#include "tensorflow/compiler/mlir/tf2xla/transforms/passes.h"
#include "xla/mlir/framework/transforms/passes.h"
#include "xla/mlir_hlo/mhlo/IR/register.h"
#include "xla/mlir_hlo/mhlo/transforms/passes.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/platform/statusor.h"

// Tool which lowers TensorFlow Graphs to StableHLO graphs.
//

// WHOLE-PASSES-DAG:  %[[vh2:.*]] = "tfl.var_handle"() <{container = "", shared_name = "read_assign/states1"}> : () -> tensor<*x!tf_type.resource<tensor<1x2x3x!quant.uniform<u8:f32, {{.*}}>>>>

// WHOLE-PASSES-DAG:  %[[rv1:.*]] = "tfl.read_variable"({{.*}}) : (tensor<*x!tf_type.resource<tensor<1x2x3x!quant.uniform<u8:f32, {{.*}}>>>>) -> tensor<1x2x3x!quant.uniform<u8:f32, {{.*}}>>

#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Transforms/Passes.h"  // from @llvm-project
#include "stablehlo/experimental/transforms/Passes.h"  // from @stablehlo
#include "tensorflow/compiler/mlir/lite/common/tfl_pass_config.h"
#include "tensorflow/compiler/mlir/lite/quantization/quantization_passes.h"
#include "tensorflow/compiler/mlir/lite/quantization/tensorflow/passes.h"

    if (VLOG_IS_ON(1)) {
      LOG_FIRST_N(INFO, 1)
          << "None of the MLIR Optimization Passes are enabled "
          << "(registered " << registry_->passes().size() << ")";
    }
    return absl::OkStatus();
  }

  if (VLOG_IS_ON(1)) {
    LOG_FIRST_N(INFO, 1) << "MLIR Graph Optimization Passes."
                         << " Enabled: " << num_passes_enabled

// input module contains operation in the tf_executor dialect, the pass raises
// an error unless the tf_executor dialect ops are optimized away by
// canonicalization.
//
// The pipeline is used in ConvertMLIRToXlaComputation. And it generally has the
// following pass structure:
// - TensorFlow passes
// - Legalization passes
// - MHLO passes
//
// device_type: XLA JIT device to use for compilation such as "XLA_CPU_JIT",

#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/quantization/stablehlo/passes/bridge/passes.h"
#include "tensorflow/compiler/mlir/tensorflow/dialect_registration.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/tf_saved_model_passes.h"
#include "tensorflow/compiler/mlir/tensorflow/translate/import_model.h"

			}
			passes[i] = p
			matchedOne = true
		}
	}
	if matchedOne {
		return ""
	}
	return fmt.Sprintf("Did not find a phase matching %s in -d=ssa/... debug option", phase)
}

// list of passes for the compiler
var passes = [...]pass{
	{name: "number lines", fn: numberLines, required: true},

#include "mlir/Pass/PassManager.h"  // from @llvm-project
#include "mlir/Transforms/Passes.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/quantization/tensorflow/passes/passes.h"
#include "tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.pb.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"
#include "xla/mlir_hlo/mhlo/transforms/passes.h"

namespace tensorflow {
namespace quantization {
namespace {

        if (keyType.isPrimitive()) {
            // Kotlin passes these types for its own basic types
            return Cast.uncheckedNonnullCast(mapProperty(JavaReflectionUtil.getWrapperTypeForPrimitiveType(keyType), valueType));
        }
        if (valueType.isPrimitive()) {
            // Kotlin passes these types for its own basic types

namespace mlir::quant::stablehlo {

// Put the definitions inside the ::mlir::quant::stablehlo namespace, to match
// the declarations in passes.h.
#define GEN_PASS_DEF_QUANTIZEWEIGHTPASS
#include "tensorflow/compiler/mlir/quantization/stablehlo/passes/passes.h.inc"

namespace {

using QuantizationUnits = llvm::SetVector<std::pair<Operation*, int>>;
using mlir::stablehlo::ConstantOp;
using mlir::stablehlo::ConvertOp;