')[2], split(storageAccountResourceId!, '/')[4])
    : resourceGroup()
   (...)

now shows the error

The property "scope" expected a value of type "resourceGroup" but the provided value is of type "{ id: string, location: string, managedBy: string, name: string, properties: properties, tags: Tags, type: string }".bicep[BCP036](https://aka.ms/bicep/core-diagnostics#BCP036)

Which implies that the return value of the resourceGroup() function is not detected as a proper resource reference, but instead the object that a resource group is (hene the type description).
Now the fun part is the following: The resourceGroup() function works absolutely fine - as long as you do not reference a parameter (a variable is fine). So for example this works like a charm:
module storageAccount_sbdc_rbac 'modules/nested_storageRoleAssignment.bicep' = if (isManagedIdentityInUse && !empty(storageAccountResourceId)) {
  name: '${server.name}-stau-rbac'
  scope: !empty('heyThere')
    ? resourceGroup(split(storageAccountResourceId!, '/')[2], split(storageAccountResourceId!, '/')[4])
    : resourceGroup()
   (...)

or
module storageAccount_sbdc_rbac 'modules/nested_storageRoleAssignment.bicep' = if (isManagedIdentityInUse && !empty(storageAccountResourceId)) {
  name: '${server.name}-stau-rbac'
  scope: true == true
    ? resourceGroup(split(storageAccountResourceId!, '/')[2], split(storageAccountResourceId!, '/')[4])
    : resourceGroup()
   (...)

or
var storageAccountResourceId = 'myId'
module storageAccount_sbdc_rbac 'modules/nested_storageRoleAssignment.bicep' = if (isManagedIdentityInUse && !empty(storageAccountResourceId)) {
  name: '${server.name}-stau-rbac'
  scope: !empty(storageAccountResourceId)
    ? resourceGroup(split(storageAccountResourceId!, '/')[2], split(storageAccountResourceId!, '/')[4])
    : resourceGroup()
   (...)

So it seems that just the reference of a parameter changes the way the resourceGroup() function behaves / is interpreted and breaks the template. This is, naturally, quite unfortunate, as we deploy quite a sizable amount of modules into different scopes based on input parameters.
To Reproduce
Steps to reproduce the behavior:
Just copy the following snippet into a Bicep file:
param someParam string = ''

module someDeployment 'br/public:avm/res/key-vault/vault:0.10.2' = {
  name: 'someDeploymentName'
  params: {
    name: 'iAmUnique'
  }
  scope: !empty(someParam) ? resourceGroup(someParam) : resourceGroup()
}

+1

@AlexanderSehr did you added the wrong Bicep CLI version in the description of the issue?

Hey @slavizh, in fact, I did not. Locally I was runnning 0.30.23 and on the pipeline agents the new 0.31.34. It did fail in both (and I don't know why as you'd think it's a bug introduced via a new version). Not that it should matter much, but the az bicep version also already failed with version 0.30.23.

@AlexanderSehr strange as I have code similar to the one you have reported and it is failing only on 31.34. This failure is seen even in VSC before deployment. As soon as I downgrade to 30.23 I do not see the issues and the code is working fine.

@AlexanderSehr - can you try explicitly setting the version in your pipeline to 0.30 with az bicep install -v "0.30.23"?
I'm wondering if there is possibly an issue on the local machine where bicep is falsely telling you it is using 0.30, but is actually using 0.31.

@AlexanderSehr - can you try explicitly setting the version in your pipeline to 0.30 with az bicep install -v "0.30.23"?
I'm wondering if there is possibly an issue on the local machine where bicep is falsely telling you it is using 0.30, but is actually using 0.31.

Hey @alex-frankel,
that did, in fact, work: https://github.com/AlexanderSehr/bicep-registry-modules/actions/runs/11783023645
I guess my local installation wanders in mysterious ways...

