EXPECT_FALSE(status.ok());
  EXPECT_EQ(count.Delta("tf.DoesntExist", "Unknown"), 1);
}

TEST(CompileMlirUtil, CreatesPipeline) {
  OpPassManager pass_manager;
  llvm::StringRef device_type = "XLA_CPU_JIT";

  CreateConvertMlirToXlaHloPipeline(pass_manager, device_type,
                                    /*enable_op_fallback=*/false,
                                    /*custom_legalization_passes*/ {});

syntax = "proto2";

package tensorflow.tf2xla.v2;

// The requested device type to compile for.
enum DeviceType {
  DEVICE_TYPE_UNSPECIFIED = 0;
  XLA_TPU_JIT = 1;
  XLA_CPU_JIT = 2;
  XLA_GPU_JIT = 3;

// RUN: tf-opt "--tfxla-device-specific-transforms=device-type=XLA_CPU_JIT" -verify-diagnostics -split-input-file %s | FileCheck -dump-input=fail %s

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 1399 : i32}} {

// CHECK-LABEL: stateless_op
func.func @stateless_op() -> tensor<i32> {
  // CHECK: %cst = "tf.Const"() <{value = dense<1> : tensor<i32>}> : () -> tensor<i32>

    ],
    deps = [
        ":xla_cpu_device",
        ":xla_cpu_jit",
        "//tensorflow/compiler/plugin",
    ] + if_cuda_or_rocm([
        ":xla_gpu_device",
        ":xla_gpu_jit",
    ]) + if_with_tpu_support([
        ":xla_tpu_device",
        ":xla_tpu_jit",
    ]),
    alwayslink = 1,
)

cc_library(
    name = "xla_cpu_jit",
    visibility = ["//visibility:public"],
    deps = [

  pm.addPass(mlir::createCanonicalizerPass());
  pm.addPass(mlir::createSymbolDCEPass());

  pm.addPass(mlir::TF::CreateTFShapeInferencePass());

  llvm::StringRef tf2xla_fallback_device_type = "XLA_CPU_JIT";
  pm.addPass(mlir::mhlo::createLegalizeTFPass(
      /*legalize_chlo=*/true, tf2xla_fallback_device_type,
      /*prefer_tf2xla=*/false));

  pm.addPass(mlir::TF::CreateStripTfAttributesPass());

// RUN: tf-opt "-xla-legalize-tf=use-tf2xla-fallback=true device-type=XLA_CPU_JIT" -verify-diagnostics %s | FileCheck --check-prefix SUPPORTED_FALLBACK_DEVICE %s
// RUN: tf-opt "-xla-legalize-tf=use-tf2xla-fallback=true" %s | FileCheck --check-prefix UNSPECIFIED_FALLBACK_DEVICE %s
// RUN: tf-opt "-xla-legalize-tf=use-tf2xla-fallback=true device-type=INVALID_DEVICE_TYPE" %s | FileCheck --check-prefix UNSUPPORTED_FALLBACK_DEVICE %s

// We run this test four times:

  explicit Tf2XlaRewriterTestPeer(mlir::Operation* op)
      : op_builder_(op),
        empty_rewriter_(op_builder_),
        tf2xla_rewriter_(op, empty_rewriter_,
                         /*device_type=*/"XLA_CPU_JIT") {}

  absl::StatusOr<TupleOp> ImportXlaComputationIntoModule(
      XlaComputation& computation) {
    return tf2xla_rewriter_.ImportXlaComputation(computation);
  }

 private:
  OpBuilder op_builder_;

    std::string device_name;

    // The index of the device.
    int device_ordinal = -1;

    // The name of the compilation device, also referred to as jit_device_type.
    // (e.g., "XLA_CPU_JIT");
    std::string compilation_device_name;

    // A vector of ShapeDeterminationFn (i.e., a bundle of LayoutSelectionFn,
    // ShapeRepresentationFn). Each bundle describes how the on-host shapes of

//     eliminates the use of resource variables.
//   . Legalizes the operations to XLA HLO operations.
//   . Canonicalizes the XLA HLO operations.
//
// device_type: XLA JIT device to use for compilation such as "XLA_CPU_JIT",
//   "XLA_GPU_JIT" or "XLA_TPU_JIT".
// use_tuple_args: when this is true, always create a tuple argument for the
//   entry computation.
// enable_op_fallback: when this is true, prefer tf2xla fallback kernels over

  mhlo::PopulateLegalizeTfPatterns(context, &patterns);
  TF::PopulateTFLoweringBeforeHLOPatterns(context, &patterns);
  mhlo::Tf2XlaTypeConverter converter;
  mhlo::PopulateLegalizeTfWithTf2XlaPatterns(
      "XLA_CPU_JIT", patterns, context, converter, /*prefer_tf2xla=*/false);
  stablehlo::StablehloToHloTypeConverter hlo_converter;
  chlo::populateChloToHloPatterns(context, &hlo_converter, &patterns);