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

// CHECK: {
// CHECK:  version: 3,
// CHECK:  operator_codes: [ {
// CHECK:    deprecated_builtin_code: 32,
// CHECK:    custom_code: "HashTableV2",
// CHECK:    builtin_code: CUSTOM
// CHECK: } ],
// CHECK: subgraphs: [ {
// CHECK:   tensors: [ {
// CHECK:     shape: [  ],

// RUN: tf-opt %s -inline='default-pipeline=''' | FileCheck %s

// Inline a function that contains only tfl ops.
func.func @func_with_tfl_ops(%arg0 : tensor<2xi32>) -> tensor<2xi32> {
  %0 = "tfl.sub"(%arg0, %arg0) {fused_activation_function = "RELU6"} : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  %1 = "tfl.add"(%0, %arg0) {fused_activation_function = "RELU6"} : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  func.return %1: tensor<2xi32>
}

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -o - | FileCheck %s

node {
  name: "Constant"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT32
        tensor_shape {
        }
        int_val: 0
      }
    }
  }
}
node {
  name: "_tf.foo"
  op: "foo"

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -tf-prune-unused-nodes -tf-input-arrays=input0,input1 -tf-input-data-types=DT_INT32,DT_INT32 -tf-input-shapes=10:10 -tf-output-arrays=Add -o - | FileCheck %s

# Verify that an unused Node (here named "Prune") isn't converted when we
# request pruning on import.
# CHECK-LABEL:  func @main
# CHECK-NOT:  Prune
# CHECK-NOT:  unused_input

node {
  name: "Prune"
  op: "Const"

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

// Check that attributes that define derived shapes are exported.

// CHECK: op: "PlaceholderWithDefault"
// CHECK: shape
// CHECK: unknown_rank: true
// CHECK: name: "static"
// CHECK: op: "PlaceholderWithDefault"
// CHECK: shape {
// CHECK-NEXT: }
// CHECK: name: "static_10"
// CHECK: op: "PlaceholderWithDefault"
// CHECK: shape
// CHECK: dim
// CHECK: size: 10

//RUN: tf_tfl_translate --enable-stablehlo-conversion --input-mlir %s -o - | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - | FileCheck %s


module {
func.func @tfInplaceUpdate(%arg0: tensor<2x1x2xf32>) -> tensor<2x1x2xf32> {
  %1 = arith.constant dense<1> : tensor<1xi32>
  %2 = arith.constant dense<2.0> : tensor<1x1x2xf32>
  %3 = "tf.InplaceUpdate"(%arg0, %1, %2) {device = ""}
    : (tensor<2x1x2xf32>, tensor<1xi32>, tensor<1x1x2xf32>) -> tensor<2x1x2xf32>

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - | FileCheck %s
// Confirm function references in if ops are preserved
func.func @main(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
// CHECK:   %{{.*}} = "tf.If"(%{{.*}}, %{{.*}}, %{{.*}}) <{else_branch = @cond_false, is_stateless = false, then_branch = @cond_true}> : (tensor<1xi1>, tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

// RUN: tf-opt %s -pass-pipeline='builtin.module(func.func(canonicalize))' | FileCheck %s

// Test that a constant stays inside an island after canonicalization

// CHECK-LABEL: func @constant_in_island
func.func @constant_in_island(%arg0 : tensor<i1>) -> tensor<f32> {
  %0 = tf_executor.graph {
// CHECK: tf_executor.island
// CHECK: tf.Const{{.*}}2.0
    %1:2 = tf_executor.island {

// 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"})

# RUN: tf_tfl_translate -tf-input-arrays=input -tf-input-shapes=4 -tf-input-data-types=DT_INT32 -tf-output-arrays=output %s -o - --output-mlir | FileCheck %s

node {
  name: "default"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT32
        tensor_shape {
        }
        int_val: 0
      }
    }
  }
}