}

TEST(TestUtil, MlirModuleDoesNotHave) {
  constexpr char arbirary_mlir[] = "arbirary_mlir";
  constexpr char bad_mlir[] = "bad_mlir";

  ASSERT_THAT(success(arbirary_mlir), Not(HasMlirModuleWith(bad_mlir)));
}

TEST(TestUtil, MlirModuleDoesNotHaveFiltered) {
  constexpr char arbirary_mlir[] = "arbirary_mlir";
  constexpr char bad_mlir[] = "bad_mlir";

using tpu::TPUCompileMetadataProto;

static constexpr char kMlirLegalizeCount[] =
    "/tensorflow/core/tf2xla/v1/mlir_failed_xla_legalize_tf_count";
static constexpr char kMlirLegalizeErrors[] =
    "/tensorflow/core/tf2xla/v1/mlir_failed_xla_legalize_tf_pass_count";
static constexpr char kBridgeStatusCounter[] =
    "/tensorflow/core/tf2xla/api/v2/phase2_compilation_status";
constexpr char kMlirCombinedMlirSuccess[] = "kMlirCombinedMlirSuccess";

// Device types.
inline constexpr llvm::StringRef kTpuDevice = "TPU";
// _xla_outside_compilation
inline constexpr llvm::StringRef kXlaOutsideCompilationAttr =
    "_xla_outside_compilation";
// device attr
inline constexpr llvm::StringRef kDeviceAttr = "device";
// Function attribute to signal that a function should be skipped from TPU
// island outlining. The attribute is set in

// to represent calling a TF host callback function.
constexpr llvm::StringRef kTfTargetName = "tf.call_tf_function";

// `tf.backend_config` is a DictionaryAttr, JAX2TF sets the value of its
// string attribute `caller_name` to the TF host callback function's name.
constexpr llvm::StringRef kTfBackendConfigAttrName = "tf.backend_config";
constexpr llvm::StringRef kCalledFuncAttrName = "called_func";

}  // namespace

// Presence of this attribute will mark the function as quantization target.
inline constexpr StringRef kFusedFunctionAttr = "tf_quant.composite_function";
// The keyword to detect if this is a `NullAttribute`.
inline constexpr StringRef kNullAttributeValue = "N/A";

// Prefixes attached to lifted functions.
constexpr StringRef kQuantizedFuncPrefix = "quantized_";
constexpr StringRef kCompositeFuncPrefix = "composite_";

          TPUPartitionedOpConversionPass> {
  void runOnOperation() override;
};

template <typename T>
LogicalResult ReplacePartitionedOp(IntegerAttr num_cores_per_replica, T op) {
  constexpr bool is_input =
      std::is_same_v<std::decay_t<T>, TF::TPUPartitionedInputOp>;
  static_assert(
      is_input || std::is_same_v<std::decay_t<T>, TF::TPUPartitionedOutputOp>,

  int64_t operands_size = get_size(inst->getOperandTypes());

  constexpr int kSizeFactor = 2;
// TODO(b/233827625): Remove TF_DISABLE_CONSTANT_FOLDING macro.
#ifdef TF_DISABLE_CONSTANT_FOLDING
  constexpr int64_t kResultsSizeThreshold = 0;
#else
  constexpr int64_t kResultsSizeThreshold = (1 << 23);  // 1 MB
#endif
  constexpr int64_t kOperandsSizeThreshold = (1 << 30);  // 128 MB

    "/tensorflow/core/tf2xla/api/v2/tf_dialect_to_executor_dialect_status",
    "Counts how often a successful export from TF Dialect to Executor Dialect "
    "is",
    "status");

constexpr char kExportSuccess[] = "success";
constexpr char kExportFailed[] = "failed";

namespace {

void AddTfDialectToExecutorPasses(OpPassManager &pm) {
  pm.addPass(mlir::TF::CreateTFRegionControlFlowToFunctional());

#include "tsl/lib/core/status_test_util.h"

namespace tensorflow {
namespace tf2xla {
namespace v2 {
namespace {

constexpr char kExportStreamzName[] =
    "/tensorflow/core/tf2xla/api/v2/tf_dialect_to_executor_dialect_status";
constexpr char kExportSuccess[] = "success";
constexpr char kExportFailed[] = "failed";

using mlir::DialectRegistry;
using mlir::MLIRContext;
using mlir::ModuleOp;
using mlir::OwningOpRef;

#include "tensorflow/compiler/mlir/lite/stablehlo/odml_converter/passes.h.inc"

constexpr float kOne = 1.0;
const float kOneOverRoot2 = kOne / std::sqrt(2);
constexpr float kHalf = kOne / 2.0;
constexpr float kTolerance = kOne / 1000.0;

// Gets the operation that uses the sole result of given operation
// if there is only one.
Operation* GetUserIfOnlyOne(Operation* op) {
  if (op->getNumResults() != 1) return nullptr;