constexpr float kMinSparsityLevel = 0.3;
// Heuristic to check if a block configuration is correct for float constants.
constexpr float kBlockOverRandomSparsityRatio = 0.9;
// After quantization, some non-zero values are set to 0.
// Lower the ratio for identifying block configuration for quantized constants.
constexpr float kBlockOverRandomSparsityRatioQuant = 0.8;

namespace v1 {
namespace {

using ::mlir::DialectRegistry;
using ::mlir::MLIRContext;
using ::mlir::ModuleOp;
using ::mlir::OwningOpRef;
using ::tensorflow::monitoring::testing::CellReader;

static constexpr char kCompilationStreamz[] =
    "/tensorflow/core/tf_mlir_bridge_first_phase_v2_count";

std::string TestDataPath() {
  return tensorflow::GetDataDependencyFilepath(

namespace {

using mlir::ModuleOp;

using mlir::DialectRegistry;
using mlir::MLIRContext;
using mlir::ModuleOp;
using mlir::OwningOpRef;
using tensorflow::monitoring::testing::CellReader;

static constexpr char kCompilationStreamz[] =
    "/tensorflow/core/tf2xla/api/v1/tf_dialect_to_executor_dialect_status";

std::string TestDataPath() {
  return tensorflow::GetDataDependencyFilepath(

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"

namespace mlir {
namespace quant {
namespace {
constexpr StringRef kDequantizeFunctionName = "composite_dequantize";
constexpr StringRef kUniformQuantizationFunctionName = "uniform";

// Pre-actions before adding quantization logics. It creates a function with the

// `TF::XlaCallModuleOp`s. They are quantized using the weight constants, not
// relying on calibration.
class WeightOnlyPtqComponent : public Component {
 public:
  // Used for debugging purposes.
  static constexpr absl::string_view kName = "quant_ptq_weight_only";

  explicit WeightOnlyPtqComponent(absl::Nonnull<MLIRContext*> ctx);

  absl::StatusOr<ModuleOp> Run(
      ModuleOp module_op,

  // Name of the running pass.
  std::string pass_name_;
  // Pointer to the global ErrorCollector instance.
  ErrorCollector *error_collector_;
};

// Prefix when adding error code as a note in Diagnostic.
constexpr char kErrorCodePrefix[] = "Error code: ";

// Adds error code to a newly created InFlightDiagnostic.
inline InFlightDiagnostic AttachErrorCode(InFlightDiagnostic &&diag,

#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_device.h"

namespace mlir {
namespace TFDevice {

namespace {

constexpr char kXlaOutsideCompilationAttr[] = "_xla_outside_compilation";

#define GEN_PASS_DEF_VERIFYNOOUTSIDECOMPILATIONMARKERSPASS
#include "tensorflow/compiler/mlir/tensorflow/transforms/tf_device_passes.h.inc"

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"

namespace mlir {
namespace TFDevice {
namespace {
constexpr char kDeviceAttr[] = "device";
constexpr char kMiniBatchSplitsAttr[] = "mini_batch_splits";
constexpr char kMiniBatchCsrAttr[] = "mini_batch_in_csr";

#define GEN_PASS_DEF_EMBEDDINGPROGRAMKEYPASS

#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"

namespace mlir {
namespace TF {
namespace {

constexpr char kResourceFunctionMsg[] =
    "expects function level resource argument";
constexpr char kInvalidResourceMsg[] =
    "expects resource to be a VarHandleOp or function argument";
constexpr char kResourceNameArgAttr[] = "tf.resource_name";

// Checks if a function has only one block.

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops_a_m.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"

namespace mlir {
namespace TF {
namespace {

constexpr const char *kDeviceAttr = "device";

#define GEN_PASS_DEF_CONSTANTOPDEVICEASSIGNMENTPASS
#include "tensorflow/compiler/mlir/tensorflow/transforms/tf_passes.h.inc"

struct ConstantOpDeviceAssignmentPass