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youtube-dl -v  https://www.reddit.com/r/watchpeoplesurvive/comments/k3aoyn/f1_driver_romain_grosjean_coming_out_out_of_the   -o - > i.mp4
[debug] System config: []
[debug] User config: []
[debug] Custom config: []
[debug] Command-line args: [u'-v', u'https://www.reddit.com/r/watchpeoplesurvive/comments/k3aoyn/f1_driver_romain_grosjean_coming_out_out_of_the', u'-o', u'-']
[debug] Encodings: locale UTF-8, fs UTF-8, out None, pref UTF-8
[debug] youtube-dl version 2020.11.29
[debug] Python version 2.7.18 (CPython) - Linux-5.4.0-52-generic-x86_64-with-LinuxMint-20-ulyana
[debug] exe versions: ffmpeg 4.2.4, ffprobe 4.2.4, rtmpdump 2.4
[debug] Proxy map: {}
[RedditR] k3aoyn: Downloading JSON metadata
[Reddit] onbvmhlc17261: Downloading m3u8 information
[Reddit] onbvmhlc17261: Downloading MPD manifest
[debug] Default format spec: best/bestvideo+bestaudio
[debug] Invoking downloader on u'https://v.redd.it/onbvmhlc17261/DASH_720.mp4'
[download] Destination: -.fdash-video_2329911
[download] 100% of 3.48MiB in 00:05
[debug] Invoking downloader on u'https://v.redd.it/onbvmhlc17261/DASH_audio.mp4'
[download] Destination: -.fdash-audio_0_132011
[download] 100% of 202.46KiB in 00:01
[ffmpeg] Merging formats into "-.mp4"
[debug] ffmpeg command line: ffmpeg -y -loglevel 'repeat+info' -i 'file:-.fdash-video_2329911' -i 'file:-.fdash-audio_0_132011' -c copy -map '0:v:0' -map '1:a:0' 'file:-.temp.mp4'
Deleting original file -.fdash-video_2329911 (pass -k to keep)
Deleting original file -.fdash-audio_0_132011 (pass -k to keep)

Description

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-o - doesn't redirect to stdout for reddit but creates a file instead (-.mp4)

I think the application is counting emoji in text. You would do it by calling graphemes and then calling isemoji on each grapheme. It seems like this might be better suited to a package which can evolve its API as necessary.

Quantized not yet supported for op : 'EXP' Quantized not yet supported for op : 'EXP' Quantized not yet supported for op : 'EXP'

I took a look at the code for Conv1DTranspose, and it doesn't currently support RaggedTensors -- i.e., the current implementation assumes that the inputs are dense. So I guess the feature request here is to have Conv1DTranspose (and possibly some other keras layers) be updated to support ragged tensors.

Also, in the second code example you posted, you need to specify ragged=True when creating the keras.Inputs. I.e.: input_1 = keras.Input(shape=(None,5), ragged=True). But that just gives you an error further along (which indicates that Conv1DTranspose doesn't support RaggedTensors).

Thank you for pointing out the error in my code! I'm not sure if TensorFlow has any other ways of handling time-series data with non-uniform shape, but if RaggedTensor is the only way, then you are correct that my feature request would be to have more layers be updated to support RaggedTensor

Also, as a way around, if I replace my Conv1DTranspose with other layers that support RaggedTensor, then is it fine to apply Conv1DTranspose afterwards? Or is it just not possible to use Conv1DTranspose anywhere in my model as long as the shape of my data is still non-uniform?

Lastly, do you happen to know what are the layer APIs that support RaggedTensor as input?

Hi @WiestDaessle, to clarify, the training works okay when you run the tutorial? But it is hanging when you make these modifications? Is that correct? Can you please provide the exact code you are running into issues with so I can reproduce it on my end. You can provide it in a colab notebook, or just paste it a comment, but the full code (properly indented and with import statements) greatly helps us to troubleshoot. Also please clarify what you mean by 'the oversize'.

Note that if you are using MirroredStrategy and model.fit then you should not need to use strategy.experimental_distribute_dataset. Distributing the dataset this way is only required when using a custom training loop.

expected is now false

no need to type expected=False, it's the default.

Oh I see what you mean about --dump-pages, it will print the API response. I guess that could mean I can remove the JSON from the string as well.

Exactly.

@timholy: Still, you didn't address the first-order point: do we want to include weaknesses?

@StefanKarpinski: I do think we should list pros and cons of Julia in a straightforward manner and this seems like a reasonable place to do it.

So yes, I think we should.

@Volker-Weissmann: "I think that most C++ libraries are poorly tested". Are you serious? The whole world runs on C++.

Deadly. I've used a lot of C and C++ libraries in my life and most of them are only tested by letting people use them and report bugs. That's not what I would consider "well tested". Some C/C++ libraries have great test suites, but it is far from the norm.

@Volker-Weissmann: Julia is a niche programming language, that niche being scientific computing.

