#include "tensorflow/core/public/session.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/statusor.h"

namespace tensorflow {
namespace internal {

// Register all custom ops including user specified custom ops.
Status RegisterAllCustomOps(const 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(

    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 = [

}

// CHECK: %[[CST:.*]] = arith.constant dense<1> : tensor<4xi32>
// CHECK:  [[VAL_0:%.*]] = "tfl.reshape"(%1, %[[CST]]) {tac.device = "GPU",  tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>
// CHECK:  [[VAL_1:%.*]] = "tfl.reshape"(%2, %[[CST]]) {tac.device = "GPU",  tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>

#include "tensorflow/compiler/mlir/lite/experimental/tac/common/targets.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/tac_filter.pb.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/transforms/passes.h"

namespace mlir {
namespace TFL {
namespace tac {
namespace {

using ::third_party::tensorflow::compiler::mlir::lite::experimental::tac::
    FunctionFilter;

#include "tensorflow/compiler/mlir/lite/experimental/tac/common/targets.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/common/utils.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/hardwares/target_hardware.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/transforms/device_transform_gpu.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"

#include "tensorflow/compiler/mlir/lite/experimental/tac/common/cost.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/common/targets.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/common/utils.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/hardwares/target_hardware.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"

namespace mlir {
namespace TFL {
namespace tac {
namespace {

  %1 = "tfl.mul"(%0, %arg2) {fused_activation_function = "RELU6", per_device_costs = {CPU = 5.0 : f32, GPU = 1.0 : f32}, tac.device = "GPU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

        "//tensorflow/lite/toco:toco_tooling",
        "//tensorflow/lite/toco:tooling_util",
        "//tensorflow/lite/toco:types_proto_cc",
        "//tensorflow/lite/toco/logging:conversion_log_util",
        "//tensorflow/lite/toco/logging:toco_conversion_log_proto_cc",
        "//third_party/python_runtime:headers",  # build_cleaner: keep; DNR: b/35864863
        "@com_google_absl//absl/container:flat_hash_set",