// hardware performs better with integer ops.
  // Default value: true
  optional bool unpack_quantized_types = 1;

  // When set to True, requantize op in the quantized fusion will merge with the
  // subsequent dequantize op if present.
  // Default value: false
  // TODO: b/321729008 - re-consider default value after testing on prod model.
  bool merge_fusion_with_dequantize = 2;
}

  }
};

// stablehlo.uniform_dequantize -> tfl.dequantize
class RewriteUniformDequantizeOp
    : public OpRewritePattern<stablehlo::UniformDequantizeOp> {
  using OpRewritePattern<stablehlo::UniformDequantizeOp>::OpRewritePattern;

  // Determines whether the input and output types are compatible with
  // `tfl.dequantize`. See the definition for the `DEQUANTIZE` kernel for the
  // detailed limitations

// 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]])

// 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>> {

// 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>

// 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,

  %2 = "tfl.dequantize"(%1#0) : (tensor<2x!quant.uniform<u8:f32, 1.0>>) -> tensor<2xf32>
  %3 = "tfl.dequantize"(%1#1) : (tensor<2x!quant.uniform<u8:f32, 1.0>>) -> tensor<2xf32>

  // unused quantization ops should be removed as well.
  %4 = "tfl.dequantize"(%1#2) : (tensor<2x!quant.uniform<u8:f32, 1.0>>) -> tensor<2xf32>

// 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-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-dynamic-range -tfl-quantize="enable-dynamic-range-quantization=true" | FileCheck %s
// RUN: tf-opt %s -tfl-prepare-quantize-dynamic-range -tfl-quantize="enable-dynamic-range-quantization=true enable-weight-only-quantization=true" | FileCheck --check-prefix=PerChannelWeightOnly %s