limitations under the License.
==============================================================================*/
#include "tensorflow/compiler/mlir/quantization/tensorflow/ops/tf_quantize_op.h"

#include <functional>
#include <optional>

#include "absl/strings/str_cat.h"
#include "absl/strings/str_join.h"
#include "absl/types/optional.h"

// "qtype" attribute, we should update the type shape in this attribute.
def ReorderTransposeDequantQuant :
      Pat<(TF_TransposeOp:$old_value
              (TFL_DequantizeOp (TFL_QuantizeOp $input, $qtype)), $perm),
          (TFL_DequantizeOp (TFL_QuantizeOp
                                (TF_TransposeOp $input, $perm),
                                (UpdateShapeWithAxis<-1> $qtype, $old_value))),

// squashed to a requantize op if the scales are different.
def : Pat<(TFL_QuantizeOp (TFL_DequantizeOp $in), $qt), (replaceWithValue $in)>;

// If the tfl.dequantize op wasn't fused, we shouldn't quantize the floating
// point constant.
def : Pat<(TFL_DequantizeOp
             (TFL_QuantizeOp (Arith_ConstantOp F32ElementsAttr:$cst), $qt)),
          (TFL_ConstOp $cst)>;

                             (TFL_ExpandDimsOp $input, $dim)>;

def LegalizeFakeQuantToDequantizeQuantize : Pat<
  (TF_FakeQuantWithMinMaxArgsOp $inputs, $min, $max, $num_bits, $narrow_range),
  (TFL_DequantizeOp
   (TFL_QuantizeOp $inputs,
                   (ConvertToQuantTypeFromAttrs $inputs, $min, $max,
                    $num_bits, $narrow_range)))>;

def LegalizeFill : Pat<(TF_FillOp $arg, $value), (TFL_FillOp $arg, $value)>;

// incorrect placement in the quantization aware training.
def MatchHardSwishQuantized : Pat<
  (TFL_MulOp (TFL_DequantizeOp (TFL_QuantizeOp
    (TFL_MulOp
     $x, (TFL_DequantizeOp (TFL_QuantizeOp (TFL_AddOp
          $x,
          (Arith_ConstantOp ConstantAttr<RankedF32ElementsAttr<[]>, "3.0f">),
          TFL_AF_Relu6), $qattr2)),
     TFL_AF_None), $qattr1)),

// def UndoBroadcastFullyConnectedBiasAddWithQDQs : Pat<
//   (TFL_AddOp $lhs,
//     (TFL_DequantizeOp
//       (TFL_QuantizeOp
//         (Arith_ConstantOp:$const_op $bias),
//       $qparams)),
//   $act_fn),
//   (TFL_AddOp $lhs,
//     (TFL_DequantizeOp
//       (TFL_QuantizeOp
//         (Arith_ConstantOp:$const_op (FlattenTo1D $bias),
//       $qparams)),
//   $act_fn),

      $_state.addAttribute("value", value);
      $_state.addAttribute("s_param", s_param);
      $_state.addAttribute("compressed_data", compressed_data);
    }]>
  ];
}

def TFL_QuantizeOp: TFL_Op<"quantize", [
    FirstAttrDerivedResultType,
    SameOperandsAndResultShape, NoMemoryEffect]> {
  let summary = "Quantize operator";

  let description = [{

  );

  let results = (outs
    TF_FloatTensor:$output
  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_QuantizeV2Op : TF_Op<"QuantizeV2", [Pure]> {
  let summary = [{
Quantize the 'input' tensor of type float to 'output' tensor of type 'T'.
  }];

  let description = [{