// 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: [ 5 ],
// CHECK-NEXT:      buffer: 1,
// CHECK-NEXT:      name: "StatefulPartitionedCall:1",
// CHECK-NEXT:      quantization: {
// CHECK-EMPTY:
// CHECK-NEXT:      },

// RUN: tf-mlir-translate -mlir-to-graph %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")
// CHECK: node {
// CHECK-NEXT: name: "const1"
// CHECK-NEXT: op: "Const"
// CHECK: dtype: DT_HALF
// CHECK: half_val: 15360

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

func.func @main(%arg0: tensor<4x10x15xf32>, %arg1: tensor<4x15x17xf32>) -> tensor<4x10x17xf32> {
  %0 = "tfl.batch_matmul"(%arg0, %arg1) {adj_x = false, adj_y = false} : (tensor<4x10x15xf32>, tensor<4x15x17xf32>) -> tensor<4x10x17xf32>
  func.return %0:  tensor<4x10x17xf32>

// CHECK-LABEL: main

// RUN: tf-quant-opt %s -quant-insert-main-function -mlir-disable-threading \
// RUN:     -allow-unregistered-dialect -split-input-file | FileCheck %s

// CHECK-LABEL: module attributes {tf.versions = {producer = 930 : i32}, tf_saved_model.semantics, tfl.description = "MLIR Converted.", tfl.schema_version = 3 : i32}  {
module attributes {tf.versions = {producer = 930 : i32}, tf_saved_model.semantics, tfl.description = "MLIR Converted.", tfl.schema_version = 3 : i32}  {

// 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: 32,
// CHECK-NEXT:    custom_code: "NumericVerify",
// CHECK-NEXT:    builtin_code: CUSTOM
// CHECK-NEXT:  } ],
// CHECK-NEXT:  subgraphs: [ {
// CHECK-NEXT:    tensors: [ {
// CHECK-NEXT:      shape: [ 4 ],

\03\07\03\03\03\00\05\04\1F\03\01\06\03\01\05\01\00\9E\07G\1B)\11\0B!\1B\1D\05\1B\1B\03\0F%\1F/!!)#\1F\19)\1F\13\15\1D\15G\11\1F\15\11\0F\0B\11builtin\00vhlo\00module\00func_v1\00return_v1\00custom_call_v1\00call_v1\00xla_call_module_serialization.mlir\00arg_attrs\00function_type\00res_attrs\00sym_name\00sym_visibility\00mhlo.num_partitions\00api_version\00backend_config\00call_target_name\00called_computations\00has_side_effect\00operand_layouts\00output_operand_aliases\00result_layouts\00tf.ba...

// RUN: tf-mlir-translate -mlir-to-graphdef -tf-export-entry-func-to-flib  %s -o - 2>&1 | FileCheck %s

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 458 : i32}} {
  func.func @main() attributes {tf.entry_function = {inputs = "", outputs = ""}} {
    tf_executor.graph {

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

// CHECK: op: "Split"
// CHECK: attr {
// CHECK:   key: "num_split"
// CHECK:   value {
// CHECK:     i: 2
// CHECK:   }
// CHECK: }

func.func @main() {
  tf_executor.graph {
    %dim:2 = tf_executor.island wraps "tf.Const"() {dtype = "tftype$DT_INT32", value = dense<0> : tensor<i32>} : () -> tensor<i32>

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_translate -input-arrays=squared_difference --experimental-prune-unreachable-nodes-unconditionally --tflite-flatbuffer-to-mlir - -o -
// TODO(b/329300758): re-enable filecheck | FileCheck %s
// Tests -input-arrays flag.

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

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

// Verify tf.aliasing_output attributes on args are dropped during export.

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 121 : i32}} {
  func.func @main(%arg0: tensor<*xf32> {tf.aliasing_output = 0 : i64}, %arg1: tensor<2x4x6x8xi32>) -> (tensor<*xf32>, tensor<2x4x6x8xi32> {tf.device = "/CPU:1"})