// RUN: tf-opt %s -tf-device-decompose-resource-ops | FileCheck -check-prefixes=CHECK,ALWAYS-DECOMPOSE %s
// RUN: tf-opt %s -tf-device-decompose-resource-ops-in-cluster | FileCheck -check-prefixes=CHECK,CLUSTER-ONLY %s

// Tests different behavior of two passes if the op is outside cluster
// tf-device-decompose-resource-ops would always decompose the resource op.
// tf-device-decompose-resource-ops-in-cluster would not decompose ops outside

// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions -quant-quantize-composite-functions='target-opset=XLA' | FileCheck --check-prefix=XLA %s
// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions -quant-quantize-composite-functions='target-opset=XLA enable-per-channel-quantization=true' | FileCheck --check-prefix=XLA_PerChannel %s

module {

// RUN: tac-opt-all-backends -tfl-raise-target-subgraphs %s -split-input-file | FileCheck %s
// RUN: tac-opt-all-backends -tfl-raise-target-subgraphs="skip-raise-cpu-ops=true" %s -split-input-file | FileCheck %s --check-prefixes=CHECK-SKIP-CPU
// RUN: tac-opt-all-backends -tfl-raise-target-subgraphs="ignore-inference-type=true" %s -split-input-file | FileCheck %s --check-prefixes=CHECK-IGNORE-INFERENCE-TYPE

module {
func.func @simpleWhile(%arg0: tensor<i32>) -> tensor<i32> {

// RUN: tf-opt -tfl-prepare-tf %s | FileCheck %s
// RUN: tf-opt %s -tf-layout-optimization=force-data-format=NHWC -tfl-prepare-tf  | FileCheck --check-prefix=LAYOUT --dump-input=always %s

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {

// RUN: stablehlo-quant-opt %s -split-input-file -verify-diagnostics \
// RUN:     -stablehlo-quantize-composite-functions | FileCheck %s
// RUN: stablehlo-quant-opt %s -split-input-file -verify-diagnostics \
// RUN:     -stablehlo-quantize-composite-functions=enable-per-channel-quantized-weight=false | FileCheck --check-prefix=CHECK-PER-TENSOR %s

// Tests that basic dot_general is properly quantized.

module attributes {tf_saved_model.semantics} {

// RUN: tf-opt %s -split-input-file -verify-diagnostics -tf-tpu-cluster-formation | FileCheck %s


// Test ops in cluster only have `_replication_info` and `device` attributes
// removed when moved to a `tf_device.cluster`.
// CHECK-LABEL: func @cluster_ops_removed_attrs
func.func @cluster_ops_removed_attrs() {

// RUN: stablehlo-quant-opt %s -split-input-file -stablehlo-lift-quantizable-spots-as-functions | FileCheck %s

// CHECK-LABEL: @conv_fn(
// CHECK-SAME:          %[[ARG_0:.*]]: tensor<1x3x3x4xf32>
func.func @conv_fn(%arg0: tensor<1x3x3x4xf32>) -> tensor<1x3x3x4xf32> {
  %0 = stablehlo.constant dense<2.000000e+00> : tensor<3x3x4x4xf32>

// RUN: tf-opt %s -split-input-file -verify-diagnostics -tf-tensor-array-ops-decomposition | FileCheck %s

// Test read and write on a tensor list.

// CHECK-LABEL: func @main
func.func @main() -> tensor<3xf32> {
  %size = "tf.Const"() {value = dense<5> : tensor<i32>} : () -> tensor<i32>
  // CHECK: %[[BUFFER:.*]] = "tf.BroadcastTo"
  // CHECK-SAME: -> tensor<5x3xf32>

// RUN: tf-opt -split-input-file -verify-diagnostics -tf-unroll-batch-matmul %s | FileCheck %s

//==== V1 tests ====

func.func @batchMatMulTwoDim(%arg0: tensor<2x3x4x5xf32>, %arg1: tensor<2x3x5x6xf32>) -> tensor<2x3x4x6xf32> {
  %0 = "tf.BatchMatMul"(%arg0, %arg1) : (tensor<2x3x4x5xf32>, tensor<2x3x5x6xf32>) -> tensor<2x3x4x6xf32>
  func.return %0 : tensor<2x3x4x6xf32>

  // CHECK-LABEL: batchMatMulTwoDim

// RUN: tf-opt %s -pass-pipeline='builtin.module(tf-executor-convert-control-to-data-outputs{composite-tpuexecute-side-effects})' -split-input-file -verify-diagnostics | FileCheck %s

!tf_res = tensor<!tf_type.resource<tensor<f32>>>

// Tests independent chains of two resources.

// CHECK-LABEL: func @simple_independent_chains_while_body