// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s

func.func @main(tensor<4 x f32>, tensor<4 x i8>, tensor<4 x f32>, tensor<4 x f32>) -> tensor<4 x f32> {
// CHECK:      {
// CHECK-NEXT:     version: 3,
// CHECK-NEXT:     operator_codes: [ {
// CHECK-NEXT:       deprecated_builtin_code: 27,
// CHECK-NEXT:       version: 2,
// CHECK-NEXT:       builtin_code: SVDF
// CHECK-NEXT:     } ],

// RUN: tf-mlir-translate -mlir-to-graphdef %s -o - | FileCheck %s

func.func @main() -> tensor<2x3xi32> {
  %graph = tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {value = dense<5> : tensor<2x3xi32>} : () -> tensor<2x3xi32> loc("Const0")
    %1:2 = tf_executor.island wraps "tf.Const"() {value = dense<4.2> : tensor<4x5xf32>} : () -> tensor<4x5xf32> loc("Const1")

// RUN: tf-mlir-translate -mlir-to-graphdef %s | tf-mlir-translate -graphdef-to-mlir | tf-mlir-translate -mlir-to-graphdef | FileCheck %s

// Verifies that optional symbol ref attributes that aren't optional in TensorFlow are handled by setting the value to an empty string.
module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 458 : i32}} {
  func.func @main() {
    tf_executor.graph {

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -emit-custom-ops -o - | flatbuffer_to_string - | FileCheck %s

func.func @main(tensor<4xf32>) -> tensor<4xf32> {
^bb0(%arg0: tensor<4xf32>):

// CHECK:  {
// CHECK-NEXT:    version: 3,
// CHECK-NEXT:    operator_codes: [ {
// CHECK-NEXT:      deprecated_builtin_code: 18,
// CHECK-NEXT:      version: 1
// CHECK-NEXT:      builtin_code: MUL
// CHECK-NEXT:    }, {

// RUN: tf_tfl_translate --enable-hlo-to-tf-conversion --input-mlir %s -o - | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - | FileCheck %s --check-prefix=CHECK-ROUNDTRIP


module {
  func.func public @main( %arg0: tensor<i64>) ->  tensor<i64> {
    %0 = func.call @test_add_roundtrip(%arg0) : (tensor<i64>) -> tensor<i64>

    return %0 : tensor<i64>
  }


  // CHECK-LABEL: func.func private @test_add_roundtrip

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s

module {
func.func @serving_default(%arg0: tensor<3x2xf32>) -> tensor<3x2xf32> attributes {tf.entry_function = {inputs = "serving_default_x", outputs = "outputs"}} {
// CHECK:       {

// CHECK-LABEL:   version: 3,

// CHECK-LABEL:   operator_codes: [ {
// CHECK:           version: 1
// CHECK:         } ],

// CHECK-LABEL:   subgraphs: [ {

// RUN: tf-mlir-translate -mlir-to-graphdef %s -tf-export-original-func-name | tf-mlir-translate -graphdef-to-mlir | tf-mlir-translate -mlir-to-graphdef -tf-export-original-func-name | FileCheck %s

// Tests #tf_type.func attributes are exported as AttrValue.NameAttrList attributes
// with its attr field populated with nested attributes.

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 458 : i32}} {
  func.func @main() {

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s

module {
func.func @serving_default(%arg0: tensor<*xf32>) -> tensor<*xf32> attributes {tf.entry_function = {inputs = "serving_default_x", outputs = "outputs"}} {
// CHECK:     {
// CHECK-NEXT:  version: 3,
// CHECK-NEXT:  operator_codes: [ {
// CHECK-NEXT:    version: 1
// CHECK-NEXT:  } ],
// CHECK-NEXT:  subgraphs: [ {
// CHECK-NEXT:    tensors: [ {

// RUN: tf-opt %s -mlir-print-debuginfo -mlir-print-local-scope | tf-opt -mlir-print-debuginfo -mlir-print-op-generic -mlir-print-local-scope | FileCheck %s

// This file should be written in the generic form with debug locations.
// (that is, as if printed with `-mlir-print-debuginfo -mlir-print-op-generic`).
// The test parses the file, prints it in the pretty form with debug locations,
// then parses it back, then prints it in the generic form again.

// RUN: tf-mlir-translate -mlir-to-graphdef %s -o - | FileCheck %s

func.func @main() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.RecvTPUEmbeddingActivations"() {config = "test_config_recv_embedding"} : () -> tensor<512x256xf32> loc("RecvTPUEmbedding")