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

# Verify that the data_format attributes is pulled from the default value in the
# registry when not present in the GraphDef
# CHECK:  tf.Conv2D
# CHECK-SAME: data_format = "NHWC"

# Verify that we don't import derived attributes as these will be added only on
# export.
# CHECK: tf.MaxPool
# CHECK-NOT: T = f32

// 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>
}

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

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

// (unlike the rest), since this is the primary use case for such ops and
// verification of shapes and broadcasts is desired.
// RUN: tf-opt "-xla-legalize-tf=legalize-chlo=true" -canonicalize %s | FileCheck %s
// RUN: tf-opt "-xla-legalize-tf=legalize-chlo=false" %s | FileCheck --check-prefix CHLO %s

//===----------------------------------------------------------------------===//
// Binary op legalizations.

// RUN: stablehlo-quant-opt %s -split-input-file -verify-diagnostics -stablehlo-test-tf-to-stablehlo | FileCheck %s

// TODO(b/330759552): Fix the msan issue and enable this test.
// func.func @fused_batchnorm_no_training() -> tensor<1x1x2x8xf32> {
//   %cst_0 = "tf.Const"() {value = dense<[[[[0.1, 0.2, 0.1, 0.2, 0.1, 0.2, 0.1, 0.2], [0.3, 0.4, 0.3, 0.4, 0.3, 0.4, 0.3, 0.4]]]]> : tensor<1x1x2x8xf32>} : () -> tensor<1x1x2x8xf32>

node {
  name: "input"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "shape"
    value {
      shape {

node {
  name: "input"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "shape"
    value {
      shape {

// RUN: tf-opt %s -tfl-quantize-variables | FileCheck %s
// RUN: tf-opt %s -tfl-prepare-quantize -tfl-quantize -tfl-post-quantize -tfl-quantize-variables -tfl-quantize -tfl-post-quantize | FileCheck --check-prefix=WHOLE-PASSES %s

// CHECK-LABEL: QuantizeReadVariable
func.func @QuantizeReadVariable() -> (tensor<1x2x1x3x!quant.uniform<i8:f32, 1.0>>) {
  %1 = "tfl.var_handle"() : () -> tensor<!tf_type.resource>