namespace mlir::quant {
namespace {

void PackOperandTestHelper(
    const llvm::SmallVector<int64_t>& unpacked_shape,
    const llvm::SmallVector<int8_t>& unpacked_values, int pack_dim,
    const llvm::SmallVector<int64_t>& expected_packed_shape,
    const llvm::SmallVector<int8_t>& expected_packed_values) {
  MLIRContext context;
  OwningOpRef<ModuleOp> module(ModuleOp::create(UnknownLoc::get(&context)));

// of the index path attribute.
SmallVector<NamedAttribute> ReplaceBoundInputAttrWithIndexPathAttr(
    const ArrayRef<NamedAttribute> arg_attrs, const StringRef index_path,
    Builder& builder) {
  // Keep all other attributes except the tf_saved_model.bound_input attribute,
  // as we are replacing it with tf_saved_model.index_path.
  SmallVector<NamedAttribute> new_arg_attrs;
  for (auto arg_attr : arg_attrs) {

    // and input devices, variables used by this function.
    llvm::SmallVector<llvm::StringRef, 4> input_names;
    llvm::SmallVector<
        std::pair<tensorflow::DataType, tensorflow::PartialTensorShape>, 4>
        input_specs;
    llvm::SmallVector<llvm::StringRef, 4> input_devices;
    llvm::SmallVector<mlir::Operation*, 4> bound_inputs;
    for (unsigned i = 0, e = func.getNumArguments(); i != e; ++i) {

#include "mlir/Support/LLVM.h"  // from @llvm-project

namespace mlir {
namespace TF {

SmallVector<int64_t, 4> ReversePermutation(ArrayRef<int64_t> permutation) {
  SmallVector<int64_t, 4> reverse(permutation.size());
  for (size_t i = 0; i < permutation.size(); ++i) {
    reverse[permutation[i]] = i;
  }
  return reverse;
}

SmallVector<int64_t, 4> GetDataFormatPermutation(StringRef from, StringRef to) {

      for (Operation& op : nested_block->getOperations()) update_from_op(&op);
    });
  }
  return SmallVector<Value>(results.getArrayRef());
}

llvm::SmallVector<Value> AccumulateResultsDefinedWithin(
    const llvm::SetVector<Operation*>& partition_ops) {
  llvm::SmallVector<Value> values_for_results;
  for (Operation* op : partition_ops) {
    if (IsConstantOrNone(op)) {
      continue;
    }

namespace mlir {
namespace quant {
namespace {

// Folds the operation recursively and return the results.
LogicalResult FoldOperation(OpBuilder& builder, Operation* op,
                            SmallVector<Value>& results) {
  SmallVector<ElementsAttr> inputs;
  for (auto operand : op->getOperands()) {
    auto preceding_const_op = operand.getDefiningOp<TF::ConstOp>();
    if (preceding_const_op) {

      : input_shapes_(input_shapes) {}
  void runOnOperation() override {
    // Parse `input_arg_shapes_` if provided (test only)
    SmallVector<ArrayRef<int64_t>> input_shapes_vec;
    absl::StatusOr<SmallVector<SmallVector<int64_t>>> parsed_shapes;
    if (!input_arg_shapes_.empty()) {
      parsed_shapes = ParseArgumentShapes(input_arg_shapes_);
      if (!parsed_shapes.ok()) {

  SmallVector<float> TransposeValues(const ArrayRef<float> values) const {
    SmallVector<float> transposed_values(values.size());
    SmallVector<int64_t> current_indices = {};
    TransposeRecursively(values, transposed_values, current_indices);

    return transposed_values;
  }

  // Returns the shape after permutation.
  SmallVector<int64_t> GetTargetShape() const { return target_shape_; }

  void runOnOperation() override;
};

LogicalResult HasNoNestedEntryFunctions(
    const llvm::SmallVector<func::FuncOp> &entry_funcs, SymbolTable &symtab) {
  auto calls_entry_functions = [&](SymbolUserOpInterface op) {
    llvm::SmallVector<func::FuncOp> callees;
    if (GetCallees(op, symtab, callees).failed()) {
      return false;
    }
    for (auto &callee : callees) {

  mlir::IntegerAttr op_key;
  mlir::IntegerAttr cost;
  mlir::StringAttr device;
  mlir::StringAttr op_name;
  mlir::SymbolRefAttr f;
  llvm::SmallVector<mlir::OpAsmParser::UnresolvedOperand, 4> in_chains;
  llvm::SmallVector<mlir::OpAsmParser::UnresolvedOperand, 4> operands;
  mlir::NamedAttrList op_attrs;
  mlir::NamedAttrList op_func_attrs;
  auto loc = parser.getNameLoc();

  if (options.has_chain &&