* Upgrade presets for CPython 3.12.7, NumPy 2.1.2, SciPy 1.14.1, ONN…

…X 1.17.0, ONNX Runtime 1.19.2

CHANGELOG.md

@@ -9,7 +9,7 @@
  * Build FFmpeg with zimg to enable zscale filter ([pull #1481](https://github.com/bytedeco/javacpp-presets/pull/1481))
  * Enable PulseAudio support for FFmpeg on Linux ([pull #1472](https://github.com/bytedeco/javacpp-presets/pull/1472))
  * Virtualize `btCollisionWorld`, `btOverlapFilterCallback`, `btOverlapCallback` from Bullet Physics SDK ([pull #1475](https://github.com/bytedeco/javacpp-presets/pull/1475))
- * Upgrade presets for OpenCV 4.10.0, FFmpeg 7.0.2, Spinnaker 4.0.0.116 ([pull #1524](https://github.com/bytedeco/javacpp-presets/pull/1524)), DNNL 3.5.3, OpenBLAS 0.3.28, CMINPACK 1.3.9, GSL 2.8, CPython 3.12.5, NumPy 2.0.1, SciPy 1.14.0, LLVM 18.1.8, LibRaw 0.21.2 ([pull #1520](https://github.com/bytedeco/javacpp-presets/pull/1520)), Tesseract 5.4.1, libffi 3.4.6, CUDA 12.6.0, cuDNN 9.3.0, NCCL 2.22.3, nvCOMP 4.0.0, OpenCL 3.0.16, NVIDIA Video Codec SDK 12.2.72, PyTorch 2.4.0 ([pull #1466](https://github.com/bytedeco/javacpp-presets/pull/1466)), SentencePiece 0.2.0, TensorFlow Lite 2.17.0, TensorRT 10.3.0.26, Triton Inference Server 2.48.0, ONNX 1.16.2, ONNX Runtime 1.18.1, TVM 0.17.0, and their dependencies
+ * Upgrade presets for OpenCV 4.10.0, FFmpeg 7.0.2, Spinnaker 4.0.0.116 ([pull #1524](https://github.com/bytedeco/javacpp-presets/pull/1524)), DNNL 3.5.3, OpenBLAS 0.3.28, CMINPACK 1.3.9, GSL 2.8, CPython 3.12.7, NumPy 2.1.2, SciPy 1.14.1, LLVM 18.1.8, LibRaw 0.21.2 ([pull #1520](https://github.com/bytedeco/javacpp-presets/pull/1520)), Tesseract 5.4.1, libffi 3.4.6, CUDA 12.6.0, cuDNN 9.3.0, NCCL 2.22.3, nvCOMP 4.0.0, OpenCL 3.0.16, NVIDIA Video Codec SDK 12.2.72, PyTorch 2.4.0 ([pull #1466](https://github.com/bytedeco/javacpp-presets/pull/1466)), SentencePiece 0.2.0, TensorFlow Lite 2.17.0, TensorRT 10.3.0.26, Triton Inference Server 2.48.0, ONNX 1.17.0, ONNX Runtime 1.19.2, TVM 0.17.0, and their dependencies
 
 ### January 29, 2024 version 1.5.10
  * Introduce `macosx-arm64` builds for PyTorch ([pull #1463](https://github.com/bytedeco/javacpp-presets/pull/1463))

README.md

@@ -134,7 +134,7 @@ Further, in the case of Android, the JavaCPP Presets also rely on:
 
 Manual Installation
 -------------------
-Simply put all the desired JAR files (`opencv*.jar`, `ffmpeg*.jar`, etc.), in addition to `javacpp.jar`, somewhere in your class path. The JAR files available as pre-built artifacts are meant to be used with [JavaCPP](https://github.com/bytedeco/javacpp). The binaries for Linux are built with Ubuntu, so they should work on most distributions currently in use. The ones for Android were compiled for ARMv7 processors featuring an FPU, so they will not work on ancient devices such as the HTC Magic or some others with an ARMv6 CPU. Here are some more specific instructions for common cases:
+Simply put all the desired JAR files (`opencv*.jar`, `ffmpeg*.jar`, etc.), in addition to `javacpp.jar`, somewhere in your class path. The JAR files available as pre-built artifacts are meant to be used with [JavaCPP](https://github.com/bytedeco/javacpp). The binaries for Linux are built with Ubuntu, so they should work on most distributions currently in use. Here are some more specific instructions for common cases:
 
