// operate on quantized values.
//
// Examples from storage to quantized type:
//   i8 -> !quant<"uniform[i8:f32]{1.0}">
//   tensor<4xi8> -> tensor<4x!quant<"uniform[i8:f32]{1.0}">>
//   vector<4xi8> -> vector<4x!quant<"uniform[i8:f32]{1.0}">>
def Quantization_StorageCastOp : Quantization_Op<"scast", [Pure]> {
  let arguments = (ins quant_RealOrStorageValueType:$arg);

  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"

  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"

	{id: "foo", typ: "<type 1 chan <- <type -1>>", want: "<-chan int8"},
	{id: "foo", typ: "<type 1 chan -< <type -1>>", want: "chan<- int8"},
	{id: "foo", typ: "<type 1 struct { I8 <type -1>; I16 <type -2> \"i16\"; }>", want: "struct{I8 int8; I16 int16 \"i16\"}"},

00000080  14 64 4f b9 c3 fb 1e 55  c1 1f c7 31 57 72 68 db  |.dO....U...1Wrh.|
00000090  03 37 a8 c9 07 f4 ca 62  6c 5c f3 8b 5a 3d 76 dd  |.7.....bl\..Z=v.|
000000a0  63 ea 68 61 6b a1 2d 95  49 38 16 7e 51 5c e5 15  |c.hak.-.I8.~Q\..|
000000b0  c0 58 7d c5 67 4a 6f 64  b6 79 1a 41 9b b1 33 15  |.X}.gJod.y.A..3.|
000000c0  38 74 92 5c a5 48 c3 f2  94 bb 33 ec af cf d7 e7  |8t.\.H....3.....|

package main

import (
	"fmt"
	"math"
	"runtime"
	"strings"
)

type ErrorTest struct {
	name string
	fn   func()
	err  string
}

var (
	i, j, k       int   = 0, 0, 1
	i8, j8, k8    int8  = 0, 0, 1
	i16, j16, k16 int16 = 0, 0, 1
	i32, j32, k32 int32 = 0, 0, 1
	i64, j64, k64 int64 = 0, 0, 1

	bb = []int16{2, 0}

	u, v, w       uint    = 0, 0, 1
	u8, v8, w8    uint8   = 0, 0, 1

// operate on quantized values.
//
// Examples from storage to quantized type:
//   i8 -> !quant<"uniform[i8:f32]{1.0}">
//   tensor<4xi8> -> tensor<4x!quant<"uniform[i8:f32]{1.0}">>
//   vector<4xi8> -> vector<4x!quant<"uniform[i8:f32]{1.0}">>
def quantfork_StorageCastOp : quantfork_Op<"scast", [Pure]> {
  let arguments = (ins quant_RealOrStorageValueType:$arg);

	cni16 int16 = -7
	cni32 int32 = -4
	cni64 int64 = -5
	cni64big int64 = -1<<31
	cni64bigger int64 = -1<<32
	cnhuge = -1<<100
	cnfgood = -2.0
	cnfbad = -2.1
)

var j int = 100020
var i int = 100021
var i8 int8 = 126
var i16 int16 = 10025
var i32 int32 = 100022
var i64 int64 = 100023
var i64big int64 = 1<<31
var i64bigger int64 = 1<<32
var huge uint64 = 1<<64 - 1
var fgood float64 = 2.0
var fbad float64 = 2.1

	}
}

const prolog = `

package main

import (
	"fmt"
	"os"
)

var (
	i8 int8 = 0x12
	i16 int16 = 0x1234
	i32 int32 = 0x12345678
	i64 int64 = 0x123456789abcdef0
	ui8 uint8 = 0x12
	ui16 uint16 = 0x1234
	ui32 uint32 = 0x12345678
	ui64 uint64 = 0x123456789abcdef0

	ni8 = ^i8
	ni16 = ^i16
	ni32 = ^i32
	ni64 = ^i64
	nui8 = ^ui8
	nui16 = ^ui16
	nui32 = ^ui32
	nui64 = ^ui64

  %0 = "quantfork.qcast"(%cst_1) : (tensor<2x3x3x2xf32>) -> tensor<2x3x3x2x!quant.uniform<i8<-127:127>:f32:3, {0.003937007874015748,0.003937007874015748}>>
  %1 = "quantfork.dcast"(%0) : (tensor<2x3x3x2x!quant.uniform<i8<-127:127>:f32:3, {0.003937007874015748,0.003937007874015748}>>) -> tensor<2x3x3x2xf32>
  %2 = "quantfork.qcast"(%arg0) : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3x!quant.uniform<i8:f32, 0.0011764706057660721:-43>>