==============================================================================*/

#include "tensorflow/compiler/mlir/quantization/stablehlo/utils/stablehlo_type_utils.h"

#include <gtest/gtest.h>
#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/Builders.h"  // from @llvm-project
#include "mlir/IR/MLIRContext.h"  // from @llvm-project

// This pattern checks if an i8 arith::ConstantOp tensor has all values within
// the INT4 range, i.e. [-8,7] and converts it into i4 if so. This assumes that
// the input is sign-extended two's complement.
class CheckRangeAndConvertI8ToI4 : public OpRewritePattern<arith::ConstantOp> {
 public:
  using OpRewritePattern<arith::ConstantOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(arith::ConstantOp op,

    return %0 : tensor<*xf32>
  }

// CHECK-LABEL: func @conv2d
// CHECK-DAG: %[[CONST_0:.*]] = arith.constant dense<0.000000e+00> : tensor<2xf32>
// CHECK-DAG: %[[CONST_1:.*]] = arith.constant dense<3.000000e+00> : tensor<2x3x512x2xf32>

  // CHECK: [[CST:%[^ ]*]] = arith.constant dense<1> : tensor<i32>
  // CHECK: "tfl.while"([[ARG0]], [[ARG1]])
  // CHECK: (tensor<i32>, tensor<1xf32>) -> (tensor<i32>, tensor<1xf32>)
  %0:2 = "tfl.while"(%arg0, %arg1) (
    // cond
    {
    ^bb0(%condArg0: tensor<*xi32>, %condArg1: tensor<*xf32>):
      %0 = "arith.constant" () {value = dense<0> : tensor<i32>} : () -> tensor<i32> loc("Const")

==============================================================================*/

#include "tensorflow/compiler/mlir/tf2xla/transforms/xla_legalize_targets.h"

#include <gmock/gmock.h>
#include <gtest/gtest.h>
#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/Dialect/Shape/IR/Shape.h"  // from @llvm-project
#include "mlir/Dialect/Tensor/IR/Tensor.h"  // from @llvm-project

#include <string>

#include "llvm/Support/MemoryBuffer.h"
#include "llvm/Support/SMLoc.h"
#include "llvm/Support/SourceMgr.h"
#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/Dialect/SCF/IR/SCF.h"  // from @llvm-project
#include "mlir/Dialect/Shape/IR/Shape.h"  // from @llvm-project

    mlir::OpBuilder op_builder(&module.getBodyRegion());
    module.walk([&](mlir::arith::ConstantOp const_op) {
      MaybeUnfoldLargeSplatConstant(&op_builder, const_op);
    });
  }

 private:
  void MaybeUnfoldLargeSplatConstant(mlir::OpBuilder* op_builder,
                                     mlir::arith::ConstantOp const_op) const {
    auto splat_elements_attr =

module {
  func.func @main(%arg0 : tensor<!tf_type.resource<tensor<4096xf32>>>,
      %arg1 : tensor<*x!tf_type.variant>) {
    %cst_0 = arith.constant dense<2> : tensor<i64>
    %cst_1 = arith.constant dense<0> : tensor<i32>
    %0 = "tf.RepeatDataset"(%arg1, %cst_0) {device = "",
      output_shapes = [#tf_type.shape<?>],

)

var (
	configRRSParityMD = NewGaugeMD(configRRSParity,
		"Reduced redundancy storage class parity")
	configStandardParityMD = NewGaugeMD(configStandardParity,
		"Standard storage class parity")
)

// loadClusterConfigMetrics - `MetricsLoaderFn` for cluster config
// such as standard and RRS parity.
func loadClusterConfigMetrics(ctx context.Context, m MetricValues, c *metricsCache) error {

#ifndef TENSORFLOW_COMPILER_MLIR_TENSORFLOW_DIALECT_REGISTRATION_H_
#define TENSORFLOW_COMPILER_MLIR_TENSORFLOW_DIALECT_REGISTRATION_H_

#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/ControlFlow/IR/ControlFlow.h"  // from @llvm-project
#include "mlir/Dialect/Func/Extensions/AllExtensions.h"  // from @llvm-project