+1 This is breaking all our production pipelines at the moment since 0.31.34. Running  through Azure Pipelines using task AzureResourceManagerTemplateDeployment@3

@AlexanderSehr : what is now the plan for the bicep-registry-modules repo? Are you going to pin the bicep version in the pipeline to the last working version, until this bug will be fixed?

Hey @peterbud,
that depends a bit. If the PG releases a new version with @jeskew's fix that rather sooner than later, then we may be fine. If not - than we can hardcode the version in the CI, e.g., tomorrow. @alex-frankel & @jeskew, can you share you thoughts? :)

@AlexanderSehr I would suggest having the ability to hard-code the version will serve in the future as well. Bugs happen. We have that mechanism in our pipelines and we have used it a couple of times until the team releases fixes. Usually the fixes are released fast sometimes a fix might affects us but it is not so critical and could be released on the next month.

@AlexanderSehr I would suggest having the ability to hard-code the version will serve in the future as well. Bugs happen. We have that mechanism in our pipelines and we have used it a couple of times until the team releases fixes. Usually the fixes are released fast sometimes a fix might affects us but it is not so critical and could be released on the next month.

Fair. Then again, at the same time we'd want to enable the use of the latest Bicep features at least in AVM (which was the original reasoning for opening this issue) as opposed to preventing our contributors from using them. It's a double-edged sword. We do however have an issue to make all versions we do configurable. We just haven't gotten around to it yet.
That being said, it just occured to me that in this case we actually cannot easily go back / hardcode the version because of one other change this release brought with it, which was a change to the main.json files where it seems some explicit dependsOn blocks are removed in favor of implicit dependencies. As there is a test running that ensures that the Bicep file is always up to date (ignorierung the version-dependent template hash), this means that if we hardcode version 0.30.23 and any user uses 0.31.34, this test will always fail for them unless they happen to also downgrade their version (which the error won't tell them). Also, we now have already quite a few templates with 0.31.34 published, so they'd immediately start failing too. Oh my.
Now I really hope a 'hotfix' release comes soon.

I do not know the details of your pipelines setup so end decision is yours. The above is more of an advise from me as we have been using bicep cli to compile the code from one of the earliest versions and sometimes when we have bugs we have the capability to revert to specific version on our hosted agents. Note also that bicep CLI is suppose to have a breaking change release when v1.0 is released. I do not know if that is still in plan, when will it happen and what the breaking changes will be but it is may be something that have in mind.

I do not know the details of your pipelines setup so end decision is yours. The above is more of an advise from me as we have been using bicep cli to compile the code from one of the earliest versions and sometimes when we have bugs we have the capability to revert to specific version on our hosted agents. Note also that bicep CLI is suppose to have a breaking change release when v1.0 is released. I do not know if that is still in plan, when will it happen and what the breaking changes will be but it is may be something that have in mind.

And it is much appreciated 🚀 Just wanted to provide some context. :)
In our case it's more our CI being 'breakingly' affected by a non-breaking change introduced in the latest version 😄 But anyways - we're diverging from the original issue.

@AlexanderSehr Sorry for the delay on this! v0.31.92 was just released and should fix this for you.


refactor(codegen): reorder dependencies in Cargo.toml

#6514  👈
main

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Cosmetic change, so merging without review.

Merge activity

Oct 13, 8:29 AM EDT: The merge label '0-merge' was detected. This PR will be added to the Graphite merge queue once it meets the requirements.


Security

Added eslint with typescript support and the security plugin to check for common security issues.
Added validation of Basecamp payloads to prevent sending a false callback url which would allow a server other than Basecamp's to receive notification messages about private Github repositories.

Forgot to add, add a husky git hook to run eslint.

@thrymgjol, looks like there are merge conflicts. You'll have to fix that and push up changes.
Also, since we added .travis.yml to the repo now, add to the .travis.yml file running eslint on each commit to make sure that all PRs now into master require eslint to pass.


Java EE 7 compliant BOM proposal

Can't say for sure, if we want to deviate from Java EE 7 for some of the 3 components, but this proposal sticks to the exact versions in javaee-web-api : 7.0 (the smaller subset of javaee-api)

Since merges with the other PR (that had everything factored in from this PR before merging anyway) I don't think anything is missing from this one? Otherwise please reopen it.


