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

func.func @main() {
  tf_executor.graph {
    tf_executor.island wraps "tf.NoOp"() {} : () -> () loc("noop")
    tf_executor.fetch
  }
  func.return
}

// CHECK: node {
// CHECK-NEXT:  name: "noop"
// CHECK-NEXT:  op: "NoOp"
// CHECK-NEXT:  experimental_debug_info {
// CHECK-NEXT:  }

// RUN: tf-opt %s -tf-replicated-clustering-bridge-v2 -tfrt-lower-cluster-to-runtime-ops-tpu 2>&1 | FileCheck %s
// RUN: tf-opt %s -tf-replicated-clustering-bridge-v1 -tfrt-lower-cluster-to-runtime-ops-tpu -tf-dialect-to-executor-v1 2>&1 | FileCheck %s

// This test verifies there is no warning about shape inference failure in TPU
// bridge in handling multiple usage of the same function.

// Since it is possible that this warning may become an error in the future,

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

// CHECK: entry function `main` must be present

func.func @const() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {device = "TPU:0", name = "const", dtype = "tfdtype$DT_INT32", value = dense<[1, 2]> : tensor<2xi32>} : () -> tensor<2xi32>
    tf_executor.fetch
  }
  func.return

// RUN: not tf-mlir-translate -mlir-tf-to-hlo-text %s -tf-input-shapes=128,10 -tf-xla-emit-use-tuple-args -tf-xla-emit-return-tuple 2>&1 | FileCheck %s

module attributes {tf.versions = {producer = 179 : i32}} {
  func.func @main(%arg0: tensor<128x8xf32> {mhlo.sharding = "bad_sharding"}) {
    func.return
  }
}

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

// CHECK-LABEL: main
func.func @main() -> tensor<3x2xi32> {
  // CHECK: "tfl.pseudo_const"() <{value = dense<0> : tensor<3x2xi32>}> {tfl.is_variable} : () -> tensor<3x2xi32>
  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<3x2xi32>, tfl.is_variable} : () -> tensor<3x2xi32> loc("variable")
  func.return %0 : tensor<3x2xi32>

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -tf-prune-unused-nodes -tf-input-arrays=z:1,z:2 -tf-input-shapes=':' -tf-output-arrays=z:2,z:1,a:0 -o - | FileCheck --check-prefix=PRUNE %s

// RUN: tf-opt "--tfxla-device-specific-transforms=device-type=XLA_CPU_JIT" -verify-diagnostics -split-input-file %s | FileCheck -dump-input=fail %s

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 1399 : i32}} {

// CHECK-LABEL: stateless_op
func.func @stateless_op() -> tensor<i32> {
  // CHECK: %cst = "tf.Const"() <{value = dense<1> : tensor<i32>}> : () -> tensor<i32>

// RUN: not tf-mlir-translate -mlir-tf-str-attr-to-mlir %s 2>&1 | FileCheck %s

"builtin.totally @invalid MLIR module {here} <-"

// RUN: tf-opt %s -tf-standard-pipeline | FileCheck %s

// CHECK-LABEL: removeDeadReadVariableOp
func.func @removeDeadReadVariableOp(%arg0: tensor<!tf_type.resource<tensor<f32>>>) -> tensor<f32> {
  %0 = "tf.ReadVariableOp"(%arg0) : (tensor<!tf_type.resource<tensor<f32>>>) -> tensor<f32>
  %1 = "tf.ReadVariableOp"(%arg0) : (tensor<!tf_type.resource<tensor<f32>>>) -> tensor<f32>
  func.return %0: tensor<f32>

# limitations under the License.
# ==============================================================================
"""Test file to display the error message and verify it with FileCheck."""

# RUN: %p/concrete_function_error | FileCheck %s

import sys

from absl import app

import tensorflow.compat.v2 as tf
if hasattr(tf, 'enable_v2_behavior'):
  tf.enable_v2_behavior()