# RUN: tf_tfl_translate -unfold_batchmatmul=false -tf-input-arrays=Placeholder,Placeholder_1 -tf-input-shapes=2,5,3:3,7 -tf-input-data-types=DT_FLOAT,DT_FLOAT -tf-output-arrays=MatMul -output-mlir %s -o - 2>&1 | FileCheck %s

node {
  name: "Placeholder"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        dim {
          size: 2

// RUN: tf-tfrt-opt -tfrt-lower-cluster-to-runtime-ops-non-tpu -split-input-file -verify-diagnostics %s | FileCheck %s

module attributes {tf.versions = {producer = 888 : i32}, tf.devices = ["/job:worker/replica:0/task:0/device:CPU:0", "/job:worker/replica:0/task:0/device:TPU_SYSTEM:0", "/job:worker/replica:0/task:0/device:GPU:0"]} {

  // CHECK-LABEL: @converts_cluster
  func.func @converts_cluster() {
    // CHECK: "tf.XlaLaunch"()

// RUN: tf-opt %s -tfl-prepare-quantize -canonicalize -tfl-quantize -canonicalize -tfl-optimize -canonicalize | FileCheck %s

// CHECK-LABEL: fuseMulIntoPerTensorConv2dWithQDQs
func.func @fuseMulIntoPerTensorConv2dWithQDQs(%arg0: tensor<256x32x32x3xf32>) -> tensor<256x8x7x3xf32> {
  %cst = arith.constant dense<1.5> : tensor<3xf32>
  %cst_0 = arith.constant dense<[1.0, 2.0, 3.0]> : tensor<3xf32>
  %w = arith.constant dense<2.0> : tensor<3x3x3x3xf32>

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - | FileCheck %s
// Confirm float constants and operators survive a roundtrip

func.func @main(tensor<4xf32>) -> tensor<4xf32> {
^bb0(%arg0: tensor<4xf32>):
  // CHECK:      [[CONST:%.*]] = "tfl.pseudo_const"() <{value = dense<1.000000e+00> : tensor<4xf32>}> : () -> tensor<4xf32>

// RUN: tac-translate -input-mlir -output-mlir -device-specs=NNAPI %s -o - 2>&1 | FileCheck %s

module {
  // CHECK-LABEL: main
  func.func @main(%arg0: tensor<4xf32>, %arg1: tensor<4xf32>) -> tensor<4xf32> {
    %0 = "tfl.squared_difference"(%arg0, %arg1) : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
    func.return %0 : tensor<4xf32>
    // CHECK:  [[VAL_0:%.*]] = tfl.sub %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<4xf32>

// RUN: odml-to-stablehlo-opt %s -stablehlo-composite-legalize-tfl-custom | FileCheck %s
// 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<3x3xf32>, %arg1: tensor<3x3xf32>, %arg2: tensor<1x100x32x4xf32>,

// RUN: json_to_flatbuffer %p/test_schema.fbs %s | flatbuffer_translate --tflite-flatbuffer-to-mlir -o - | FileCheck %s

// CHECK: %0 = "tfl.pseudo_const"() <{value = dense<2> : tensor<2xi32>}> : () -> tensor<2xi32>
// CHECK: %1 = "tfl.reshape"(%arg0, %0) : (tensor<1x4xf32>, tensor<2xi32>) -> tensor<2x2xf32>

{
  "version": 3,
  "operator_codes": [
    {
      "builtin_code": "RESHAPE"
    }
  ],
  "subgraphs": [
    {
      "tensors": [

// RUN: odml-to-stablehlo-opt %s -unfold-splat-constant-pass -cse -verify-diagnostics | FileCheck %s

// CHECK-LABEL: @unfold_splat_constant_float
func.func @unfold_splat_constant_float() -> tensor<1x750xf32> {
  %cst = mhlo.constant dense<7.680000e+02> : tensor<1x750xf32>
  func.return %cst : tensor<1x750xf32>

  // CHECK-DAG: %0 = mhlo.constant dense<7.680000e+02> : tensor<f32>

// RUN: tf-tfrt-opt -pass-pipeline='builtin.module(tf-executor-to-tfrt-pipeline{target-tpurt=true})' %s | FileCheck %s

module attributes {tf_saved_model.semantics}  {
  // CHECK-LABEL: func @main
  func.func @main_func() -> (tensor<*xf32> {tf_saved_model.index_path = ["a"]}) attributes {tf_saved_model.exported_names = ["main_func"]} {
    %0 = tf_executor.graph {

// RUN: tf-tfrt-opt -tf-restore-pruning %s | FileCheck %s

// CHECK-LABEL:   func.func @prune_unused_restore
func.func @prune_unused_restore() {
  %cst = "tf.Const"() <{value = dense<"restore_ariables"> : tensor<!tf_type.string>}> : () -> tensor<!tf_type.string>
  %cst_0 = "tf.Const"() <{value = dense<""> : tensor<1x!tf_type.string>}> : () -> tensor<1x!tf_type.string>