#include "mlir/Transforms/GreedyPatternRewriteDriver.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/lite/quantization/ir/QuantOps.h"
#include "tensorflow/compiler/mlir/lite/stablehlo/transforms/passes.h"
#include "tensorflow/compiler/mlir/lite/transforms/dilated_conv.h"
#include "tensorflow/compiler/mlir/lite/transforms/passes.h"

# CROSS-COMPILE ARM64 PYCPP
build:cross_compile_linux_arm64_pycpp_test --config=linux_arm64_pycpp_test
# Tests that fail only when cross-compiled
build:cross_compile_linux_arm64_pycpp_test -//tensorflow/compiler/mlir/quantization/stablehlo:convert_tf_quant_to_mhlo_int_test
# MACOS ARM64 PYCPP

     TFL_AF_None),
    $x,
    TFL_AF_None),
  (TFL_HardSwishOp $x)>;

// Matching HardSwish with extra FakeQuant. These FakeQuant ops were due to
// incorrect placement in the quantization aware training.
def MatchHardSwishQuantized : Pat<
  (TFL_MulOp (TFL_DequantizeOp (TFL_QuantizeOp
    (TFL_MulOp
     $x, (TFL_DequantizeOp (TFL_QuantizeOp (TFL_AddOp
          $x,

    auto quant_to_float =
        rewriter.create<DivOp>(op.getLoc(), float_diff, quant_diff);

    auto float_to_quant =
        rewriter.create<DivOp>(op.getLoc(), quant_diff, float_diff);

    // During quantization, the quantized min/max values may not line up
    // perfectly with the specified min/max. Nudge them into the right range.
    auto min_scaled =
        rewriter.create<DivOp>(op.getLoc(), float_min, quant_to_float);

// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions -quant-quantize-composite-functions='target-opset=XLA enable-per-channel-quantization=true' | FileCheck --check-prefix=XLA_PerChannel %s

module {
  func.func @conv_with_int_per_layer(%arg0: tensor<*xf32>) -> tensor<*xf32> {