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* Upgrade presets for DNNL 3.7.3, CPython 3.13.3, NumPy 2.2.5, LLVM 20.1.3, libffi 3.4.8, ONNX Runtime 1.21.1
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‎CHANGELOG.md

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* Allow setting "org.bytedeco.openblas.load" system property to "none" ([issue #1203](https://github.com/bytedeco/javacpp-presets/issues/1203))
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* Fix presets for the CUPTI module of CUDA on Windows ([pull #1576](https://github.com/bytedeco/javacpp-presets/pull/1576))
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* Introduce `macosx-arm64` builds for ARPACK-NG, CMINPACK, FFTW, GSL, TensorFlow Lite, ONNX, ONNX Runtime ([issue #1069](https://github.com/bytedeco/javacpp-presets/issues/1069))
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* Upgrade presets for OpenCV 4.11.0, FFmpeg 7.1.1, DNNL 3.7.1, OpenBLAS 0.3.29, CPython 3.13.2, NumPy 2.2.3, SciPy 1.15.2, LLVM 20.1.0, libffi 3.4.7, PyTorch 2.6.0, TensorFlow Lite 2.19.0, ONNX Runtime 1.21.0, and their dependencies
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* Upgrade presets for OpenCV 4.11.0, FFmpeg 7.1.1, DNNL 3.7.3, OpenBLAS 0.3.29, CPython 3.13.3, NumPy 2.2.5, SciPy 1.15.2, LLVM 20.1.3, libffi 3.4.8, PyTorch 2.6.0, TensorFlow Lite 2.19.0, ONNX Runtime 1.21.1, and their dependencies
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### November 16, 2024 version 1.5.11
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* Enable distributed package using Gloo in presets for PyTorch ([pull #1510](https://github.com/bytedeco/javacpp-presets/pull/1510))

‎cpython/README.md

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------------
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This directory contains the JavaCPP Presets module for:
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* CPython 3.13.2 https://www.python.org/
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* CPython 3.13.3 https://www.python.org/
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Please refer to the parent README.md file for more detailed information about the JavaCPP Presets.
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<dependency>
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<groupId>org.bytedeco</groupId>
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<artifactId>cpython-platform</artifactId>
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<version>3.13.2-1.5.12-SNAPSHOT</version>
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<version>3.13.3-1.5.12-SNAPSHOT</version>
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</dependency>
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</dependencies>
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<build>

‎cpython/cppbuild.sh

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fi
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OPENSSL=openssl-3.4.1
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CPYTHON_VERSION=3.13.2
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CPYTHON_VERSION=3.13.3
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download https://www.openssl.org/source/$OPENSSL.tar.gz $OPENSSL.tar.gz
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download https://www.python.org/ftp/python/$CPYTHON_VERSION/Python-$CPYTHON_VERSION.tgz Python-$CPYTHON_VERSION.tgz
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‎cpython/platform/pom.xml

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<groupId>org.bytedeco</groupId>
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<artifactId>cpython-platform</artifactId>
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<version>3.13.2-${project.parent.version}</version>
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<version>3.13.3-${project.parent.version}</version>
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<name>JavaCPP Presets Platform for CPython</name>
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<properties>

‎cpython/pom.xml

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<groupId>org.bytedeco</groupId>
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<artifactId>cpython</artifactId>
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<version>3.13.2-${project.parent.version}</version>
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<version>3.13.3-${project.parent.version}</version>
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<name>JavaCPP Presets for CPython</name>
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<dependencies>

‎cpython/samples/pom.xml

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<dependency>
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<groupId>org.bytedeco</groupId>
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<artifactId>cpython-platform</artifactId>
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<version>3.13.2-1.5.12-SNAPSHOT</version>
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<version>3.13.3-1.5.12-SNAPSHOT</version>
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</dependency>
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</dependencies>
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<build>

‎cpython/src/gen/java/org/bytedeco/cpython/PyConfig.java

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public native int use_frozen_modules(); public native PyConfig use_frozen_modules(int setter);
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public native int safe_path(); public native PyConfig safe_path(int setter);
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public native int int_max_str_digits(); public native PyConfig int_max_str_digits(int setter);
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// #ifdef __APPLE__
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// #endif
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public native int cpu_count(); public native PyConfig cpu_count(int setter);
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// #ifdef Py_GIL_DISABLED

