#include "xla/shape.h"
#include "xla/stream_executor/tpu/c_api_conversions.h"
#include "xla/stream_executor/tpu/tpu_api.h"
#include "xla/translate/mhlo_to_hlo/type_to_shape.h"

namespace mlir {

static FailureOr<std::vector<int64_t>> GetTPUInfeedLayoutFromAPI(
    RankedTensorType t) {
  // Call the TPU API to determine the right infeed layout. Note that
  // this can fail if we're not running on a TPU-enabled node.

  // representation of a `ModuleOp` containing a single function `@main`.
  // Returns `failure` iff there is no `@main` or no such operation is found in
  // `@main`.
  template <typename OpT>
  FailureOr<OpT> FindFirstOpFromMainFunc(ModuleOp module_op) {
    func::FuncOp main_func_op = FindMainFuncOp(module_op);
    if (main_func_op == nullptr) return failure();

    auto ops = main_func_op.getOps<OpT>();

    mlir::SymbolTableCollection* symbol_table = nullptr);

// Creates a new MLIR module that contains only the given functions and all
// reachable functions from them.
mlir::FailureOr<mlir::OwningOpRef<mlir::ModuleOp>> CreatePrunedModule(
    mlir::ModuleOp module, llvm::ArrayRef<llvm::StringRef> function_names);

}  // namespace TF
}  // namespace mlir

  if (splatLhs && splatRhs) {
    auto signedLhs = addSign(splatLhs.getSplatValue<ValType>(), etype);
    auto signedRhs = addSign(splatRhs.getSplatValue<ValType>(), etype);
    FailureOr<decltype(signedLhs)> result(Convert()(signedLhs, signedRhs));
    return succeeded(result) ? SplatElementsAttr::get(type, *result)
                             : Attribute();
  }

  SmallVector<ValType, 6> values;

  return op.getCallTargetName() == kTfTargetName;
}

DictionaryAttr GetTfBackendConfig(stablehlo::CustomCallOp op) {
  return op->getAttrOfType<DictionaryAttr>(kTfBackendConfigAttrName);
}

FailureOr<SymbolRefAttr> GetTfFuncCustomCallFuncName(
    stablehlo::CustomCallOp op) {
  if (!IsTfFuncCustomCall(op)) {
    return success(nullptr);
  }

  auto config = GetTfBackendConfig(op);
  if (config == nullptr) {

  // reached convergence, false otherwise.
  FailureOr<bool> PropagateShapeIntoAttachedFunctions(Operation* op,
                                                      int64_t max_iterations);

  // Shape propagation for region based control flow.
  // Returns a failure() on error, otherwise returns true to indicate that it
  // reached convergence, false otherwise.
  FailureOr<bool> PropagateShapeIntoAttachedRegions(Operation* op,

  std::unique_ptr<RE2> match_regex_;  // NOLINT
};

// Converts `Method` to a single-line textproto representation. Returns
// `failure()` when converting to textproto failed.
FailureOr<std::string> QuantizationMethodToTextProto(const Method& method) {
  TextFormat::Printer printer;
  printer.SetSingleLineMode(true);

  std::string method_txtpb;
  if (!printer.PrintToString(method, &method_txtpb)) {

        stack.push_back(called_func);
      }

      return WalkResult::advance();
    });
    if (result.wasInterrupted()) {
      return result;
    }
  }

  return WalkResult::advance();
}

FailureOr<OwningOpRef<ModuleOp>> CreatePrunedModule(
    ModuleOp module, llvm::ArrayRef<llvm::StringRef> function_names) {
  SymbolTableCollection symbol_table;
  OpBuilder builder(module.getContext());

        return IntegerType::get(type.getContext(), 16,
                                IntegerType::SignednessSemantics::Unsigned);
      })
      .Default([&type](Type) { return type; });
}

FailureOr<mlir::DenseElementsAttr> GetDenseAttrFromTensorProtoAttr(
    const llvm::StringRef mangled_tensor_proto, TensorType tensor_type) {
  ::tensorflow::TensorProto tensor_proto;

using quant::tensorflow::IsTFQintType;

#define GEN_PASS_DEF_CONVERTTFQUANTOPSTOMHLO
#include "tensorflow/compiler/mlir/quantization/stablehlo/passes/bridge/passes.h.inc"

template <typename UniformQuantizedOp>
FailureOr<TensorType> GetUniformQuantizedType(
    UniformQuantizedOp op, Type original_type,
    TypedValue<TensorType> scales_value,
    TypedValue<TensorType> zero_points_value, FloatType expressed_type,