 NetBeans (Java SE 7 or newer):
 
@@ -205,7 +205,7 @@ Each child module in turn relies by default on the included [`cppbuild.sh` scrip
  * FFTW 3.3.10  http://www.fftw.org/download.html
  * GSL 2.8  http://www.gnu.org/software/gsl/#downloading
  * CPython 3.12.x  https://www.python.org/downloads/
- * NumPy 2.0.x  https://github.com/numpy/numpy
+ * NumPy 2.1.x  https://github.com/numpy/numpy
  * SciPy 1.14.x  https://github.com/scipy/scipy
  * Gym 0.26.x  https://github.com/openai/gym
  * LLVM 18.1.x  http://llvm.org/releases/download.html
@@ -231,9 +231,9 @@ Each child module in turn relies by default on the included [`cppbuild.sh` scrip
  * Triton Inference Server 2.48.x  https://developer.nvidia.com/nvidia-triton-inference-server
  * The Arcade Learning Environment 0.8.x  https://github.com/mgbellemare/Arcade-Learning-Environment
  * DepthAI 2.24.x  https://github.com/luxonis/depthai-core
- * ONNX 1.16.x  https://github.com/onnx/onnx
+ * ONNX 1.17.x  https://github.com/onnx/onnx
  * nGraph 0.26.0  https://github.com/NervanaSystems/ngraph
- * ONNX Runtime 1.18.x  https://github.com/microsoft/onnxruntime
+ * ONNX Runtime 1.19.x  https://github.com/microsoft/onnxruntime
  * TVM 0.17.x  https://github.com/apache/tvm
  * Bullet Physics SDK 3.25  https://pybullet.org
  * LiquidFun  http://google.github.io/liquidfun/

cpython/README.md

@@ -14,7 +14,7 @@ Introduction
 ------------
 This directory contains the JavaCPP Presets module for:
 
- * CPython 3.12.5  https://www.python.org/
+ * CPython 3.12.7  https://www.python.org/
 
 Please refer to the parent README.md file for more detailed information about the JavaCPP Presets.
 
@@ -54,7 +54,7 @@ We can use [Maven 3](http://maven.apache.org/) to download and install automatic
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>cpython-platform</artifactId>
-            <version>3.12.5-1.5.11-SNAPSHOT</version>
+            <version>3.12.7-1.5.11-SNAPSHOT</version>
         </dependency>
     </dependencies>
     <build>

cpython/cppbuild.sh

@@ -7,8 +7,8 @@ if [[ -z "$PLATFORM" ]]; then
     exit
 fi
 
-OPENSSL=openssl-3.3.1
-CPYTHON_VERSION=3.12.5
+OPENSSL=openssl-3.3.2
+CPYTHON_VERSION=3.12.7
 download https://www.openssl.org/source/$OPENSSL.tar.gz $OPENSSL.tar.gz
 download https://www.python.org/ftp/python/$CPYTHON_VERSION/Python-$CPYTHON_VERSION.tgz Python-$CPYTHON_VERSION.tgz
 

cpython/platform/pom.xml

@@ -12,7 +12,7 @@
 
   <groupId>org.bytedeco</groupId>
   <artifactId>cpython-platform</artifactId>
-  <version>3.12.5-${project.parent.version}</version>
+  <version>3.12.7-${project.parent.version}</version>
   <name>JavaCPP Presets Platform for CPython</name>
 
   <properties>

cpython/pom.xml

@@ -11,7 +11,7 @@
 
   <groupId>org.bytedeco</groupId>
   <artifactId>cpython</artifactId>
-  <version>3.12.5-${project.parent.version}</version>
+  <version>3.12.7-${project.parent.version}</version>
   <name>JavaCPP Presets for CPython</name>
 
   <dependencies>

cpython/samples/pom.xml

@@ -12,7 +12,7 @@
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>cpython-platform</artifactId>
-            <version>3.12.5-1.5.11-SNAPSHOT</version>
+            <version>3.12.7-1.5.11-SNAPSHOT</version>
         </dependency>
     </dependencies>
     <build>

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

@@ -144,12 +144,12 @@ There is also (independent) API version information in modsupport.h.
 /*--start constants--*/
 public static final int PY_MAJOR_VERSION =        3;
 public static final int PY_MINOR_VERSION =        12;
-public static final int PY_MICRO_VERSION =        5;
+public static final int PY_MICRO_VERSION =        7;
 public static final int PY_RELEASE_LEVEL =        PY_RELEASE_LEVEL_FINAL;
 public static final int PY_RELEASE_SERIAL =       0;
 
