#include "absl/base/call_once.h"
#include "llvm-c/Target.h"
#include "llvm/Support/ManagedStatic.h"
#include "tensorflow/compiler/aot/codegen.h"
#include "tensorflow/compiler/aot/flags.h"
#include "tensorflow/compiler/aot/quantize.h"
#include "tensorflow/compiler/tf2xla/tf2xla.h"
#include "tensorflow/compiler/tf2xla/tf2xla_util.h"
#include "xla/client/client_library.h"
#include "xla/client/compile_only_client.h"
#include "xla/client/xla_computation.h"

// RUN: tf-quant-opt %s -split-input-file -quant-lift-quantizable-spots-as-functions -quant-quantize='target-opset=XLA' -verify-each=false | FileCheck %s

func.func private @conv(%input: tensor<1x3x4x3xf32> {tf._user_specified_name = "input_tensor"}) -> tensor<*xf32> attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf_type.shape<1x3x4x3>]} {
  %weight = arith.constant dense_resource<__elided__> : tensor<2x3x3x2xf32>

  let regions = (region SizedRegion<1>:$body);
  let hasVerifier = 1;
}

def quantfork_ReturnOp : quantfork_Op<"return", [Terminator]> {
  let summary = [{
    The `return` operation terminates a quantize region and returns values.
  }];

  let arguments = (ins Variadic<AnyTensor>:$results);
}

//===----------------------------------------------------------------------===//

        "passes/prepare_quantize.inc",
        "passes/prepare_quantize_drq.cc",
        "passes/preprocess_op.cc",
        "passes/preprocess_op.inc",
        "passes/propagate_quantize_type.cc",
        "passes/quantize.cc",
        "passes/quantize_composite_functions.cc",
        "passes/quantize_composite_functions.inc",
        "passes/quantize_weights.cc",
        "passes/quantized_function_library.h",

	return func() float64 {
		sum := f()
		for _, s := range fs {
			sum += s()
		}
		return sum
	}
}

// quantize returns a new stream that rounds f to a multiple
// of mult at each step.
func (f float64Stream) quantize(mult float64) float64Stream {
	return func() float64 {
		r := f() / mult
		if r < 0 {
			return math.Ceil(r) * mult
		}
		return math.Floor(r) * mult

        "passes/merge_fusion_with_dequantize.cc",
        "passes/nchw_convolution_to_nhwc.cc",
        "passes/optimize_graph.cc",
        "passes/post_quantize.cc",
        "passes/prepare_quantize.cc",
        "passes/quantize.cc",
        "passes/quantize_composite_functions.cc",
        "passes/quantize_weight.cc",
        "passes/remove_sharding_custom_call.cc",
        "passes/remove_sharding_custom_call.inc",

// RUN: stablehlo-quant-opt %s -split-input-file -stablehlo-quantize -verify-each=false | FileCheck %s

// Tests if reduce_window op following quantized function is quantized.

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 1722 : i32}, tf_saved_model.semantics} {
  // CHECK-LABEL: main_00
  // CHECK-SAME: %[[ARG0:.*]]: tensor<2x3x1x1024xf32>

		// might not start at (0, 0).
		//
		// TODO: Pick a better sub-sample of the Plan 9 palette.
		pm = image.NewPaletted(b, palette.Plan9[:opts.NumColors])
		if opts.Quantizer != nil {
			pm.Palette = opts.Quantizer.Quantize(make(color.Palette, 0, opts.NumColors), m)
		}
		opts.Drawer.Draw(pm, b, m, b.Min)
	}

	// When calling Encode instead of EncodeAll, the single-frame image is

	Set(x, y int, c color.Color)
	SetRGBA64(x, y int, c color.RGBA64)
}

// Quantizer produces a palette for an image.
type Quantizer interface {
	// Quantize appends up to cap(p) - len(p) colors to p and returns the
	// updated palette suitable for converting m to a paletted image.
	Quantize(p color.Palette, m image.Image) color.Palette
}

// Op is a Porter-Duff compositing operator.
type Op int

const (

// RUN: stablehlo-quant-opt %s -split-input-file -stablehlo-quantize -verify-each=false | FileCheck %s

module attributes {tf_saved_model.semantics} {
  // CHECK-LABEL: same_scale_after_composite
  // CHECK-SAME: %[[ARG0:.*]]: tensor<1x2xf32>
  // CHECK-SAME: %[[ARG1:.*]]: tensor<2x3xf32>
  func.func private @same_scale_after_composite(%arg0: tensor<1x2xf32>, %arg1: tensor<2x3xf32>) -> tensor<3x1xf32> {