Rewrite questbook killing objectives

We should not have to look in the fight history. Instead, when a fight complete, we need to look for currently active quests and update them.

It seems like it partly works like I want it to, see adr_battle.php, L1213 as of 3e3fd32863a182c1dff2dcbfd385b1b72695de9e
      // Check if the character killed a monster that he needed for a killing quest !
      $sql    = " SELECT * FROM " . ADR_QUEST_LOG_TABLE . "
        WHERE quest_kill_monster = '" . $monster['monster_name'] . "'
      AND quest_kill_monster_current_amount < quest_kill_monster_amount
      AND user_id = '" . $challenger['character_id'] . "'
        ";

There's one really weird element...
in zone_npc_actions, in the $npc_give_action block, we carry around $npc_give_row...
We check if the user has the item.
Two things:

We need to use an IN or something.
The check itself actually checks if we don't have the item (report cheat if we don't, which message_dies), AND that the NPC doesn't require killing monsters.
(^ really, we could check that BEFORE looping and doing N SELECT FROM items)

So that means, it's really only an (npc accepts clue) OR (npc wants item(s)) OR (npc wants monsters killed)
Then... We delete the item (we probably should do a DELETE LIMIT? Or maybe we actually want to handle quantity?).
And.. Then we check that A quest wanted those items?! We have gone through npc_items, and emitted a CHEAT if we didn't find the required item.
We don't check which quest requires it. We check that some quest wanted that item.
...I actually think this is a check that we do have the quest?
We need to change that...
After we've done checking that we do have these items in the quest log, we yet again check that there's no mob kills required.
But it doesn't stop there!
We just did check if we could complete the quest via items.
Now we check if we can complete it via monsters killed.
All good? Except that this line:
if (... && $npc_give_row['quest_kill_monster_current_amount'] == $npc_give_row['npc_kill_monster_amount'])

most definitely doesn't work – or at least doesn't do what it should. Since $npc_give_row is just a check from the adr npcs table, not from the quest log.
I suppose it didn't matter then, because it only deletes quests with the correct amount, but it's still really bad.

as an option you can make unique monsters, objects, locations. Access to them appeared only when obtaining the necessary quest and also became unavailable after its execution. Quest items are indestructible and can only be sold to a game store.


[cherry-pick][release-2.40.0][Fixes #21927] Compress (Un)BoundedSourceAsSdfWrapper element and restriction coders

…rceAsSdfWrapper element and restriction coders
Cherry-pick of https://github.com/apache/beam/pull/21928

[Fixes #21927] Compress BoundedSourceAsSdfWrapper element and restriction coders

A typical BoundedSource may be split into many BoundedSource instances during initial splitting. Doing a simple test of the BigtableSource shows that encoding 10 instances after splitting took on average 102660 bytes while compressing each instance separately after encoding took 1639 bytes for a >60x improvement.

Also handle unbounded sources.

Please add a meaningful description for your change here

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r: @lukecwik

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moveit: Add packages from MoveIt! project

Particularly this commit brings in

moveit_core;
moveit_ros_perception;
moveit_ros_planning;
moveit_msgs

and their build dependencies like

object_recognition_msgs;
srdfdom.

Also they are included into packagegroup-ros-world.bb.

I also just tested bitbake core-image-ros-world with this build configuration:
Build Configuration:
BB_VERSION        = "1.33.2"
BUILD_SYS         = "x86_64-linux"
NATIVELSBSTRING   = "fedora-23"
TARGET_SYS        = "i586-oe-linux"
MACHINE           = "qemux86"
DISTRO            = "nodistro"
DISTRO_VERSION    = "nodistro.0"
TUNE_FEATURES     = "m32 i586"
TARGET_FPU        = ""
meta              = "HEAD:ac5c08ea00816852a712a8d7025bce7e865460ec"
meta-oe
meta-python
meta-multimedia   = "master:1dff2351aa6cdafa5a501e8956cb853ab17ed9ae"
meta-qt4          = "master:2c7f8df9039be498f8a2232d1428adb7f4e5e800"
meta-intel-realsense = "master:1d50cb5b82926f8e69a78999e9e8d2ffe2169897"
meta-ros          = "rojkov-moveit-v1:b4d98d9cdda14c1cc204b01f9ed9fba10338bead"

