// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s

func.func @main(tensor<4 x f32>, tensor<4 x i8>, tensor<4 x f32>, tensor<4 x f32>) -> tensor<4 x f32> {
// CHECK:      {
// CHECK-NEXT:     version: 3,
// CHECK-NEXT:     operator_codes: [ {
// CHECK-NEXT:       deprecated_builtin_code: 27,
// CHECK-NEXT:       version: 2,
// CHECK-NEXT:       builtin_code: SVDF
// CHECK-NEXT:     } ],

package main

var count float32 = 16
var i0 int
var i1 int
var i2 int
var i3 int
var i4 int
var i5 int
var i6 int
var i7 int
var i8 int
var i9 int
var i10 int
var i11 int
var i12 int
var i13 int
var i14 int
var i15 int
var i16 int

func main() {
	var f0 float32 = 0.0
	var f1 float32 = 1.0
	var f2 float32 = 2.0

  attr {
    key: "Tshape"
    value {
      type: DT_INT32
    }
  }
}

# MLIR-LABEL: func @main
# MLIR-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<1x1x1x256x!quant.uniform<i8:f32, 0.21632751372549019:27>>) -> tensor<1x6x31x!quant.uniform<i8:f32, 0.09363494573854933:22>>
# MLIR-SAME:  control_outputs = ""
# MLIR-SAME:  inputs = "input"
# MLIR-SAME:  outputs = "output"

	var i6 interface{} = X6{}
	check(func() { _ = i6.(p.I2) }, "")

	var i7 interface{} = X7{}
	check(func() { _ = i7.(p.I2) }, "")

	var i8 interface{} = X8{}
	check(func() { _ = i8.(p.I2) }, "")
}

func check(f func(), msg string) {
	defer func() {
		v := recover()
		if v == nil {
			if msg == "" {
				return
			}
			panic("did not panic")
		}

type TUI uintptr
type TF float64
type TF32 float32
type TF64 float64
type TB bool
type TS string

func (v TI) String() string   { return Sprintf("I: %d", int(v)) }
func (v TI8) String() string  { return Sprintf("I8: %d", int8(v)) }
func (v TI16) String() string { return Sprintf("I16: %d", int16(v)) }
func (v TI32) String() string { return Sprintf("I32: %d", int32(v)) }
func (v TI64) String() string { return Sprintf("I64: %d", int64(v)) }

    %cst_0 = arith.constant dense<1> : tensor<256x256xi8>
    %cst_1 = "tfl.pseudo_qconst"() {qtype = tensor<256x256x!quant.uniform<i8:f32, 1.000000e+00>>, value = dense<1>  : tensor<256x256xi8>} : () -> tensor<256x256x!quant.uniform<i8:f32, 1.000000e+00>>
    %cst_2 = arith.constant dense<0> : tensor<i32>

      %2 = "quantfork.dcast"(%1) : (tensor<1x3x!quant.uniform<i8:f32, 0.13170163023705575:-1>>) -> tensor<1x3xf32>
      %3 = stablehlo.reshape %2 : (tensor<1x3xf32>) -> tensor<3x1xf32>
      %4 = "quantfork.qcast"(%3) {volatile} : (tensor<3x1xf32>) -> tensor<3x1x!quant.uniform<i8:f32, 0.13170163023705575:-1>>

#include "mlir/Support/LLVM.h"  // from @llvm-project

namespace mlir {
namespace quant {

// Creates a `UniformQuantizedType` with the given `scale` and `zero_point`
// values. The produced type has f32 as its expressed type and i8 as its
// storage type. The available values use the full range of the storage value,
// i.e. [-128, 127]. Assumes asymmetric quantization, meaning the zero point
// value can be a non-zero value.

	i2 **uint8
	b2 **byte

	i3 ***uint8
	b3 ***byte

	i4 ****uint8
	b4 ****byte

	i5 *****uint8
	b5 *****byte

	i6 ******uint8
	b6 ******byte

	i7 *******uint8
	b7 *******byte

	i8 ********uint8
	b8 ********byte

func _[
	Int8 ~int8,
	Int16 ~int16,
	Int32 ~int32,
	Int64 ~int64,
	Int8_16 ~int8 | ~int16,
](
	i8 Int8,
	i16 Int16,
	i32 Int32,
	i64 Int64,
	i8_16 Int8_16,
) {
	b = 42
	b = 42.0
	// etc.

	i8 = -1 << 7
	i8 = 1<<7 - 1
	i16 = -1 << 15
	i16 = 1<<15 - 1
	i32 = -1 << 31
	i32 = 1<<31 - 1
	i64 = -1 << 63
	i64 = 1<<63 - 1