Option<OpSet> op_set_{
      *this, "target-opset", llvm::cl::init(OpSet::TF),
      llvm::cl::desc("Choose target opset."),
      llvm::cl::values(
          clEnumValN(OpSet::TF, "TF",
                     "Uses TF ops that mimic quantization behavior"),
          clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"),
          clEnumValN(OpSet::UNIFORM_QUANTIZED, "UNIFORM_QUANTIZED",

 private:
  Option<OpSet> target_opset_{
      *this, "target-opset", llvm::cl::init(OpSet::TF),
      llvm::cl::desc("Choose target opset."),
      llvm::cl::values(
          clEnumValN(OpSet::TF, "TF",
                     "Uses TF ops that mimic quantization behavior"),
          clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"),
          clEnumValN(OpSet::UNIFORM_QUANTIZED, "UNIFORM_QUANTIZED",

  QuantizationMethod quantization_method = 6;

  reserved 1 to 4;
}

// List of supported opsets to deploy the quantized model.
// The quantized model contains different set of ops depending on the opset.
// NEXT ID: 5
enum OpSet {
  OP_SET_UNSPECIFIED = 0;  // go/do-include-enum-unspecified
  // Uses TF ops that mimic quantization behavior. Used when the corresponding

// RUN: tf-quant-opt %s -split-input-file -quant-preprocess-op='target-opset=XLA quantization-method=weight_only enable-per-channel-quantization=false' | FileCheck --check-prefix PerTensor %s
// RUN: tf-quant-opt %s -split-input-file -quant-preprocess-op='target-opset=XLA quantization-method=weight_only enable-per-channel-quantization=true' | FileCheck --check-prefix PerChannel %s

module {
  // For XLA weight-only per-channel depthwise convolution, tensor shape should have

// RUN: tf-quant-opt %s -quant-insert-quantized-functions='quantization-method=drq' | FileCheck %s
// RUN: tf-quant-opt %s -quant-insert-quantized-functions='quantization-method=drq target-opset=UNIFORM_QUANTIZED' | FileCheck --check-prefix=UQ-CHECK %s

// Empty module
module {
  func.func @simple_fn(%arg0: tensor<*xf32>) -> tensor<*xf32> {
    func.return %arg0 : tensor<*xf32>
  }
}

// CHECK-NOT: func private @internal_calculate_quant_params

// RUN: tf-quant-opt %s -split-input-file -quant-convert-fake-quant-to-qdq -quant-lift-quantizable-spots-as-functions='target-opset=XLA' -quant-insert-quantized-functions -quant-quantize-composite-functions='target-opset=XLA' -symbol-dce -inline -tf-shape-inference -canonicalize -quant-replace-cast-hacks-with-tf-xla-ops -cse -quant-optimize | FileCheck %s

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1219 : i32}, tf_saved_model.semantics} {

// RUN: tf-quant-opt %s -quant-insert-quantized-functions='quantization-method=weight_only target-opset=XLA' | FileCheck %s

// Empty module
module {
  func.func @simple_fn(%arg0: tensor<*xf32>) -> tensor<*xf32> {
    func.return %arg0 : tensor<*xf32>
  }
}

// CHECK-NOT: func private @internal_dequantize_f32
// CHECK-NOT: func private @internal_conv3d_fn
// CHECK-NOT: func private @internal_batch_matmul_fn

// RUN: tf-quant-opt %s -quant-insert-quantized-functions | FileCheck %s
// RUN: tf-quant-opt %s -quant-insert-quantized-functions='quantization-method=ptq target-opset=UNIFORM_QUANTIZED' --mlir-print-ir-after-all | FileCheck --check-prefix=UQ-CHECK %s

// Empty module
module {
  func.func @simple_fn(%arg0: tensor<*xf32>) -> tensor<*xf32> {
    func.return %arg0 : tensor<*xf32>
  }
}

// CHECK-NOT: func private @internal_rescale_fn

// RUN: tf-quant-opt %s -split-input-file -mlir-print-debuginfo -mlir-print-local-scope -quant-add-quantization-unit-loc -inline -quant-prepare-lifting -quant-lift-quantizable-spots-as-functions='target-opset=XLA' | FileCheck %s

// This file test the selective quantiation feature in TF Quantizer. In the test
// config, the op named "test_opt_out" will not be quantized.

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1269 : i32}} {

// RUN: tf-quant-opt %s -split-input-file -quant-lift-quantizable-spots-as-functions='target-opset=XLA' | FileCheck %s

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1269 : i32}, tf_saved_model.semantics} {