 /* Version as a string */
-public static final String PY_VERSION =              "3.12.5";
+public static final String PY_VERSION =              "3.12.7";
 /*--end constants--*/
 
 /* Version as a single 4-byte hex number, e.g. 0x010502B2 == 1.5.2b2.

numpy/README.md

@@ -9,7 +9,7 @@ Introduction
 ------------
 This directory contains the JavaCPP Presets module for:
 
- * NumPy 2.0.1  http://www.numpy.org/
+ * NumPy 2.1.2  http://www.numpy.org/
 
 Please refer to the parent README.md file for more detailed information about the JavaCPP Presets.
 
@@ -48,7 +48,7 @@ We can use [Maven 3](http://maven.apache.org/) to download and install automatic
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>numpy-platform</artifactId>
-            <version>2.0.1-1.5.11-SNAPSHOT</version>
+            <version>2.1.2-1.5.11-SNAPSHOT</version>
         </dependency>
 
         <!-- Additional dependencies to use bundled full version of MKL -->

numpy/cppbuild.sh

@@ -7,7 +7,7 @@ if [[ -z "$PLATFORM" ]]; then
     exit
 fi
 
-NUMPY_VERSION=2.0.1
+NUMPY_VERSION=2.1.2
 download https://github.com/numpy/numpy/releases/download/v$NUMPY_VERSION/numpy-$NUMPY_VERSION.tar.gz numpy-$NUMPY_VERSION.tar.gz
 
 mkdir -p $PLATFORM

numpy/platform/pom.xml

@@ -12,7 +12,7 @@
 
   <groupId>org.bytedeco</groupId>
   <artifactId>numpy-platform</artifactId>
-  <version>2.0.1-${project.parent.version}</version>
+  <version>2.1.2-${project.parent.version}</version>
   <name>JavaCPP Presets Platform for NumPy</name>
 
   <properties>
@@ -28,7 +28,7 @@
     <dependency>
       <groupId>org.bytedeco</groupId>
       <artifactId>cpython-platform</artifactId>
-      <version>3.12.5-${project.parent.version}</version>
+      <version>3.12.7-${project.parent.version}</version>
     </dependency>
     <dependency>
       <groupId>${project.groupId}</groupId>

numpy/pom.xml

@@ -11,7 +11,7 @@
 
   <groupId>org.bytedeco</groupId>
   <artifactId>numpy</artifactId>
-  <version>2.0.1-${project.parent.version}</version>
+  <version>2.1.2-${project.parent.version}</version>
   <name>JavaCPP Presets for NumPy</name>
 
   <dependencies>
@@ -23,7 +23,7 @@
     <dependency>
       <groupId>org.bytedeco</groupId>
       <artifactId>cpython</artifactId>
-      <version>3.12.5-${project.parent.version}</version>
+      <version>3.12.7-${project.parent.version}</version>
     </dependency>
     <dependency>
       <groupId>org.bytedeco</groupId>
@@ -51,7 +51,7 @@
           <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>cpython</artifactId>
-            <version>3.12.5-${project.parent.version}</version>
+            <version>3.12.7-${project.parent.version}</version>
           </dependency>
           <dependency>
             <groupId>org.bytedeco</groupId>
@@ -62,7 +62,7 @@
           <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>cpython</artifactId>
-            <version>3.12.5-${project.parent.version}</version>
+            <version>3.12.7-${project.parent.version}</version>
             <classifier>${javacpp.platform}</classifier>
           </dependency>
         </dependencies>
@@ -171,7 +171,7 @@
               <dependency>
                 <groupId>org.bytedeco</groupId>
                 <artifactId>cpython</artifactId>
-                <version>3.12.5-${project.parent.version}</version>
+                <version>3.12.7-${project.parent.version}</version>
                 <classifier>${os.name}-${os.arch}</classifier>
               </dependency>
             </dependencies>

numpy/samples/pom.xml

@@ -12,7 +12,7 @@
         <dependency>
             <groupId>org.bytedeco</groupId>
             <artifactId>numpy-platform</artifactId>
-            <version>2.0.1-1.5.11-SNAPSHOT</version>
+            <version>2.1.2-1.5.11-SNAPSHOT</version>
         </dependency>
 
