#include "tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.pb.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/einsum.h"

namespace mlir {
namespace quant {
namespace {

using ::tensorflow::quantization::OpSet;

class PrepareLiftingPass
    : public PassWrapper<PrepareLiftingPass, OperationPass<func::FuncOp>> {
 public:

    func.return %5 : tensor<*xi32>
  }

   // Einsum with int32 accumulation.
  func.func private @internal_einsum_fn(
                         %input : tensor<*xi8>, %weight : tensor<*xi8>,
                         %input_scale : tensor<*xf32>, %input_zp : tensor<*xi32>,
                         %weight_scale : tensor<*xf32>, %weight_zp : tensor<*xi32>) -> tensor<*xi32> {

    # via
    #   -r requirements.in
    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
opt-einsum==3.3.0 \
    --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \
    --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549
    # via

    # via
    #   -r requirements.in
    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
opt-einsum==3.3.0 \
    --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \
    --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549
    # via

// CHECK: Number of dequantize layers added: 1
}


// -----

module {
  func.func @float_einsum(%arg0: tensor<?x64x32xf32>, %arg1: tensor<32x2x16xf32>) -> (tensor<?x64x2x16xf32>) {
    %0 = "tf.Einsum"(%arg0, %arg1) {equation = "abc,cde->abde"} : (tensor<?x64x32xf32>, tensor<32x2x16xf32>) -> tensor<?x64x2x16xf32>
    func.return %0 : tensor<?x64x2x16xf32>
  }

// CHECK-LABEL: func @float_einsum

    # via
    #   -r requirements.in
    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
opt-einsum==3.3.0 \
    --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \
    --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549
    # via

    # via
    #   -r requirements.in
    #   h5py
    #   jax
    #   keras-nightly
    #   ml-dtypes
    #   opt-einsum
    #   scipy
    #   tb-nightly
opt-einsum==3.3.0 \
    --hash=sha256:2455e59e3947d3c275477df7f5205b30635e266fe6dc300e3d9f9646bfcea147 \
    --hash=sha256:59f6475f77bbc37dcf7cd748519c0ec60722e91e63ca114e68821c0c54a46549
    # via

        "decompose_reduce_dataset.cc",
        "decompose_resource_ops_pass.cc",
        "device_attribute_to_launch.cc",
        "device_index_selector.cc",
        "drop_while_shape_invariant.cc",
        "einsum.cc",
        "executor_island_coarsening.cc",
        "executor_tpuv1_inline_tpu_island.cc",
        "executor_tpuv1_island_coarsening.cc",
        "executor_tpuv1_outline_tpu_island.cc",

      }
      return HashCode.fromBytesNoCopy(bytes);
    }

    @Override
    public int bits() {
      int bitSum = 0;
      for (HashFunction function : functions) {
        bitSum += function.bits();
      }
      return bitSum;
    }

    @Override
    public boolean equals(@CheckForNull Object object) {
      if (object instanceof ConcatenatedHashFunction) {

    zip(this, suffix) { _, s -> s }


@JvmName("timesUnitUnit")
internal
inline operator fun Parser<Unit>.times(crossinline suffix: Parser<Unit>): Parser<Unit> =
    zipM(this, suffix) { _, _ -> unitSuccess }


@JvmName("timesTUnit")
internal
inline operator fun <T> Parser<T>.times(crossinline suffix: Parser<Unit>): Parser<T> =
    zip(this, suffix) { p, _ -> p }