//
// A TensorHandle is management class around a Tensor which may track additional
// metadata and synchronization.
//
// This allows us to hide concrete implementations of TensorHandle from header
// files. The interface lists the common functionality that must be provided by
// any concrete implementation. However, in cases where the true concrete class
// is needed a static_cast can be applied.

)

filegroup(
    name = "device_headers",
    srcs = ["parallel_device.h"],
)

filegroup(
    name = "device_sources",
    srcs = ["parallel_device.cc"],
)

filegroup(
    name = "headers",
    srcs = [
        ":device_headers",
        ":lib_headers",
    ],
    visibility = ["//tensorflow/python:__pkg__"],
)

filegroup(
    name = "sources",
    srcs = [
        ":device_sources",

  EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteStatus(status);
}
BENCHMARK(BM_ExecuteFunction)->Arg(0)->Arg(1);

TEST(CAPI, Variables) {
  // Variables use resource handles, so this is really a test for resource
  // tensor handling.
  TF_Status* status = TF_NewStatus();
  TFE_ContextOptions* opts = TFE_NewContextOptions();
  TFE_Context* ctx = TFE_NewContext(opts, status);

// "Context" under which operations/functions are executed. It encapsulates
// things like the available devices, resource manager etc.
// TFE_Context must outlive all tensor handles created using it. In other
// words, TFE_DeleteContext() must be called after all tensor handles have
// been deleted (with TFE_DeleteTensorHandle).
//
// TODO(ashankar): Merge with TF_Session?
typedef struct TFE_Context TFE_Context;

// have any visibility into the graph/eager implementation and is only providing
// C bindings to the abstract classes defined in the
// c_api_unified_experimental_internal.h header.
//
// =============================================================================

using tensorflow::AbstractFunction;
using tensorflow::AbstractTensorHandle;
using tensorflow::DataType;

    srcs = [
        "safe_ptr.cc",
        "//tensorflow/c/eager:headers",
    ],
    hdrs = ["safe_ptr.h"],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [
        ":c_api_internal",
    ],
)

# -----------------------------------------------------------------------------
# Public targets

filegroup(
    name = "headers",
    srcs = [
        "c_api.h",
        "c_api_experimental.h",

// Create a packed TensorHandle with the given list of TensorHandles.
// If `handles` are on the same device, assign the same device to the packed
// handle; if `handles` are on different deivces, assign a CompositeDevice to
// it.
TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_CreatePackedTensorHandle(
    TFE_Context* ctx, TFE_TensorHandle** handles, int* num_handles,
    TF_Status* status);

  # set up symlink for devtoolset-10
  rm -f /dt10/usr/include/aarch64-linux-gnu/$f
  ln -s /usr/include/aarch64-linux-gnu/$f /dt10/usr/include/aarch64-linux-gnu/$f
done
popd

# Python 3.10 include headers fix:
# sysconfig.get_path('include') incorrectly points to /usr/local/include/python
# map /usr/include/python3.10 to /usr/local/include/python3.10
if [[ ! -f "/usr/local/include/$VERSION" ]]; then

NUMPY_ANNOTATIONS = {
    "numpy": package_annotation(
        additive_build_content = """\
filegroup(
    name = "includes",
    srcs = glob(["site-packages/numpy/core/include/**/*.h"]),
)
cc_library(
    name = "numpy_headers",
    hdrs = [":includes"],
    strip_include_prefix="site-packages/numpy/core/include/",
)
""",
    ),
}

pip_parse(
    name = "pypi",
    annotations = NUMPY_ANNOTATIONS,

  echo "Use 'devtoolset-9' to build a manylinux2014 compatible toolchain"
  exit 1
  ;;
esac

mkdir -p "${TARGET}"

mkdir -p ${TARGET}/usr/include

# Put the current kernel headers from ubuntu in place.
ln -s "/usr/include/linux" "${TARGET}/usr/include/linux"
ln -s "/usr/include/asm-generic" "${TARGET}/usr/include/asm-generic"
ln -s "/usr/include/aarch64-linux-gnu/asm" "${TARGET}/usr/include/asm"