// 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

// 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_";

    "/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;

  // `auto_control_deps.py` where it is assumed that allocated resource values
  // NEVER alias. We should align our assumptions in the future.
  static constexpr int64_t kUnknownResourceId = -1;
  static constexpr int64_t kInvalidResourceId = -2;
  static constexpr int64_t kMaxResourceTypeId = 9999;
};

}  // namespace detail

// An analysis that runs on a module and maps each resource-type value to a

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/lite/c/builtin_op_data.h"

namespace mlir {
namespace TFL {

namespace {

constexpr char kTFImplements[] = "tf._implements";
constexpr char kMaxUnpooling[] = "MaxUnpooling2D";
constexpr char kImageWarping[] = "DenseImageWarp";

inline ConstBytesAttr CustomOption(OpBuilder* builder,
                                   const std::string& content) {

    {tsl::monitoring::Buckets::Exponential(1, 1.5, 45)});

// Name of component for error logging. This name is fixed and required to
// enable logging.
constexpr char kBridgeComponent[] = "TFXLABridge";
constexpr char kFullBridge[] = "full_bridge";

namespace {

bool ShouldFallbackToGraphCompiler(
    const std::variant<MlirToHloArgs, FunctionToHloArgs>& computation) {

#include "tsl/platform/protobuf.h"

namespace mlir {
using tsl::Status;
using tsl::StatusOr;

static constexpr char kMlirToGraphCompilationCheckName[] =
    "mlir-to-graph-compilation-check";
// Use CPU arbitrarily in order to check that a graph compiles at all
static constexpr char kArbitraryDeviceName[] = "XLA_CPU_JIT";

namespace {
inline absl::string_view StringRefToView(llvm::StringRef ref) {

InitMlir::InitMlir(int *argc, char ***argv) {
  llvm::setBugReportMsg(
      "TensorFlow crashed, please file a bug on "
      "https://github.com/tensorflow/tensorflow/issues with the trace "
      "below.\n");

  constexpr char kSeparator[] = "--";

  // Find index of separator between two sets of flags.
  int pass_remainder = 1;
  bool split = false;
  for (int i = 0; i < *argc; ++i) {
    if (llvm::StringRef((*argv)[i]) == kSeparator) {