using ::mlir::func::FuncOp;
using ::tsl::Status;
using ::tsl::StatusOr;
using ::xla::ReplicaGroup;
using ::xla::ShapeUtil;
using ::xla::XlaBuilder;
using ::xla::XlaComputation;
using ::xla::XlaOp;

static constexpr char kMlirModuleStr[] = R"(
module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 1442 : i32}} {

#include "mlir/IR/Builders.h"  // from @llvm-project
#include "mlir/IR/Operation.h"  // from @llvm-project

namespace mlir {
namespace TFL {
namespace tac {

// Cost attribute string on the TFL dialect.
constexpr char kCost[] = "tac.cost";

inline void UpdateCost(Operation* op, float cost, OpBuilder* builder) {
  op->setAttr(kCost, builder->getF32FloatAttr(cost));
}

// Get the cost annotated with kCost.

    {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) {

//
// This method uses a temporary buffer to hold contents.
int CopyFileContents(int dst_fd, int src_fd, off_t size) {
  // Use a copy buffer of 128KB but don't store it on the stack.
  constexpr static size_t kPosixCopyFileBufferSize = 128 * 1024;
  std::unique_ptr<char[]> buffer(new char[kPosixCopyFileBufferSize]);

  off_t offset = 0;
  int bytes_transferred = 0;
  int rc = 1;

#include "tensorflow/core/util/device_name_utils.h"

namespace tensorflow {
using tsl::StatusOr;

inline constexpr absl::string_view kNumCoresPerReplicaAttr =
    "num_cores_per_replica";
inline constexpr absl::string_view kTopologyAttr = "topology";
inline constexpr absl::string_view kDeviceAssignmentAttr = "device_assignment";

// A TPU device for execution alongside its associated host CPU device.

#include <string>
#include <utility>

#include "llvm/ADT/StringExtras.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project

namespace mlir {
namespace quant {

constexpr StringRef kTfEntryFunctionAttr = "tf.entry_function";

void AddEntryFunctionInput(StringRef input_name, func::FuncOp func_op) {
  auto entry_func_attr =
      func_op->getAttrOfType<DictionaryAttr>(kTfEntryFunctionAttr);

// lifted quantizable functions.
class PreCalibrationComponent : public Component {
 public:
  // Name of the post-training quantization pre-calibration step. Used for
  // debugging purposes.
  static constexpr absl::string_view kName = "quant_ptq_pre_calibration";

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

  absl::StatusOr<ModuleOp> Run(
      ModuleOp,

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

namespace mlir {
namespace TFTPU {
namespace {

constexpr char kXlaOutsideCompilationAttr[] = "_xla_outside_compilation";
constexpr char kTPUEmbeddingAttr[] = "_tpu_embedding_layer";

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

#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) {

namespace TFDevice {
namespace {

constexpr char kBadDecompositionMessage[] =
    "Resource ops decomposition did not converge";

// Decomposing resource ops should not take more than a few iterations (2-3) to
// converge as only a few patterns create new resource ops that can be further
// decomposed. The rest of the iterations are enough to clean up any dead ops
// created by decomposition.
constexpr int kMaxIterations = 20;