%0 = mhlo.constant dense<(1.000000e+00,-1.000000e+00)> : tensor<128x32x4xcomplex<f32>>
  %1 = mhlo.constant dense<(1.000000e+00,1.000000e+00)> : tensor<8x64x128xcomplex<f32>>
  %2 = "mhlo.einsum"(%1, %0) <{einsum_config = "abc,cde->abde"}> : (tensor<8x64x128xcomplex<f32>>, tensor<128x32x4xcomplex<f32>>) -> tensor<8x64x32x4xcomplex<f32>>
  return %2 : tensor<8x64x32x4xcomplex<f32>>
}

// -----

  func.return %0 : tensor<?x2x4x5xf32>
}

// CHECK: func @xla_dot_v2
// CHECK: %[[einsum:.*]] = "tf.Einsum"(%arg0, %arg1) <{equation = "abc,cde->abde"}> : (tensor<?x2x3xf32>, tensor<3x4x5xf32>) -> tensor<?x2x4x5xf32>
// CHECK: return %[[einsum]] : tensor<?x2x4x5xf32>

// -----

// dimension_numbers: {
//   offset_dims: 0
//   collapsed_slice_dims: 1

          func_aliases.values(), [quantize_model_test_base.FUNC_ALIAS]
      )

  @parameterized.parameters(
      testing.parameter_combinations([{
          'equation': (
              'abc,cde->abde',
              'abc,dce->abde',
          ),
      }])
  )
  def test_einsum_ptq_model(
      self,
      equation: str,
  ):
    _, y_shape, bias_shape, x_signature, y_signature = (

}


// -----

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
// CHECK: -------- Quantization Summary --------

        input_tensor=ops.convert_to_tensor(input_data)
    )

    self.assertAllClose(expected_outputs, got_outputs, atol=0.05)

  @parameterized.parameters(
      ('abc,cde->abde', quant_opts_pb2.XLA),
      ('abc,dce->abde', quant_opts_pb2.XLA),
  )
  def test_einsum_ptq_model(
      self,
      equation: str,
      target_opset: quant_opts_pb2.OpSet,
  ):

func InvertBranch(as obj.As) obj.As {
	switch as {
	case ABEQ:
		return ABNE
	case ABEQZ:
		return ABNEZ
	case ABGE:
		return ABLT
	case ABGEU:
		return ABLTU
	case ABGEZ:
		return ABLTZ
	case ABGT:
		return ABLE
	case ABGTU:
		return ABLEU
	case ABGTZ:
		return ABLEZ
	case ABLE:
		return ABGT
	case ABLEU:
		return ABGTU
	case ABLEZ:
		return ABGTZ

	asmeq, invasmun bool
}{
	ssa.BlockPPC64EQ: {ppc64.ABEQ, ppc64.ABNE, false, false},
	ssa.BlockPPC64NE: {ppc64.ABNE, ppc64.ABEQ, false, false},

	ssa.BlockPPC64LT: {ppc64.ABLT, ppc64.ABGE, false, false},
	ssa.BlockPPC64GE: {ppc64.ABGE, ppc64.ABLT, false, false},
	ssa.BlockPPC64LE: {ppc64.ABLE, ppc64.ABGT, false, false},
	ssa.BlockPPC64GT: {ppc64.ABGT, ppc64.ABLE, false, false},

	ASRAI
	ALUI
	AAUIPC
	AADD
	ASLT
	ASLTU
	AAND
	AOR
	AXOR
	ASLL
	ASRL
	ASUB
	ASRA

	// 2.5: Control Transfer Instructions
	AJAL
	AJALR
	ABEQ
	ABNE
	ABLT
	ABLTU
	ABGE
	ABGEU

	// 2.6: Load and Store Instructions
	ALW
	ALWU
	ALH
	ALHU
	ALB
	ALBU
	ASW
	ASH
	ASB

	// 2.7: Memory Ordering Instructions
	AFENCE

package cmd

import (
	"context"
	"os"
	"path/filepath"
	"testing"

	"github.com/google/uuid"
)

var testUUID = uuid.MustParse("f5c58c61-7175-4018-ab5e-a94fe9c2de4e")

func BenchmarkCrcHash(b *testing.B) {
	cases := []struct {
		key int
	}{
		{16},
		{64},
		{128},
		{256},
		{512},
		{1024},
	}
	for _, testCase := range cases {

	MULHDU R9, R6
	MOVD   (R2)(R1*1), R10
	ADDC   R10, R11        // add to low order bits
	ADDE   R0, R6
	ADDC   R4, R11
	ADDE   R0, R6
	MOVD   R6, R4
	MOVD   R11, (R2)(R1*1)

	MOVD   (8)(R8)(R1*1), R6
	MULHDU R9, R6
	MOVD   (8)(R2)(R1*1), R10
	ADDC   R10, R11           // add to low order bits
	ADDE   R0, R6
	ADDC   R4, R11
	ADDE   R0, R6
	MOVD   R6, R4
	MOVD   R11, (8)(R2)(R1*1)

	ADD $16, R1 // i*8 + 8