// RUN: tf-opt %s -tfl-prepare-quantize="quantize-signed=true" | FileCheck %s
// RUN: tf-opt %s -tfl-prepare-quantize="quantize-signed=true disable-per-channel=true" | FileCheck --check-prefix=PerTensor %s

// CHECK-LABEL: uint8_to_int8
func.func @uint8_to_int8(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
  %1 = "tfl.quantize"(%arg0) {qtype = tensor<2x2x!quant.uniform<u8:f32, 1.0:128>>} : (tensor<2x2xf32>) -> tensor<2x2x!quant.uniform<u8:f32, 1.0:128>>

// RUN: tf-opt "-xla-legalize-tf=device-type=XLA_CPU_JIT legalize-chlo=false use-tf2xla-fallback=true prefer-tf2xla=true" %s | FileCheck %s
// RUN: tf-opt "-xla-legalize-tf=device-type=XLA_CPU_JIT legalize-chlo=false prefer-tf2xla=true" %s | FileCheck --check-prefix NOFALLBACK %s

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

// CHECK-LABEL: @abs
func.func @abs(%arg0: tensor<2xf32>) -> tensor<2xf32> {

// RUN: tf-opt %s -tfl-post-quantize | FileCheck %s
// RUN: tf-opt %s -tfl-post-quantize-remove-qdq | FileCheck --check-prefix=QDQ %s

// CHECK-LABEL: RemoveUnused
// QDQ-LABEL: RemoveUnused
func.func @RemoveUnused(%arg0: tensor<4xf32>, %arg1: tensor<i32>) -> (tensor<2xf32>,tensor<2xf32>) {
  %0 = "tfl.quantize"(%arg0) {qtype = tensor<4x!quant.uniform<u8:f32, 1.0>>} : (tensor<4xf32>) -> tensor<4x!quant.uniform<u8:f32, 1.0>>

// RUN: odml-to-stablehlo-opt %s -unfuse-mhlo-batch-norm-pass -cse -verify-diagnostics | FileCheck %s

// CHECK-LABEL: @batchNormInference_2D_inner_features
// CHECK-SAME: %[[X:[^:[:space:]]+]]
// CHECK-SAME: %[[SCALE:[^:[:space:]]+]]
// CHECK-SAME: %[[OFFSET:[^:[:space:]]+]]
// CHECK-SAME: %[[MEAN:[^:[:space:]]+]]
// CHECK-SAME: %[[VARIANCE:[^:[:space:]]+]]
func.func @batchNormInference_2D_inner_features(

// RUN: tf-opt %s -allow-unregistered-dialect --tf-localize-var-handles --split-input-file | FileCheck %s

// CHECK-LABEL: module
module attributes {tf_saved_model.semantics} {
  "tf_saved_model.global_tensor"() { is_mutable, sym_name = "v", type = tensor<10xf32>, value = dense<[0.,1.,2.,3.,4.,5.,6.,7.,8.,9.]> : tensor<10xf32> } : () -> ()
  // CHECK-LABEL: @read_from_global

// RUN: tf-quant-opt %s -split-input-file -quant-add-dump-tensor-op='debugger_type=whole_model' | FileCheck --check-prefix=WholeModel %s
// RUN: tf-quant-opt %s -split-input-file -quant-add-dump-tensor-op='debugger_type=int_per_layer' | FileCheck --check-prefix=IntPerLayer %s
// RUN: tf-quant-opt %s -split-input-file -quant-add-dump-tensor-op='debugger_type=float_per_layer' | FileCheck --check-prefix=FloatPerLayer %s


module {

// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions='quantization-method=drq target-opset=UNIFORM_QUANTIZED' -quant-quantize-composite-functions='quantization-method=drq target-opset=UNIFORM_QUANTIZED' -symbol-dce | FileCheck %s

module {
  // TODO(b/260020937): Support transpose_a, transpose_b for matmul.
  func.func @matmul(%arg0: tensor<2x12xf32>) -> (tensor<*xf32>) {

// RUN: tf-opt %s -tfl-raise-custom-ops="test-raise-tf-targets=tf.FakeQuantWithMinMaxVarsPerChannel,tf.FakeQuantWithMinMaxVars" -tfl-prepare-tf=use-fake-quant-num-bits=true | FileCheck --check-prefix LOBIT --dump-input=always %s

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

module {
  func.func @conv_with_single_layer(%arg0: tensor<1x2x2x3xf32>) -> (tensor<*xf32>) {

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer --use-buffer-offset %s -o - | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - | FileCheck %s
// Ensure constants roundtrip exactly

func.func @bool() -> tensor<4xi1> {
  // CHECK-LABEL: @bool
  // CHECK: value = dense<[false, true, true, false]> : tensor<4xi1>
  %0 = "tfl.pseudo_const"() { value = dense<[false, true, true, false]> : tensor<4xi1> } : () -> tensor<4xi1>
  func.return %0 : tensor<4xi1>
}