// RUN: stablehlo-quant-opt %s -stablehlo-merge-fusion-with-dequantize -split-input-file -verify-diagnostics | FileCheck %s

// Merge fusion with dequantize for relu case.

module attributes {tf_saved_model.semantics} {
  // CHECK-LABEL: func.func private @merge_relu_fusion
  func.func private @merge_relu_fusion(%arg0: tensor<1x4xf32>) -> tensor<1x3xf32> {

// MixedPrecision-NEXT: %[[q:.*]] = "tfl.quantize"(%arg0)
// MixedPrecision-NEXT: %[[dq:.*]] = "tfl.dequantize"(%[[q]])
// MixedPrecision-NEXT: %[[q_0:.*]] = "tfl.quantize"(%arg1)
// MixedPrecision-NEXT: %[[dq_0:.*]] = "tfl.dequantize"(%[[q_0]])
// MixedPrecision-NEXT: %[[c:.*]] = "tfl.concatenation"(%[[dq]], %[[dq_0]])
// MixedPrecision-NEXT: %[[q_1:.*]] = "tfl.quantize"(%[[c]])

// CHECK-DAG: %[[input_11:.*]] = "tfl.dequantize"({{.*}}) : (tensor<3x!quant.uniform<i32:f32, 4.8060645560249487E-8>>) -> tensor<3xf32>
// CHECK-DAG: %[[input_12:.*]] = "tfl.dequantize"({{.*}}) : (tensor<3x!quant.uniform<i32:f32, 7.2090970130772759E-8>>) -> tensor<3xf32>

// RUN: tf-opt %s -tfl-prepare-quantize="quantize-signed=true post-training-quantize=true" -cse | FileCheck %s
// RUN: tf-opt %s -tfl-prepare-quantize="quantize-signed=true post-training-quantize=true legacy-float-scale=true" -cse| FileCheck --check-prefix=Legacy %s

// CHECK-LABEL: QuantizeLstmCellInput
func.func @QuantizeLstmCellInput(%arg0: tensor<1x28x28xf32>) -> tensor<1x28x20xf32> {
    %cst_2 = "tfl.no_value"() {value = unit} : () -> none

// RUN: tf-opt %s -tfl-prepare-quantize="post-training-quantize=true" -tfl-quantize="numeric-verify=true log-if-failed=true" | FileCheck --check-prefix=DEBUG %s
// RUN: tf-opt %s -tfl-prepare-quantize="post-training-quantize=true" -tfl-quantize="numeric-verify=true log-if-failed=true whole-model-verify=true" | FileCheck --check-prefix=MODEL-DEBUG %s

// DEBUG-LABEL: QuantizeConv2D

// CHECK: %[[QUANTIZE:.*]] = "tfl.quantize"(%[[CST]]) <{qtype = tensor<1x32x42x128x!quant.uniform<i8<-127:127>:f32:0, {1.000000e+00}>>}> {volatile}
// CHECK: %[[DEQUANTIZE:.*]] = "tfl.dequantize"(%[[QUANTIZE]]) : (tensor<1x32x42x128x!quant.uniform<i8<-127:127>:f32:0, {1.000000e+00}>>) -> tensor<1x32x42x128xf32>
// CHECK: "tfl.transpose_conv"(%arg1, %[[DEQUANTIZE]], %arg0,

// RUN: tf-opt %s -tfl-prepare-quantize -tfl-quantize  | FileCheck %s
// RUN: tf-opt %s -tfl-quantize="legacy-quantize=true" | FileCheck --check-prefix=LEGACY %s
// RUN: tf-opt %s -tfl-prepare-quantize -tfl-quantize="ops-blocklist=tfl.fully_connected,tfl.softmax locs-blocklist=Block,NullBlock" | FileCheck --check-prefix=BLOCK %s

// CHECK-LABEL: QuantizeFloatConst
func.func @QuantizeFloatConst() -> tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>> {

  // CHECK: %[[DQ_1:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
  // CHECK: %[[DQ_2:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
  // CHECK: %[[DQ_3:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
  // CHECK: %[[DQ_4:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
  // CHECK: %[[DQ_5:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>

    %0 = "tfl.dequantize"(%arg0) : (tensor<1x224x224x3x!quant.uniform<u8:f32, 1.0>>) -> tensor<1x224x224x3xf32>
    %1 = "tfl.dequantize"(%arg1) : (tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 1.0>>) -> tensor<32x3x3x3xf32>
    %2 = "tfl.dequantize"(%arg2) : (tensor<32x!quant.uniform<i32:f32, 1.0>>) -> tensor<32xf32>

    // dequantize ops, and insert them between the tf.FakeQuantWithMinMaxVarsOp
    // and its users.
    Value value = tf_op.getOutputs();
    auto quantize = rewriter.create<TFL::QuantizeOp>(
        tf_op.getLoc(), qtype.getValue(), value, qtype);
    auto dequantize = rewriter.create<TFL::DequantizeOp>(
        tf_op.getLoc(), res_type, quantize.getOutput());