}

// -----

// Tests collecting compilation and execution devices results in an error.

module attributes {tf.versions = {producer = 888 : i32}, tf.devices = ["/job:worker/replica:0/task:0/device:CPU:0", "/job:worker/replica:0/task:0/device:TPU:0"]} {
  func.func @bad_devices() {
    // expected-error@+1 {{error in fetching TPU compilation/execution devices: no TPU_SYSTEM devices found}}

}

StringRef BiasAddOp::GetOptimalLayout(const RuntimeDevices& devices) {
  // Keep current data format if no GPUs are available or if explicit placement
  // does not allow to use GPU for this operation.
  if (!CanUseGpuDevice(devices) || !CanUseGpuDevice(getOperation()))
    return getDataFormat();

  // Prefer NHWC for GPU devices.
  return "NHWC";
}

// RUN: tf-opt %s -split-input-file -verify-diagnostics -tf-extract-outside-compilation | FILECHECK_OPTS="" FileCheck %s

module attributes {tf.versions = {producer = 888 : i32}, tf.devices = ["/job:worker/replica:0/task:0/device:CPU:0", "/job:worker/replica:0/task:0/device:TPU_SYSTEM:0", "/job:worker/replica:0/task:0/device:TPU:0"]} {
  // Tests that TPU cluster with no outside compilation does not generate parallel_execute.

  // CHECK-LABEL: func @no_outside_compilation

    return
  fi

  local device="${devices[0]}"
  if [ "${#devices[@]}" -ne 1 ]; then
    seen_arrays=(/dev/md/*)
    device=${seen_arrays[0]}
    echo "Setting RAID array with local SSDs on device ${device}"
    if [ ! -e "$device" ]; then
      device="/dev/md/0"
      echo "y" | mdadm --create "${device}" --level=0 --raid-devices=${#devices[@]} "${devices[@]}"
    fi
  fi

			klog.V(4).InfoS("Device will be attached to container in the corresponding path on host", "containerName", container.Name, "path", symlinkPath)
			devices = append(devices, kubecontainer.DeviceInfo{PathOnHost: symlinkPath, PathInContainer: device.DevicePath, Permissions: permission})
		}
	}

	return devices, nil
}

// shouldMountHostsFile checks if the nodes /etc/hosts should be mounted
// Kubernetes only mounts on /etc/hosts if:

          [%arg0, %arg1] as %r0: tensor<*x!tf_type.resource<tensor<32xf32>>>,
          [%arg2, %arg3] as %r1: tensor<*x!tf_type.resource<tensor<32xf32>>>,
          [%u0#0, %u0#1] as %u : tensor<32xf32>)
          {n = 2 : i32, devices = {CORE_0 = ["/CPU:0", "/GPU:1"]}} {
        %read0 = "tf.ReadVariableOp"(%r0) : (tensor<*x!tf_type.resource<tensor<32xf32>>>) -> tensor<32xf32>
        // expected-remark@above {{ID: 1}}

  // Create FunctionLibraryRuntime.
  SessionOptions session_options;
  std::vector<std::unique_ptr<Device>> devices;
  TF_CHECK_OK(DeviceFactory::AddDevices(
      session_options, "/job:localhost/replica:0/task:0", &devices));
  OptimizerOptions opts;
  auto device_mgr = std::make_unique<StaticDeviceMgr>(std::move(devices));
  auto pflr = std::make_unique<ProcessFunctionLibraryRuntime>(

	EM_ST100         Machine = 60  /* STMicroelectronics ST100 processor. */
	EM_TINYJ         Machine = 61  /* Advanced Logic Corp. TinyJ processor. */
	EM_X86_64        Machine = 62  /* Advanced Micro Devices x86-64 */
	EM_PDSP          Machine = 63  /* Sony DSP Processor */
	EM_PDP10         Machine = 64  /* Digital Equipment Corp. PDP-10 */
	EM_PDP11         Machine = 65  /* Digital Equipment Corp. PDP-11 */

			klog.V(2).InfoS("MountVolume.markDeviceErrorState leaving volume uncertain", "volumeName", volumeToMount.VolumeName)
			return
		}

		// Only devices which were uncertain can be marked as unmounted
		markDeviceUnmountError := actualStateOfWorld.MarkDeviceAsUnmounted(volumeToMount.VolumeName)
		if markDeviceUnmountError != nil {

           /*default=*/"false",
           "Enables certain TPUExecute ops to run in parallel if they only "
           "operate on resources that live on composite devices.">
  ];
}

def ExecutorUpdateControlDependenciesPass : Pass<"tf-executor-update-control-dependencies", "ModuleOp"> {