package ssa

import (
	"cmd/compile/internal/types"
	"sort"
)

// decompose converts phi ops on compound builtin types into phi
// ops on simple types, then invokes rewrite rules to decompose
// other ops on those types.
func decomposeBuiltIn(f *Func) {
	// Decompose phis
	for _, b := range f.Blocks {
		for _, v := range b.Values {
			if v.Op != OpPhi {
				continue
			}

std::unique_ptr<OperationPass<ModuleOp>> CreateConvertLaunchFuncToTFCallPass();

// A pass that decomposes composite resource operations into primitive ones like
// ReadVariableOp, AssignVariableOp and other computations to facilitate
// transformations like resource op lifting.
std::unique_ptr<OperationPass<func::FuncOp>> CreateDecomposeResourceOpsPass();

// A pass that decomposes composite resource operations in device cluster

}

def DecomposeResourceOpsPass : Pass<"tf-device-decompose-resource-ops", "mlir::func::FuncOp"> {
  let summary = "Decompose composite resource variable operations into primitive Read/AssignVariableOp and raw computation.";

  let description = [{
    A pass that decomposes composite resource operations into primitive ones like

                                         decomposed_partitioned_call_callees)))
      return failure();
  }
  return success();
}

// Decomposes stack ops on a region and recursively decomposes called functions.
// data_var_to_size_var: a mapping from stacks' buffer local variables to size
// local variables.
// decomposed_partitioned_call_callees: cache for partitioned call ops' callee

// Creates an instance of the TensorFlow Lite dialect QuantizeVariables pass.
std::unique_ptr<OperationPass<ModuleOp>> CreatePrepareQuantizeVariablesPass();

// Creates an instance of the TensorFlow Lite pass that decomposes hybrid
// quantization patterns to the same dense operation with tfl dequantization
// and quantization patterns.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateDecomposeHybridQuantizationPass();

               "Is the corresponding integer signed">,
  ];
}
def DecomposeHybridQuantizationPass : Pass<"tfl-decompose-hybrid-quantization", "mlir::func::FuncOp"> {
  let summary = "Decomposes hybridge quantization to explicit quantize / dequantize";
  let description = [{
      Decomposes (with explicit quantize/dequantize ops) selected math
      operations which exist in the model with hybrid quantization

// canonicalization.
//
// Operations in tf dialect are lowered to XLA HLO through the following steps:
//   . Legalizes control flow operations.
//   . Decomposes compound resource operations so that the only remaining
//     operations on resource variables are resource reads/writes..
//   . Replaces resource reads/writes with function inputs/outputs and

  for (int i = state_size + 1; i < body_args.size(); ++i) {
    body_returns.push_back(body_args[i]);
  }
  builder.create<TF::YieldOp>(loc,
                              /*operands=*/body_returns);
}

// Decomposes any ReduceDatasetOps in `function` into a dataset iteration and a
// call to the reduce function in the ReduceDatasetOp.
LogicalResult DecomposeReduceDatasetInFunction(FuncOp function) {
  if (!llvm::hasSingleElement(function))

	if x != 0 {
		utf8.EncodeRune(buf[2*JamoUTF8Len:], jamoTBase+x)
		return 3 * JamoUTF8Len
	}
	return 2 * JamoUTF8Len
}

// decomposeHangul algorithmically decomposes a Hangul rune into
// its Jamo components.
// See https://unicode.org/reports/tr15/#Hangul for details on decomposing Hangul.
func (rb *reorderBuffer) decomposeHangul(r rune) {
	r -= hangulBase
	x := r % jamoTCount

	if rv := reflect.ValueOf(vr); rv.Kind() == reflect.Pointer &&
		rv.IsNil() &&
		rv.Type().Elem().Implements(valuerReflectType) {
		return nil, nil
	}
	return vr.Value()
}

// decimal composes or decomposes a decimal value to and from individual parts.
// There are four parts: a boolean negative flag, a form byte with three possible states
// (finite=0, infinite=1, NaN=2), a base-2 big-endian integer