)

bazel_dep(name = "xla", repo_name = "xla")
local_path_override(
    module_name = "xla",
    path = "third_party/xla",
)

tsl_extension = use_extension("@xla//third_party/extensions:tsl.bzl", "tsl_extension")
use_repo(tsl_extension, tsl = "tsl")

llvm = use_extension("@xla//third_party/extensions:llvm.bzl", "llvm_extension")
use_repo(llvm, "llvm-project")

  cat > $BATS_TEST_TMPDIR/allowed_to_be_extra <<EOF
@xla//third_party/mkl
@xla//third_party/mkl_dnn
@absl_py//
@bazel_tools//src
@bazel_tools//platforms
@bazel_tools//tools
@org_tensorflow//tensorflow
@com_google_absl//
@com_google_protobuf//
@internal_platforms_do_not_use//host
@jpegxl//
@pybind11_abseil//pybind11_abseil
//external
@local
@xla//xla
@tsl//tsl
@com_github_googlecloudplatform_google_cloud_cpp//

        "//tensorflow/core/transforms:__subpackages__",
    ],
    deps = [
        ":c_api_macros_hdrs",
        "@xla//xla/tsl/c:tsl_status",
        "@xla//xla/tsl/c:tsl_status_internal",
        "@xla//xla/tsl/platform:status",
    ] + select({
        "//tensorflow:android": [
            "//tensorflow/core:portable_tensorflow_lib_lite",  # TODO(annarev): exclude runtime srcs

#ifdef __cplusplus
extern "C" {
#endif

// When `enable` is true, set
// tensorflow.ConfigProto.OptimizerOptions.global_jit_level to ON_1, and also
// set XLA flag values to prepare for XLA compilation. Otherwise set
// global_jit_level to OFF.
//
// This and the next API are syntax sugar over TF_SetConfig(), and is used by
// clients that cannot read/write the tensorflow.ConfigProto proto.

        "@com_google_absl//absl/container:flat_hash_map",
        "@com_google_absl//absl/memory",
        "@com_google_absl//absl/strings",
        "@com_google_absl//absl/time",
        "@xla//xla/tsl/c:tsl_status_internal",
        "@xla//xla/tsl/distributed_runtime/coordination:coordination_service_agent",
    ],
    alwayslink = 1,
)

cc_library(
    name = "c_api_experimental_reader",
    testonly = True,

  auto* optimizer_options =
      config.mutable_graph_options()->mutable_optimizer_options();
  if (enable) {
    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::ON_1);

    // These XLA flags are needed to trigger XLA properly from C (more generally
    // non-Python) clients. If this API is called again with `enable` set to
    // false, it is safe to keep these flag values as is.
    tensorflow::MarkForCompilationPassFlags* flags =

**Linux XLA**                 | [![Status](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-xla.svg)](https://storage.googleapis.com/tensorflow-kokoro-build-badges/ubuntu-xla.html)         | TBA

#include "tensorflow/c/eager/tfe_tensorhandle_internal.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/c/tf_status_helper.h"
#include "xla/tsl/c/tsl_status_internal.h"
#include "xla/tsl/distributed_runtime/coordination/coordination_service_agent.h"
#include "xla/tsl/framework/cancellation.h"
#include "tensorflow/core/common_runtime/composite_device.h"
#include "tensorflow/core/common_runtime/device.h"

                ' of compute capabilities excluding version %s.' % ver)
            all_valid = False
          if ver < 35:
            print('WARNING: XLA does not support CUDA compute capabilities '
                  'lower than sm_35. Disable XLA when running on older GPUs.')
      else:
        ver = float(m.group(0))
        if ver < 3.0:

#include "tensorflow/c/eager/graph_function.h"
#include "tensorflow/c/tf_datatype.h"
#include "tensorflow/c/tf_status.h"
#include "tensorflow/c/tf_status_helper.h"
#include "xla/tsl/c/tsl_status_internal.h"
#include "tensorflow/core/framework/full_type.pb.h"
#include "tensorflow/core/framework/shape_inference.h"
#include "tensorflow/core/framework/tensor_shape.h"