from tensorflow.python.util.lazy_loader import KerasLazyLoader as _KerasLazyLoader

# Make sure code inside the TensorFlow codebase can use tf2.enabled() at import.
_os.environ["TF2_BEHAVIOR"] = "1"
from tensorflow.python import tf2 as _tf2
_tf2.enable()

# API IMPORTS PLACEHOLDER

# WRAPPER_PLACEHOLDER

# Make sure directory containing top level submodules is in

void TFE_ContextSetSoftDevicePlacement(TFE_Context* ctx, unsigned char enable,
                                       TF_Status* status) {
  tensorflow::unwrap(ctx)->SetAllowSoftPlacement(enable);
}

void TFE_ContextSetLogDevicePlacement(TFE_Context* ctx, unsigned char enable,
                                      TF_Status* status) {
  tensorflow::unwrap(ctx)->SetLogDevicePlacement(enable);
}

)

# This flag enables experimental MLIR support.
config_setting(
    name = "with_mlir_support",
    define_values = {"with_mlir_support": "true"},
    visibility = ["//visibility:public"],
)

# This flag forcibly enables experimental MLIR bridge support.
config_setting(
    name = "enable_mlir_bridge",
    define_values = {"enable_mlir_bridge": "true"},

      if ld_version_int is None:
        ld_version_int = convert_version_to_int(ld_version[4])

      # Enable if 'ld' version >= 2.35
      if ld_version_int >= 2035000:
        write_to_bazelrc(
            'build --copt="-DEIGEN_ALTIVEC_ENABLE_MMA_DYNAMIC_DISPATCH=1"')

  with_xla_support = environ_cp.get('TF_ENABLE_XLA', None)
  if with_xla_support is not None:
    write_to_bazelrc('build --define=with_xla_support=%s' %

        "//tensorflow/core:test_main",
    ],
)

tf_cc_test(
    name = "parallel_device_remote_test",
    srcs = ["parallel_device_remote_test.cc"],
    # TODO(b/136478427): Enable global heap checking when servers shut down
    # cleanly.
    args = ["--heap_check="],
    deps = [
        ":parallel_device",
        ":parallel_device_testlib",
        "//tensorflow/c:c_api",

    Windows x64 & x86.

    *   **Windows x64 & x86 packages:**
        *   oneDNN optimizations are *enabled by default* on X86 CPUs

            string(static_cast<const char*>(tensor_interface->Data()),
                   tensor_interface->ByteSize()))) {
      return InvalidArgument(
          "Malformed TF_RESOURCE tensor: unable to parse resource handle");
    }
    return absl::OkStatus();
  }
  return absl::OkStatus();
}

static bool TF_Run_Inputs(TF_Tensor* const* c_inputs,

  TF_SetConfig(&options->session_options, proto, proto_len, status);
}

void TFE_ContextOptionsSetAsync(TFE_ContextOptions* options,
                                unsigned char enable) {
  options->async = enable;
}

void TFE_ContextOptionsSetDevicePlacementPolicy(
    TFE_ContextOptions* options, TFE_ContextDevicePlacementPolicy policy) {
  options->device_placement_policy = policy;
}

source "${BASH_SOURCE%/*}/utilities/setup.sh"

# Record GPU count and CUDA version status
if [[ "$TFCI_NVIDIA_SMI_ENABLE" == 1 ]]; then
  tfrun nvidia-smi
fi

# Update the version numbers for Nightly only
if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then
  tfrun python3 tensorflow/tools/ci_build/update_version.py --nightly
  # replace tensorflow to tf_nightly in the wheel name

  mkdir -p $TFCI_OUTPUT_DIR/extra_wheels
  ls -t *.whl | tail -n +2 | xargs mv -t $TFCI_OUTPUT_DIR/extra_wheels
fi

# Repair wheels with auditwheel and delete the old one.
if [[ "$TFCI_WHL_AUDIT_ENABLE" == "1" ]]; then
  python3 -m auditwheel repair --plat "$TFCI_WHL_AUDIT_PLAT" --wheel-dir . *.whl
  # if the wheel is already named correctly, auditwheel won't rename it. so we