"//tensorflow/core:test_main",
        "//tensorflow/core/kernels:ops_testutil",
        "@com_google_absl//absl/container:inlined_vector",
        "@com_google_absl//absl/strings:str_format",
        "@eigen_archive//:eigen3",
    ],
)

tf_cc_test(
    name = "ops_test",
    size = "medium",
    srcs = ["ops_test.cc"],
    linkopts = select({
        "//conditions:default": [],
    }),

  virtual AbstractTensorInterface* CreateFloatScalar(float value) = 0;
  virtual AbstractTensorInterface* CreateDoubleScalar(double value) = 0;
  virtual AbstractTensorInterface* CreateHalfScalar(Eigen::half value) = 0;
  virtual AbstractTensorInterface* CreateStringScalar(tstring value) = 0;
  virtual AbstractTensorInterface* CreateComplex128Scalar(complex128 value) = 0;

)

config_setting(
    name = "using_rocm_hipcc",
    define_values = {"using_rocm_hipcc": "true"},
)

config_setting(
    name = "override_eigen_strong_inline",
    define_values = {"override_eigen_strong_inline": "true"},
    visibility = ["//visibility:public"],
)

# This flag specifies whether TensorFlow 2.0 API should be built instead

    *   This does not change the minimum required version for building
        TensorFlow from source on Windows, but builds enabling
        `EIGEN_STRONG_INLINE` can take over 48 hours to compile without this
        flag. Refer to `configure.py` for more information about
        `EIGEN_STRONG_INLINE` and `/d2ReducedOptimizeHugeFunctions`.
    *   If either of the required DLLs, `msvcp140.dll` (old) or `msvcp140_1.dll`

  )

  if get_var(
      environ_cp, 'TF_OVERRIDE_EIGEN_STRONG_INLINE', 'Eigen strong inline',
      True, ('Would you like to override eigen strong inline for some C++ '
             'compilation to reduce the compilation time?'),
      'Eigen strong inline overridden.', 'Not overriding eigen strong inline, '
      'some compilations could take more than 20 mins.'):

build:mkl_aarch64 --define=build_with_openmp=true
build:mkl_aarch64 --define=build_with_acl=true
build:mkl_aarch64 -c opt

# Config setting to build oneDNN with Compute Library for the Arm Architecture (ACL).
# with Eigen threadpool support
build:mkl_aarch64_threadpool --define=build_with_mkl_aarch64=true
build:mkl_aarch64_threadpool -c opt

# CUDA: This config refers to building CUDA op kernels with nvcc.