// RUN: not flatbuffer_translate -mlir-to-tflite-flatbuffer -emit-builtin-tflite-ops=false %s 2>&1 | FileCheck %s

// CHECK: 'tfl.add' op is a TFLite builtin op but builtin emission is not enabled

func.func @main(tensor<3x2xi32>) -> tensor<3x2xi32> {
^bb0(%arg0: tensor<3x2xi32>):
  %0 = "arith.constant"() {name = "Const2", value = dense<10> : tensor<i32>} : () -> tensor<i32>

#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/experimental/tac/common/targets.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/hardwares/target_hardware.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/runtime_metadata_generated.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/lite/quantization/ir/QuantOps.h"

"""Target aware conversion for TFLite model."""

from tensorflow.compiler.mlir.lite.experimental.tac.py_wrapper import _pywrap_tac_wrapper


def run_tac(model_path, targets, output_path):
  """Run target aware conversion for the given tflite model file.

  Args:
    model_path: Path to the tflite model file.
    targets: A list of string of the desired targets. E.g., ['GPU', 'CPU'].

  bool outline_tf_while = false;
  // Whether to do shape inference.
  bool shape_inference = true;
  // Whether to do TFLite runtime verification.
  bool runtime_verification = true;
  // Whether to enable TFLite variables or not, this will allow
  // mutable variables and produce ReadVariable/AssignVariable ops in TFLite.
  bool enable_tflite_variables = false;
  // Whether to disable the variable freezing pass or not.

// quantization, specifically for
// third_party/tensorflow/lite/tools/optimize/quantize_weights.h.
// TODO(b/202468183): Selective quantization + quant debugger support for
// dynamic range quantization for verify_numeric and whole_model_verify flags.
TfLiteStatus QuantizeWeights(
    flatbuffers::FlatBufferBuilder* builder, const tflite::Model* input_model,
    const tflite::TensorType& inference_type,

#include "flatbuffers/flatbuffers.h"  // from @flatbuffers
#include "flatbuffers/minireflect.h"  // from @flatbuffers
#include "tensorflow/compiler/mlir/lite/schema/reflection/schema_generated.h"
#if FLATBUFFERS_LITTLEENDIAN == 0
#include "tensorflow/lite/core/model_builder.h"
#endif

namespace tflite {
namespace {

// Reads a model from a provided file path and verifies if it is a valid

def TFL_RuntimeVerification : OpInterface<"TflRuntimeVerifyOpInterface"> {
  let description = [{
    Interface to verify TFLite runtime op verification.

    This verifies that the converted TFLite ops has operand/result type
    supported by the TFLite runtime.
  }];

  let methods = [
    StaticInterfaceMethod<
      [{Returns whether the op's operands/results are supported by runtime.}],

// of double, and call TOCO's quantization routines to maintain bit-exactness of
// the values with the TOCO quantizer.
TfLiteStatus QuantizeModel(
    absl::string_view model_buffer, const tflite::TensorType &input_type,
    const tflite::TensorType &output_type,
    const tflite::TensorType &inference_type,
    const std::unordered_set<std::string> &operator_names,
    bool disable_per_channel, bool fully_quantize, std::string &output_buffer,

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_translate -input-arrays=squared_difference --experimental-prune-unreachable-nodes-unconditionally --tflite-flatbuffer-to-mlir - -o -
// TODO(b/329300758): re-enable filecheck | FileCheck %s
// Tests -input-arrays flag.

func.func @main(%arg0: tensor<4xf32>) -> tensor<4xf32> {
  %0 = "tfl.pseudo_const" () {value = dense<1.0> : tensor<4xf32>} : () -> tensor<4xf32> loc("Const")

// op.
std::unique_ptr<OperationPass<ModuleOp>> CreateUnfoldSplatConstantPass();

// Create a pass that legalizes MHLO to TFLite dialect.
std::unique_ptr<OperationPass<ModuleOp>> CreateLegalizeHloToTfLitePass();

// Creates a pass that lowers stablehlo composite ops to tflite ops.
std::unique_ptr<OperationPass<ModuleOp>> CreateCompositeLoweringPass();

// Adds the HLO to TF rewrite patterns to the specified pattern list.