// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_translate -output-arrays=mul,exp,div --experimental-prune-unreachable-nodes-unconditionally --tflite-flatbuffer-to-mlir - -o - | FileCheck %s
// Confirm graph pruning.

func.func @main(tensor<4xf32>) -> tensor<4xf32> {
^bb0(%arg0: tensor<4xf32>):
  %0 = "tfl.pseudo_const" () {value = dense<1.0> : tensor<4xf32>} : () -> tensor<4xf32> loc("Const")

// RUN: tf-mlir-translate -mlir-tf-to-hlo-text %s -tf-input-shapes=: -tf-xla-emit-return-tuple | FileCheck %s


// TODO(b/259459405): Remove this test along with the upstream refactoring to
// avoid non TF inputs.
// This is not a supported mode.
module attributes {tf.versions = {producer = 179 : i32}} {
  func.func @main(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {
    %0 = "stablehlo.add"(%arg0, %arg1) : (tensor<f32>, tensor<f32>) -> tensor<f32>

// RUN: tf-opt --tfl-legalize-tf-while %s -o - | flatbuffer_translate -mlir-to-tflite-flatbuffer - -o -  | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - | FileCheck %s

// This test is to test for unranked function output from input, the output type should be compatible with input type.

// CHECK: func @main(%arg0: tensor<1xf32>) -> tensor<*xf32> attributes {tf.entry_function = {inputs = "arg0", outputs = "tfl.while"}} {
// CHECK:   %0 = "tfl.while"(%arg0) ({

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

func.func @main() {
  tf_executor.graph {
    // CHECK: name: ".foo"
    %0:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<i32>} : () -> (tensor<i32>) loc("^foo")
    // CHECK: name: "fo.o"
    %1:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<1> : tensor<i32>} : () -> (tensor<i32>) loc("fo{o")

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

func.func @main() -> (tensor<1x2xf16>, tensor<2xf16>) {
  %graph:2 = tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_HALF", value = dense<1.0> : tensor<1x2xf16>} : () -> tensor<1x2xf16> loc("const1")

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

func.func @main() {
  tf_executor.graph {
    // CHECK: name: "foo"
    %0:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<i32>} : () -> (tensor<i32>) loc("foo")
    // CHECK: name: "foo{{_.*_1}}"
    %1:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<1> : tensor<i32>} : () -> (tensor<i32>) loc("foo")

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

// CHECK: {
// CHECK-NEXT:  version: 3,
// CHECK-NEXT:   operator_codes: [  ],
// CHECK-NEXT:   subgraphs: [ {
// CHECK-NEXT:     tensors: [ {
// CHECK-NEXT:       shape: [  ],
// CHECK-NEXT:       type: VARIANT,
// CHECK-NEXT:       buffer: 1,
// CHECK-NEXT:       name: "tf.Const",
// CHECK-NEXT:       quantization: {
// CHECK-EMPTY:

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

func.func @main(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>) -> tensor<10xi32>
attributes {tf.entry_function = {inputs = "input0,input1", outputs = "Add"}} {
  %graph = tf_executor.graph {
    %2:2 = tf_executor.island wraps "tf.Add"(%arg0, %arg1) {T = "tfdtype$DT_INT32", device = ""} : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi32> loc("Add")
    tf_executor.fetch %2 : tensor<10xi32>
  }

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

// CHECK: func @add(
// CHECK-SAME: {tf_saved_model.index_path = ["input1"]}
// CHECK-SAME: {tf_saved_model.index_path = ["input2"]}
// CHECK-SAME: {tf_saved_model.index_path = ["result"]}
// CHECK-SAME: tf.entry_function = {inputs = "input1:0,input2:0", outputs = "result:0"}
// CHECK-SAME: tf_saved_model.exported_names = ["add"]

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

func.func @main(tensor<4xf64>, tensor<4xf64>) -> tensor<4xf64> {
^bb0(%arg0: tensor<4xf64>, %arg1: tensor<4xf64>):
// CHECK:  {
// CHECK-NEXT:  version: 3,
// CHECK-NEXT:  operator_codes: [ {
// CHECK-NEXT:    deprecated_builtin_code: 32,
// CHECK-NEXT:    custom_code: "FlexAdd",
// CHECK-NEXT:    builtin_code: CUSTOM