See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

// Rewrites ops that require quantized inputs or outputs to ops that allow
// non-quantized inputs and outputs.

#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project

#include <memory>

#include "llvm/ADT/StringRef.h"
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project

namespace mlir {
namespace TFTPU {

// Creates a pass that rewrites `tf_device.launch_func` on TPUs into TPU runtime
// ops.
std::unique_ptr<mlir::OperationPass<mlir::ModuleOp>> CreateTPURewritePass(
    llvm::StringRef module_name = llvm::StringRef());

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

include "mlir/Pass/PassBase.td"

def TPURewritePass : Pass<"tf-tpu-rewrite", "mlir::ModuleOp"> {
  let summary = "Rewrites a `tf_device.cluster_func` on TPUs into TPU runtime operations.";

  let description = [{
    This pass rewrites a `tf_device.cluster_func` operation into a sequence of `tf._TPUCompileMlir`

#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/lib/core/status.h"

namespace tensorflow {

// An OpKernel that compiles an op to an XLA computation and runs it. Unlike
// XlaLaunch this doesn't rely on any rewrites of the graphdef - it will run a
// vanilla TensorFlow op as long as the bridge supports it.
class XlaCompileOnDemandOp : public OpKernel {
 public:
  explicit XlaCompileOnDemandOp(OpKernelConstruction* ctx)

namespace mlir {
namespace TFDevice {

namespace {

#define GEN_PASS_DEF_CONVERTLAUNCHFUNCTOTFCALLPASS
#include "tensorflow/compiler/mlir/tensorflow/transforms/tf_passes.h.inc"

// Rewrites tf_device::LaunchFuncOp into TF::PartitionedCallOp.
struct ConvertLaunchFuncToTFCallPass
    : public impl::ConvertLaunchFuncToTFCallPassBase<
          ConvertLaunchFuncToTFCallPass> {
  void runOnOperation() override;
};

// license that can be found in the LICENSE file.

/*
Package rangefunc rewrites range-over-func to code that doesn't use range-over-funcs.
Rewriting the construct in the front end, before noder, means the functions generated during
the rewrite are available in a noder-generated representation for inlining by the back end.

# Theory of Operation

The basic idea is to rewrite

	for x := range f {
		...
	}

into

  pm.addPass(mlir::createInlinerPass());
  pm.addNestedPass<mlir::func::FuncOp>(mlir::createCanonicalizerPass());
  pm.addPass(mlir::quant::CreateCastBf16OpsToF32Pass());

  // Optimizes the graph via cleanups, merges, rewrites, constant folding,
  // and edge case handling where possible.
  pm.addNestedPass<mlir::func::FuncOp>(
      mlir::TF::CreateDropWhileShapeInvariantPass());
  pm.addNestedPass<mlir::func::FuncOp>(

#include "tensorflow/compiler/jit/report_clustering_info_pass.h"
#include "tensorflow/core/common_runtime/optimization_registry.h"

namespace tensorflow {

// PRE_PLACEMENT passes:

// EncapsulateXlaComputationsPass rewrites computations generated by the
// xla.compile() Python code into XlaLaunch nodes.
REGISTER_OPTIMIZATION(OptimizationPassRegistry::PRE_PLACEMENT, 36,
                      EncapsulateXlaComputationsPass);

// from

                                          CastI64ToI32(window_dims[1]).value());
    StringAttr activation_function = rewriter.getStringAttr("NONE");

    rewriter.replaceOpWithNewOp<TFL::MaxPool2DOp>(
        op, result_type, input, padding, stride_w_attr, stride_h_attr,
        window_w_attr, window_h_attr, activation_function);
  }
};

import okhttp3.Cache;
import okhttp3.Interceptor;
import okhttp3.OkHttpClient;
import okhttp3.Request;
import okhttp3.Response;

public final class RewriteResponseCacheControl {
  /** Dangerous interceptor that rewrites the server's cache-control header. */
  private static final Interceptor REWRITE_CACHE_CONTROL_INTERCEPTOR = chain -> {
    Response originalResponse = chain.proceed(chain.request());
    return originalResponse.newBuilder()