‎cpython/src/gen/java/org/bytedeco/cpython/_PyCoLineInstrumentationData.java

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/* Ancillary data structure used for instrumentation.
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Line instrumentation creates an array of
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these. One entry per code unit.*/
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Line instrumentation creates this with sufficient
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space for one entry per code unit. The total size
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of the data will be `bytes_per_entry * Py_SIZE(code)` */
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@Properties(inherit = org.bytedeco.cpython.presets.python.class)
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public class _PyCoLineInstrumentationData extends Pointer {
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static { Loader.load(); }
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return new _PyCoLineInstrumentationData((Pointer)this).offsetAddress(i);
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}
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public native @Cast("uint8_t") byte original_opcode(); public native _PyCoLineInstrumentationData original_opcode(byte setter);
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public native byte line_delta(); public native _PyCoLineInstrumentationData line_delta(byte setter);
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public native @Cast("uint8_t") byte bytes_per_entry(); public native _PyCoLineInstrumentationData bytes_per_entry(byte setter);
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public native @Cast("uint8_t") byte data(int i); public native _PyCoLineInstrumentationData data(int i, byte setter);
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@MemberGetter public native @Cast("uint8_t*") BytePointer data();
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}

‎cpython/src/gen/java/org/bytedeco/cpython/global/python.java

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/*--start constants--*/
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public static final int PY_MAJOR_VERSION = 3;
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public static final int PY_MINOR_VERSION = 13;
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public static final int PY_MICRO_VERSION = 2;
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public static final int PY_MICRO_VERSION = 3;
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public static final int PY_RELEASE_LEVEL = PY_RELEASE_LEVEL_FINAL;
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public static final int PY_RELEASE_SERIAL = 0;
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/* Version as a string */
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public static final String PY_VERSION = "3.13.2";
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public static final String PY_VERSION = "3.13.3";
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/*--end constants--*/
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/* Version as a single 4-byte hex number, e.g. 0x010502B2 == 1.5.2b2.

‎dnnl/README.md

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------------
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This directory contains the JavaCPP Presets module for:
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* DNNL 3.7.1 https://01.org/dnnl
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* DNNL 3.7.3 https://01.org/dnnl
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------------
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Here is a simple example of DNNL ported to Java from this C++ source file:
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* https://github.com/oneapi-src/oneDNN/blob/v3.7.1/examples/cnn_inference_int8.cpp
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* https://github.com/oneapi-src/oneDNN/blob/v3.7.3/examples/cnn_inference_int8.cpp
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We can use [Maven 3](http://maven.apache.org/) to download and install automatically all the class files as well as the native binaries. To run this sample code, after creating the `pom.xml` and `CpuCnnInferenceInt8.java` source files below, simply execute on the command line:
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```bash
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<version>3.7.1-1.5.12-SNAPSHOT</version>
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‎dnnl/cppbuild.sh

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‎dnnl/platform/pom.xml

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<version>3.7.1-${project.parent.version}</version>
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<version>3.7.3-${project.parent.version}</version>
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‎dnnl/pom.xml

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‎dnnl/samples/pom.xml

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‎dnnl/src/gen/java/org/bytedeco/dnnl/global/dnnl.java

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/** Patch version */
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public static final int DNNL_VERSION_PATCH = 3;
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‎ffmpeg/cppbuild.sh

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VPX_VERSION=1.15.0
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ALSA_VERSION=1.2.13
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VPX_VERSION=1.15.1
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ALSA_VERSION=1.2.14
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‎leptonica/cppbuild.sh

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LIBPNG=libpng-1.6.47 # warning: libpng16 doesn't work on CentOS 6 for some reason
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LIBWEBP=libwebp-1.5.0
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OPENJPEG_VERSION=2.5.3
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download https://sourceforge.net/projects/libpng/files/libpng16/1.6.47/$LIBPNG.tar.gz $LIBPNG.tar.gz
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‎libffi/README.md

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‎libffi/cppbuild.sh

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‎libffi/platform/pom.xml

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‎libffi/samples/pom.xml

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‎libffi/src/gen/java/org/bytedeco/libffi/global/ffi.java

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libffi 3.4.7
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libffi 3.4.8
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- Copyright (c) 2011, 2014, 2019, 2021, 2022, 2024 Anthony Green
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- Copyright (c) 1996-2003, 2007, 2008 Red Hat, Inc.
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‎llvm/README.md

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‎llvm/cppbuild.sh

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‎llvm/platform/pom.xml

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‎llvm/pom.xml

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‎llvm/samples/clang/pom.xml

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‎llvm/samples/llvm/pom.xml

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‎llvm/samples/polly/pom.xml

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‎numpy/README.md

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------------
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This directory contains the JavaCPP Presets module for:
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<!-- Additional dependencies to use bundled full version of MKL -->

‎numpy/cppbuild.sh

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@@ -7,7 +7,7 @@ if [[ -z "$PLATFORM" ]]; then
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exit
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fi
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NUMPY_VERSION=2.2.3
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NUMPY_VERSION=2.2.5
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download https://github.com/numpy/numpy/releases/download/v$NUMPY_VERSION/numpy-$NUMPY_VERSION.tar.gz numpy-$NUMPY_VERSION.tar.gz
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mkdir -p $PLATFORM

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