// 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:     deprecated_builtin_code: 58,
// CHECK-NEXT:     version: 1,
// CHECK-NEXT:     builtin_code: LESS
// CHECK-NEXT:   }, {
// CHECK-NEXT:     deprecated_builtin_code: 118,
// CHECK-NEXT:     version: 1,
// CHECK-NEXT:     builtin_code: IF

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

func.func @main() {
  tf_executor.graph {
// CHECK:      node {
// CHECK-NEXT:   name: "predicate"
// CHECK-NEXT:   op: "Const"
// CHECK-NEXT:   attr {
// CHECK:          key: "dtype"
// CHECK-NEXT:     value {
// CHECK-NEXT:       type: DT_INT32
// CHECK-NEXT:     }
// CHECK-NEXT:   }
// CHECK-NEXT:   attr {
// CHECK-NEXT:     key: "value"
// CHECK-NEXT:     value {

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

// Check to see if nested regions in while loops are preserved
// CHECK:     %{{.*}}:2 = "tfl.while"(%{{.*}}, %{{.*}}) ({
// CHECK:     ^bb0(%{{.*}}: tensor<*xi32>, %{{.*}}: tensor<*xf32>):
// CHECK:       "tfl.yield"(%{{.*}}) : (tensor<i1>) -> ()
// CHECK:     },  {
// CHECK:     ^bb0(%{{.*}}: tensor<*xi32>, %{{.*}}: tensor<*xf32>):

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

func.func @main(tensor<4x5xbf16>) -> tensor<4x5xbf16> {
^bb0(%arg0: tensor<4x5xbf16>):

// CHECK:  {
// CHECK-NEXT:      version: 3,
// CHECK-NEXT:      operator_codes: [ {
// CHECK-NEXT:        deprecated_builtin_code: 53,
// CHECK-NEXT:        version: 7,
// CHECK-NEXT:        builtin_code: CAST
// CHECK-NEXT:      } ],

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

func.func @main(%arg0: tensor<4xi32>, %arg1: tensor<32x4x4x128xf32>, %arg2: tensor<1x32x42x128xf32>) -> tensor<1x64x84x32xf32> {
// CHECK: {
// CHECK-NEXT:  version: 3,
// CHECK-NEXT:  operator_codes: [ {
// CHECK-NEXT:    deprecated_builtin_code: 67,
// CHECK-NEXT:    version: 1,
// CHECK-NEXT:    builtin_code: TRANSPOSE_CONV
// CHECK-NEXT:  } ],

// RUN: not tf-mlir-translate -split-input-file -mlir-to-graphdef %s -o - 2>&1 | FileCheck %s

// Tests function with multiple blocks.

func.func @main() {
  ^bb:
    cf.br ^bb1
  ^bb1:
    func.return
}

// CHECK: functions must be of a single Graph with single op Islands: only single block functions are supported

// -----

// Tests invalid functions for exporting to Graph/GraphDef.

func.func @main() {
  func.return
}

// Test to verify translation & export work as intended with runtime.

// RUN: tf-opt --mlir-print-debuginfo --canonicalize --tfl-while-loop-outline %s | mlir-tflite-runner --dump-interpreter-state 2>&1 | FileCheck %s

// Verify value computed:
// ----------------------
// CHECK: result: Tensor<type: FLOAT32, shape: 1, values: 96>
// CHECK: pconst: Tensor<type: INT32, shape: , values: 1>

// Verify tensors in interpreter state:

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

func.func @main(tensor<3x2xf32>) -> tensor<3x2xi32> {
^bb0(%arg0: tensor<3x2xf32>):
  // CHECK:      {
  // CHECK-NEXT:     version: 3,
  // CHECK-NEXT:     operator_codes: [ {
  // CHECK-NEXT:       deprecated_builtin_code: 127,
  // CHECK-NEXT:       version: 1,
  // CHECK-NEXT:       builtin_code: BUCKETIZE
  // CHECK-NEXT:     } ],

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

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 486 : i32}} {
  func.func @main() {
    tf_executor.graph {
      %outputs, %control = tf_executor.island wraps "tf.Const"() {device = "", value = dense<1> : tensor<i32>} : () -> tensor<i32>

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

module {
func.func @add(%arg0: tensor<3x2xf32>) -> tensor<3x2xf32> attributes {tf.entry_function = {inputs = "serving_default_x", outputs = "outputs"}} {
  %0 = "tfl.pseudo_const" () {value = dense<[[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]> : tensor<3x2xf32>} : () -> tensor<3x2xf32>