}

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

  static constexpr size_t kResultIndex = 0;

  // Shapes of the input arguments.
  static constexpr int32_t kArg0Shapes[] = {
1, 2
  };
  static constexpr int32_t kArg1Shapes[] = {
3, 4
  };
  static constexpr int32_t kArg2Shapes[] = {
1
  };
  static constexpr int32_t kArg3Shapes[] = {
1
  };
  static constexpr int32_t kArg4Shapes[] = {
5
  };

using tpu::FunctionToHloArgs;
using tpu::MlirToHloArgs;
using tpu::ShardingAndIndex;
using tpu::TPUCompileMetadataProto;

static constexpr char kCompilationTimeStreamzName[] =
    "/tensorflow/core/tf2xla/api/v2/phase2_compilation_time";
static constexpr char kFullBridge[] = "full_bridge";
static constexpr char kCompilationStatusStreamzName[] =
    "/tensorflow/core/tf2xla/api/v2/phase2_compilation_status";

using ::testing::IsEmpty;
using ::testing::IsNull;
using ::testing::NotNull;
using ::testing::Optional;
using ::tsl::testing::StatusIs;

using AttrsAndConstraintsTest = ::mlir::quant::QuantizationTestBase;

constexpr absl::string_view kModuleStatic = R"mlir(
  module {
    func.func @main(%arg0: tensor<1x1024xf32>, %arg1: tensor<1024x3xf32>) -> tensor<1x3xf32> attributes {_from_xla_call_module} {

using ::tsl::monitoring::testing::Histogram;

static constexpr char kCompilationTimeStreamzName[] =
    "/tensorflow/core/tf2xla/api/v1/phase2_compilation_time";

static constexpr char kCompilationStatusStreamzName[] =
    "/tensorflow/core/tf2xla/api/v1/phase2_compilation_status";

static constexpr char kPlatformName[] = "Host";
constexpr char kEntryFuncName[] = "main";

static constexpr char kMlirModuleStr[] = R"(

// 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_";

#include "tensorflow/compiler/mlir/tensorflow/utils/xla_call_module_attrs.h"

namespace mlir::quant {

constexpr char kAttrMapAttribute[] = "attr_map";

// Name of the string attribute attached to `XlaCallModuleOp`, which is the
// textproto representation of `Method`.
inline constexpr StringRef kQuantizationMethodAttr = "_quantization_method";

// Permutation from the NHWC tensor format to NCHW. This is an inverse