Everything builds successfully. Building fcl emits the following warning:
WARNING: fcl-0.3.2-r0 do_package_qa: QA Issue: fcl: /work/i586-oe-linux/fcl/0.3.2-r0/packages-split/fcl/usr/lib/libfcl.so contains probably-redundant RPATH /usr/lib [useless-rpaths]

This is not directly related to this pull request. bitbake fcl probably has this warning since quite some time; it is just become visible as it is now become part of the core-image-ros-world.
I will merge the pull request, no matter if the warning is addressed or not. I do not consider warnings as show stoppers for merging pull requests. If not addressed, I simply add an issue in our issue tracker to remember the issue and to let users know that we are aware.

Thanks @bulwahn !
I have

addressed the review comments;
added a FCL patch fixing the RPATH issue;
rebased and force pushed.

I tested the pull request successfully on these two build configurations:
Build Configuration:
BB_VERSION        = "1.33.2"
BUILD_SYS         = "x86_64-linux"
NATIVELSBSTRING   = "fedora-23"
TARGET_SYS        = "i586-oe-linux"
MACHINE           = "qemux86"
DISTRO            = "nodistro"
DISTRO_VERSION    = "nodistro.0"
TUNE_FEATURES     = "m32 i586"
TARGET_FPU        = ""
meta              = "HEAD:ac5c08ea00816852a712a8d7025bce7e865460ec"
meta-oe
meta-python
meta-multimedia   = "master:1dff2351aa6cdafa5a501e8956cb853ab17ed9ae"
meta-qt4          = "master:2c7f8df9039be498f8a2232d1428adb7f4e5e800"
meta-intel-realsense = "master:1d50cb5b82926f8e69a78999e9e8d2ffe2169897"
meta-ros          = "rojkov-moveit-v2:cde0c0df72426a8f9f82caf6fc822093cd504949"

Build Configuration:
BB_VERSION        = "1.33.2"
BUILD_SYS         = "x86_64-linux"
NATIVELSBSTRING   = "ubuntu-14.04"
TARGET_SYS        = "i586-oe-linux"
MACHINE           = "qemux86"
DISTRO            = "nodistro"
DISTRO_VERSION    = "nodistro.0"
TUNE_FEATURES     = "m32 i586"
TARGET_FPU        = ""
meta              = "HEAD:ac5c08ea00816852a712a8d7025bce7e865460ec"
meta-oe
meta-multimedia
meta-python       = "master:6c584374a599f6f8d3607f20ecfc13a67ccf1da1"
meta-ros          = "rojkov-moveit-v2:cde0c0df72426a8f9f82caf6fc822093cd504949"


Multiple worker classes dont work

regarding https://github.com/davydovanton/sidekiq-history/issues/30
this is a test that should pass, if multiple workers were supported.

This pull request was just to illustrate how the current design doesn't support the features necessary. I know you can't merge in any changes directly b/c GSoC, but I wanted to see if I could help you publish a stable version of your project regardless. I'll just use my fork for now, since I have a feature that I need to implement, and I'll check back in on this in a few months.
Good luck!

Thanks!
@nessur could you create issue for each feature needful for you in my repository too? Or create list with all features in separate issue?

The linked issue here: https://github.com/davydovanton/sidekiq-history/issues/30 ,  logs the only remaining problem I'm having, where sidekiq-history middleware causes a worker to enter the failed/retry state when it raises a runtime error. In your test suite, you only use one worker class at a time, so you've never seen this behavior. If you try recording statistics on two worker classes, you'll see this immediately.