Well, that's like your opinion, man. Julia is a general purpose programming language and always has been. In any case, we don't need to agree on this, but we're not putting in the Julia manual that Julia is less mature than other languages just because it's more recent.

Anyway, consider e.g. NewvarNode. Using ? NewvarNode, you get

This is a purely internal non-exported type. This is like complaining Python doesn't document the _PyIO_get_module_state function.

combinatorics statprofilerhtml DifferentialEquations

Please do open issues on these packages about any issues with their documentation. In any case, pretending that this is a norm in the ecosystem and such does not help convince package maintainers to improve things, so we will not be including anything to that effect here.

Would it be possible to run Documenter.jl on every package on https://juliapackages.com/p/packagename and host it on https://juliapackages.com/p/packagename ? That would help a lot and this is what Rust does.

See JuliaHub: https://juliahub.com/docs/DifferentialEquations/UQdwS/6.15.0/. All registered Julia packages have docs there or, if they have opted out, are linked to from there and can be searched.

That's one beafy PC that you have.

No, it's a two-year-old MacBook Pro that wasn't even the fastest CPU when I bought it. I am, however, running Julia master, which is already considerably faster than Julia 1.5.

I've tried make Java "see the light" by using some global command-line options too:
<code>set "JAVA_TOOL_OPTIONS=-Dfile.encoding=UTF8 -Duser.language=en"</code>
that explicit the input files encoding, but without luck.

I.D.K. if you've pushed a fix yet,
but it still happens in even in a recent (November 30, 2020) build <a href="https://github.com/iBotPeaches/Apktool/commit/d63088db9f0ed14d6a069afe5dcf057db27884b5"><code>d63088</code></a> from master.

<pre> verbose ? [N] assets ? [Y] res ? [Y] smalli source ? [Y]

[INFO] java: D:\Software\Java\8\x64\jdk1.8.0_151\bin\java.exe [INFO] java arc.: -d64 [INFO] java opts: -Dfile.encoding=UTF8 -Duser.language=en -Xmn512m -Xms512m -Xmx2048m -Xverify:none -XX:+UseParallelGC -XX:ParallelGCThreads=2 -Dfile.encoding=UTF8 -Duser.language=en

[INFO] ApkTool.jar D:/DOS/android/bin/apktool.jar [INFO] extract-target: D:/DOS/android/כלכליסט_v5.0.49_apkpure.com.apk [INFO] additional arg.s:

"D:\Software\Java\8\x64\jdk1.8.0_151\bin\java.exe" -d64 -jar "D:/DOS/android/bin/apktool.jar" --force --output "project_כלכליסט_v5.0.49_apkpure.com" decode "D:/DOS/android/כלכליסט_v5.0.49_apkpure.com.apk"

Picked up JAVA_TOOL_OPTIONS: -Dfile.encoding=UTF8 -Duser.language=en -Xmn512m -Xms512m -Xmx2048m -Xverify:none -XX:+UseParallelGC -XX:ParallelGCThreads=2 -Dfile.encoding=UTF8 -Duser.language=en I: Using Apktool 2.4.2-d63088-SNAPSHOT on כלכליסט_v5.0.49_apkpure.com.apk m.apk I: Loading resource table... Exception in thread "main" java.lang.StringIndexOutOfBoundsException: String index out of range: 24 at java.lang.String.substring(String.java:1963) at brut.androlib.res.decoder.StringBlock.getHTML(StringBlock.java:160) at brut.androlib.res.decoder.ARSCDecoder.readValue(ARSCDecoder.java:360) at brut.androlib.res.decoder.ARSCDecoder.readEntryData(ARSCDecoder.java:276) at brut.androlib.res.decoder.ARSCDecoder.readTableType(ARSCDecoder.java:252) at brut.androlib.res.decoder.ARSCDecoder.readTableTypeSpec(ARSCDecoder.java:175) at brut.androlib.res.decoder.ARSCDecoder.readTablePackage(ARSCDecoder.java:131) at brut.androlib.res.decoder.ARSCDecoder.readTableHeader(ARSCDecoder.java:82) at brut.androlib.res.decoder.ARSCDecoder.decode(ARSCDecoder.java:48) at brut.androlib.res.AndrolibResources.getResPackagesFromApk(AndrolibResources.java:790)

    at brut.androlib.res.AndrolibResources.loadMainPkg(AndrolibResources.java:67)
    at brut.androlib.res.AndrolibResources.getResTable(AndrolibResources.java:59)
    at brut.androlib.Androlib.getResTable(Androlib.java:66)
    at brut.androlib.ApkDecoder.setTargetSdkVersion(ApkDecoder.java:236)
    at brut.androlib.ApkDecoder.decode(ApkDecoder.java:118)
    at brut.apktool.Main.cmdDecode(Main.java:179)
    at brut.apktool.Main.main(Main.java:82)

</pre>

Perhaps modifying the following code to your usage can help with normalizing things for you with native functions?
https://github.com/eladkarako/Overkill-UTF-8-Support-For-Windows-And-Java-With-Full-Code-And-Usage-Example/blob/master/input2stdout.java#L15

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you.

