// RUN: tf-opt %s -tfl-prepare-quantize="quantize-allowlist=quantize_float_placeholder_only,not_reset_input" | FileCheck %s
// RUN: tf-opt %s -tfl-prepare-quantize="disable-set-input-nodes-quantization-params=true" | FileCheck --check-prefix=MixedPrecision %s
// RUN: tf-opt %s -tfl-prepare-quantize="is-qdq-conversion=true" | FileCheck --check-prefix=QDQ %s

// CHECK-LABEL: main
// Uses `main` function to match the default target function of QuantSpecs and

// 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

// %9 = stablehlo.constant  // Input 3 zero point z3.
// %10 = stablehlo.constant  // s1 * s2.
// %11 = call @uniform_quantize(%0, %2, %3)  // Quantize input (q1).
// %12 = call @uniform_quantize_0(%1, %5, %6)  // Quantize input (q2).
// %13 = stablehlo.convert %11  // i8->i32 cast for q1.
// %14 = stablehlo.convert %3  // [Optional] i8->i32 cast for z1.

// CHECK:  return %[[RESULT]]
}

func.func @QDQsFollowedByTranspose(tensor<1x2xf32>) -> (tensor<2x1xf32>) {
^bb0(%arg0: tensor<1x2xf32>):
  %cst_0 = arith.constant dense<[1, 0]> : tensor<2xi32>
  %0 = "tfl.quantize"(%arg0){qtype = tensor<1x2x!quant.uniform<u8:f32, 1.0>>}: (tensor<1x2xf32>) -> (tensor<1x2x!quant.uniform<u8:f32, 1.0>>)
  %1 = "tfl.dequantize"(%0): (tensor<1x2x!quant.uniform<u8:f32, 1.0>>) -> (tensor<1x2xf32>)

    // This is the argument used to refer to the pass in
    // the textual format (on the commandline for example).
    return "quant-quantize-composite-functions";
  }

  StringRef getDescription() const final {
    // This is a brief description of the pass.
    return "Quantize composite functions with QDQ input/outputs.";
  }

  void getDependentDialects(DialectRegistry& registry) const override {

    readonly_model_ = input_model_->GetModel();
    model_ = UnPackFlatBufferModel(*readonly_model_);
  }
};

TEST_F(QuantizeLSTM2Test, VerifyLSTM) {
  // Quantize model.
  auto status = QuantizeModelAllOperators(
      &model_, TensorType_FLOAT32, TensorType_FLOAT32,
      /*allow_float=*/false, TensorType_INT8, output_buffer_);
  ASSERT_THAT(status, Eq(kTfLiteOk));

// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions -quant-quantize-composite-functions | FileCheck --check-prefix=TF %s
// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions -quant-quantize-composite-functions='target-opset=XLA' | FileCheck --check-prefix=XLA %s

      return emitError(loc, type_or_err.status().ToString()),
             type_or_err.status();
    }
    auto type = std::move(type_or_err).value();

    if (op_name == "tfl.quantize") {
      // Special case for quantize: return type must also be in qtype attribute
      op_state.addAttribute("qtype", mlir::TypeAttr::get(type));
    } else if (op_name == "tfl.reshape" && op_state.operands.size() == 1) {

foreach BinaryOp = [TFL_DivOp, TFL_MulOp]<Op> in
  defm : FuseMulOrDivWithConv2dOrDepthwiseConv2d<BinaryOp>;


// This pattern applies when the same quantize/dequantize have been used twice
// with the same scale. We want to remove the redundancy.
// TODO(fengliuai): move this to the sanity check of pre-quantize pass.
def eliminate_dq_q_pairs : Pat<
  (TFL_QuantizeOp (TFL_DequantizeOp $in), $qt),
  (replaceWithValue $in),

        "transforms/post_quantize.cc",
        "transforms/prepare_quantize.cc",
        "transforms/prepare_quantize_dynamic_range.cc",
        "transforms/prepare_quantize_helper.cc",
        "transforms/quantize.cc",
        "transforms/quantize_variables.cc",
        "utils/generated_op_quant_spec_getters.inc",
    ],
    hdrs = [
        "transforms/passes.h",
        "transforms/prepare_quantize_helper.h",
    ],