#include "tensorflow/lite/toco/model_flags.pb.h"
#include "tensorflow/lite/toco/toco_flags.pb.h"

namespace tensorflow {

// Converts the given GraphDef to a TF Lite FlatBuffer string according to the
// given model flags, toco flags and debug information. Returns error status if
// it fails to convert the input.
absl::Status ConvertGraphDefToTFLiteFlatBuffer(
    const toco::ModelFlags& model_flags, toco::TocoFlags& toco_flags,

#include "tensorflow/lite/toco/model_flags.pb.h"
#include "tensorflow/lite/toco/toco_flags.pb.h"
#include "tensorflow/lite/toco/types.pb.h"
#include "tensorflow/lite/tools/optimize/reduced_precision_support.h"
#include "tsl/platform/errors.h"
#include "tsl/platform/statusor.h"

namespace tensorflow {

absl::Status ConvertGraphDefToTFLiteFlatBuffer(
    const toco::ModelFlags& model_flags, toco::TocoFlags& toco_flags,

#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/lite/toco/model_flags.pb.h"
#include "tensorflow/lite/toco/toco_flags.pb.h"

namespace tensorflow {

// Converts the given Jax model to a TF Lite FlatBuffer
// string according to the given model flags, toco flags and tags. Returns error
// status if it fails to convert the input.

#include "tensorflow/lite/toco/model_flags.pb.h"
#include "tensorflow/lite/toco/toco_flags.pb.h"

namespace tensorflow {

// Converts the given saved_model(either v1 or v2) to a TF Lite FlatBuffer
// string according to the given model flags, toco flags and tags. Returns error
// status if it fails to convert the input.
Status ConvertSavedModelToTFLiteFlatBuffer(

#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/lite/toco/model_flags.pb.h"
#include "tensorflow/lite/toco/toco_flags.pb.h"
#include "tensorflow/lite/toco/types.pb.h"
#include "tsl/platform/errors.h"
#include "tsl/platform/protobuf.h"  // IWYU pragma: keep

namespace tensorflow {
namespace {

    %0 = tfl.add %arg0, %arg1 {fused_activation_function = "RELU6", tac.device = "GPU"} : tensor<100xf32>
    func.return %0 : tensor<100xf32>
  }
  func.func @func_2_GPU_FLOAT(%arg0: tensor<100xf32>, %arg1: tensor<100xf32>) -> tensor<2x100xf32> attributes {tac.cost = 8.040000e+01 : f32, tac.device = "GPU", tac.inference_type = "FLOAT", tac.interface_name = "func_2"} {

// CHECK:           %[[VAL_3:.*]] = tfl.add %[[VAL_0]], %[[VAL_1]] {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : tensor<1xf32>
// CHECK:           %[[VAL_4:.*]] = tfl.mul %[[VAL_3]], %[[VAL_2]] {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : tensor<1xf32>

// CHECK:           %[[VAL_5:.*]] = tfl.add %[[VAL_0]], %[[VAL_1]] {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : tensor<1xf32>
// CHECK:           %[[VAL_6:.*]] = tfl.mul %[[VAL_5]], %[[VAL_2]] {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : tensor<1xf32>

// Test for partial folding: only fold i32 constants.
// RUN: tac-opt-all-backends -tfl-fold-constants-to-subgraph='fold-all-constants=false' %s -split-input-file -verify-diagnostics | FileCheck --check-prefix=PARTIAL %s

// Test for fold all constants.
// RUN: tac-opt-all-backends -tfl-fold-constants-to-subgraph='fold-all-constants=true' %s -split-input-file -verify-diagnostics | FileCheck --check-prefix=ALL %s

module {

    default_visibility = [
        "//tensorflow:__pkg__",
        "//tensorflow/compiler/mlir/lite:__subpackages__",
        "//tensorflow/lite/python/converter:__subpackages__",
        "//tensorflow/lite/toco/python:__subpackages__",
    ],
    licenses = ["notice"],
)

cc_library(
    name = "error_collector",
    srcs = ["error_collector.cc"],
    hdrs = ["error_collector.h"],
    deps = [