That's GPUCompiler-specific. and doesn't matter for regular precompilation files.

print_exit_hint(repl::AbstractREPL, line) = print_exit_hint(outstream(repl), line)

Closes #8430

Description of the new feature/enhancement

I took me a while to understand why custom commands I add appear at the bottom. The reason is that when sorting commands in command palette we do it not lexicographically and as a result consider a case.

IMHO the case should not matter in the palette (or at least case-insensitive sorting should be optionally available).

Proposed technical implementation details (optional)

Upon comparing the filtered commands, try to set both command names to lower case and then compare lexicographically.

Fixes #9826.

A quick benchmark:

julia> X = [big(rand(1:10000)) // big(rand(1:10000)) for i = 1:100, j = 1:100];

julia> @time X*X; # before
 12.525924 seconds (45.29 M allocations: 1.267 GiB, 8.44% gc time)

julia> @time X*X; # after
  2.343117 seconds (18.95 M allocations: 733.016 MiB, 18.66% gc time, 17.26% compilation time)

As discussed on julia-dev, there is some performance advantage to using the GMP mpq functions for Rational{BigInt} operations.

The easiest way to do this would be:

• Make BigInt an immutable type. (It currently has immutable semantics anyway.) This way, Rational{BigInt} would be binary compatible with GMP's mpq_t (a pointer to an __mpq_struct consisting of the numerator __mpz_struct followed by the denominator), since our BigInt type is already a mirror of __mpz_struct.
• Define specialized + etc. methods for Rational{BigInt} that call GMP mpq functions.

I get the impression that the main reason that BigInt is a type and not immutable is that this makes it easier to pass by reference to GMP functions. So changing this to immutable would benefit greatly from a better way to pass ccall "out" arguments by reference, as discussed in #2322.

Alternatively, if you want to leave BigInt as-is, then one needs an easy way to convert BigInt to/from an immutable version thereof, and this requires us to add an additional "internal" constructor function BigInt(alloc::Cint, size::Cint, d::Ptr{Limb}).

This version already vectorizes under certain conditions. Also, LoopVectorization provides a macro which makes this even more robust.

Thanks for your reply. I waste 2 hours and finally realized that SIMD instructions gain no performance here.

MUMPS might be a good candidate for a new linear solver option in mplinsol. Benchmarking would be required, but its support for parallelism may make it a good alternative to PARDISO when solving large systems, but without the license restrictions.

It includes a MATLAB/Octave interface. While this doesn't work with MPI-based distributed memory parallelism, it does support shared memory, multithreaded parallelism through OpenMP and multithreaded BLAS implementations.

Moved to MATPOWER/mips#2, since that's the master repo for mplinsolve().

Pull request overview

• Moves DataConvergParams, DataConversions, DataDaylighting, DataDaylightingDevices, and DaylightingManager to state

Pull Request Author

Add to this list or remove from it as applicable. This is a simple templated set of guidelines.

• [ ] Title of PR should be user-synopsis style (clearly understandable in a standalone changelog context)
• [ ] Label the PR with at least one of: Defect, Refactoring, NewFeature, Performance, and/or DoNoPublish
• [ ] Pull requests that impact EnergyPlus code must also include unit tests to cover enhancement or defect repair
• [ ] Author should provide a "walkthrough" of relevant code changes using a GitHub code review comment process
• [ ] If any diffs are expected, author must demonstrate they are justified using plots and descriptions
• [ ] If changes fix a defect, the fix should be demonstrated in plots and descriptions
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This will not be exhaustively relevant to every PR.

• [ ] Perform a Code Review on GitHub
• [ ] If branch is behind develop, merge develop and build locally to check for side effects of the merge
• [ ] If defect, verify by running develop branch and reproducing defect, then running PR and reproducing fix
• [ ] If feature, test running new feature, try creative ways to break it
• [ ] CI status: all green or justified
• [ ] Check that performance is not impacted (CI Linux results include performance check)
• [ ] Run Unit Test(s) locally
• [ ] Check any new function arguments for performance impacts
• [ ] Verify IDF naming conventions and styles, memos and notes and defaults
• [ ] If new idf included, locally check the err file and other outputs

CI is OK here. Merging.

I've got a couple questions and spelling nits. I've checked out the branch and played with it and it feels fine to me, so I'm okay with this. I just want to make sure the TODOs shouldn't be "TODONEs"

@zadjii-msft - thanks for the review! And sorry for all misspellings (I guess it is an hour of the day.. or of the night.. that I start to err the way that even spellchecker cannot stop me). I am not sure if we need to implement TODOs now.. as both are somewhat hypothetical.. I can absolutely work on them if you find it is a correct timing :blush:

linuxaarch64 is the usual unrelated NaN == Inf error that we've been seeing recently on CI, so this should be good to merge once the other tests are green.