}

struct EinsumDimensionNumbers {
  // Each field contains the list of dimensions appearing only in the specifed
  // arguments of the einsum op with natural ordering. For example `rhs_out`
  // contains the dimensions appearing in the RHS and the OUTPUT of the einsum
  // but not in the LHS.
  std::vector<int64_t> lhs;
  std::vector<int64_t> rhs;
  std::vector<std::tuple<int64_t, int64_t>> lhs_rhs;

### Format

The format for ADR should follow this template:

```markdown
# ADR-000X - Title

## Date

20YY-MM-DD

## Context

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna
aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.

                    "\""# func_name #"\", $0...)", returns>;

// Add the second argument to the first argument, which is expected to be an
// argument list.
// bias(einsum(inputs), bias) --> einsum_with_bias(AppendToVector(inputs, bias))
// Since inputs is a vector in case of einsum, we cannot use ArgumentList here.
def AppendToVector : NativeCodeCall<"AppendToVector($0, $1)">;

// The list of arguments of the composite function.

# REQUIREMENTS_FILE=jax.requirements.txt


setuptools
wheel
cloudpickle
colorama>=0.4.4
matplotlib
pillow>=9.1.0
rich
absl-py
portpicker
six
opt-einsum
auditwheel
typing_extensions
importlib_metadata>=4.6
numpy==1.26.0;python_version=="3.12"
numpy==1.23.4;python_version=="3.11"
numpy==1.22.4;python_version<"3.11"
scipy==1.11.2;python_version=="3.12"

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/core/util/matmul_bcast.h"

namespace mlir {
namespace TF {

namespace {

// Replace TF BatchMatMul by TF Einsum op
template <typename BatchMatMulOpType>
class ConvertTFBatchMatMulToEinsumOp
    : public OpRewritePattern<BatchMatMulOpType> {
  using OpRewritePattern<BatchMatMulOpType>::OpRewritePattern;

  if (!value_type.hasRank()) return false;
  if (!value_type.getElementType().isInteger(integer_width)) return false;

  return true;
}

// Constraint to check:
// 1. The einsum has two inputs and one output.
// 2. The einsum is not created by the convert function itself.
// 3. Both inputs are int32 tensor.
// 4. Both inputs have the graph ancestor of either const-(sub), or cast-sub.

bool IsInLiftedFunc(Operation* op);

// Checks if the op is inside a StableHLO op with region.
// If the given op pointer is a nullptr, returns false.
bool IsInStableHloOpRegion(Operation* op);

// Checks if a given einsum op is supported for XlaDotV2 quantization.
bool IsEinsumSupportedByXlaDotV2(StringAttr equation_attr);

// Gets the quantization method from `op`. It is retrieved from the
// `kQuantizationMethodAttr` string attribute. Returns

  %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>>
}

// -----

        // Check that each directory in hierarchy is present
        file.withInputStream { InputStream instr ->
            def dirs = [] as Set
            def zipStr = new ZipInputStream(instr)
            def entry
            while (entry = zipStr.getNextEntry()) {
                if (entry.directory) {
                    assertTrue("Duplicate directory '$entry.name'", dirs.add(entry.name))
                }

        settingsFile """
            include "dist"
            include "lib"
        """

        file("dist/build.gradle").text = """
            abstract class ZipSrc extends DefaultTask {
                @Internal
                int countResolved

                @Internal
                abstract DirectoryProperty getSources()

                @InputFiles