         <!-- Additional dependencies to use bundled full version of MKL -->

numpy/src/gen/java/org/bytedeco/numpy/PyArrayMultiIterObject.java

@@ -58,7 +58,7 @@ public class PyArrayMultiIterObject extends Pointer {
         @MemberGetter public native @Cast("PyArrayIterObject**") PointerPointer iters();
 // #elif defined(__cplusplus)
         /*
-         * C++ doesn't stricly support flexible members and gives compilers
+         * C++ doesn't strictly support flexible members and gives compilers
          * warnings (pedantic only), so we lie.  We can't make it 64 because
          * then Cython is unhappy (larger struct at runtime is OK smaller not).
          */

numpy/src/gen/java/org/bytedeco/numpy/PyUFuncObject.java

@@ -15,7 +15,6 @@
 import static org.bytedeco.numpy.global.numpy.*;
 
 
-
 @Properties(inherit = org.bytedeco.numpy.presets.numpy.class)
 public class PyUFuncObject extends Pointer {
     static { Loader.load(); }
@@ -160,4 +159,10 @@ public class PyUFuncObject extends Pointer {
         /* A PyListObject of `(tuple of DTypes, ArrayMethod/Promoter)` */
         public native PyObject _loops(); public native PyUFuncObject _loops(PyObject setter);
 //     #endif
+//     #if NPY_FEATURE_VERSION >= NPY_2_1_API_VERSION
+        /*
+         * Optional function to process core dimensions of a gufunc.
+         */
+        public native PyUFunc_ProcessCoreDimsFunc process_core_dims_func(); public native PyUFuncObject process_core_dims_func(PyUFunc_ProcessCoreDimsFunc setter);
+//     #endif
 }

numpy/src/gen/java/org/bytedeco/numpy/PyUFunc_ProcessCoreDimsFunc.java

@@ -0,0 +1,58 @@
+// Targeted by JavaCPP version 1.5.11-SNAPSHOT: DO NOT EDIT THIS FILE
+
+package org.bytedeco.numpy;
+
+import java.nio.*;
+import org.bytedeco.javacpp.*;
+import org.bytedeco.javacpp.annotation.*;
+
+import static org.bytedeco.javacpp.presets.javacpp.*;
+import static org.bytedeco.openblas.global.openblas_nolapack.*;
+import static org.bytedeco.openblas.global.openblas.*;
+import org.bytedeco.cpython.*;
+import static org.bytedeco.cpython.global.python.*;
+
+import static org.bytedeco.numpy.global.numpy.*;
+
+
+/*
+ * This is the signature for the functions that may be assigned to the
+ * `process_core_dims_func` field of the PyUFuncObject structure.
+ * Implementation of this function is optional.  This function is only used
+ * by generalized ufuncs (i.e. those with the field `core_enabled` set to 1).
+ * The function is called by the ufunc during the processing of the arguments
+ * of a call of the ufunc. The function can check the core dimensions of the
+ * input and output arrays and return -1 with an exception set if any
+ * requirements are not satisfied. If the caller of the ufunc didn't provide
+ * output arrays, the core dimensions associated with the output arrays (i.e.
+ * those that are not also used in input arrays) will have the value -1 in
+ * `core_dim_sizes`.  This function can replace any output core dimensions
+ * that are -1 with a value that is appropriate for the ufunc.
+ *
+ * Parameter       Description
+ * --------------- ------------------------------------------------------
+ * ufunc           The ufunc object
+ * core_dim_sizes  An array with length `ufunc->core_num_dim_ix`.
+ *                 The core dimensions of the arrays passed to the ufunc
+ *                 will have been set.  If the caller of the ufunc didn't
+ *                 provide the output array(s), the output-only core
+ *                 dimensions will have the value -1.
+ *
+ * The function must not change any element in `core_dim_sizes` that is
+ * not -1 on input. Doing so will result in incorrect output from the
+ * ufunc, and could result in a crash of the Python interpreter.
+ *
+ * The function must return 0 on success, -1 on failure (with an exception
+ * set).
+ */
+@Properties(inherit = org.bytedeco.numpy.presets.numpy.class)
+public class PyUFunc_ProcessCoreDimsFunc extends FunctionPointer {
+    static { Loader.load(); }
+    /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */
+    public    PyUFunc_ProcessCoreDimsFunc(Pointer p) { super(p); }
+    protected PyUFunc_ProcessCoreDimsFunc() { allocate(); }
+    private native void allocate();
+    public native int call(
+                                PyUFuncObject ufunc,
+                                @Cast("npy_intp*") SizeTPointer core_dim_sizes);
+}