func.return %0#0 : tensor<?xi32>
}

// -----

// test invalid Sqrt input
func.func @testSqrtWithWrongInputType(tensor<? x i32>) -> tensor<? x i32> {
^bb0(%arg0: tensor<? x i32>):
  // expected-error @+1 {{tfl.sqrt' op operand #0 must be tensor of 32-bit float values}}
  %0 = "tfl.sqrt"(%arg0): (tensor<? x i32>) -> tensor<? x i32>
  func.return %0#0 : tensor<? x i32>
}

// -----

(Cvt32Fto64F ...) => (Copy ...) // Note v will have the wrong type for patterns dependent on Float32/Float64
(Cvt64Fto32F ...) => (FRSP ...)

(CvtBoolToUint8 ...) => (Copy ...)

(Round(32|64)F ...) => (LoweredRound(32|64)F ...)

(Sqrt ...) => (FSQRT ...)
(Sqrt32 ...) => (FSQRTS ...)
(Floor ...) => (FFLOOR ...)
(Ceil ...) => (FCEIL ...)
(Trunc ...) => (FTRUNC ...)
(Round ...) => (FROUND ...)
(Copysign x y) => (FCPSGN y x)
(Abs ...) => (FABS ...)

// -----

// CHECK-LABEL: func @sqrt
func.func @sqrt(%arg0: tensor<2xf32>) -> tensor<2xf32> {
  // CHECK:  mhlo.sqrt %arg0 : tensor<2xf32>
  %0 = "tf.Sqrt"(%arg0) : (tensor<2xf32>) -> tensor<2xf32>
  func.return %0 : tensor<2xf32>
}

// -----

// CHECK-LABEL: func @sqrt_dynamic
func.func @sqrt_dynamic(%arg0: tensor<?xf32>) -> tensor<?xf32> {
  // CHECK:  mhlo.sqrt %arg0 : tensor<?xf32>

  if (!IsF32ShapedType(result_type)) return nullptr;

  auto compute = [](APFloat value) -> APFloat {
    float f = value.convertToFloat();
    float result = std::sqrt(f);
    return APFloat(result);
  };
  return ConstFoldUnaryOp(result_type, operands[0], compute);
}

//===----------------------------------------------------------------------===//
// RsqrtOp

  %7 = "tfl.abs"(%0) : (tensor<f32>) -> tensor<f32>
  %8 = "tfl.sin"(%1) : (tensor<f32>) -> tensor<f32>
  %9 = "tfl.cos"(%2) : (tensor<f32>) -> tensor<f32>
  %10 = "tfl.log"(%3) : (tensor<f32>) -> tensor<f32>
  %11 = "tfl.sqrt"(%4) : (tensor<f32>) -> tensor<f32>
  %12 = "tfl.rsqrt"(%5) : (tensor<f32>) -> tensor<f32>
  %13 = "tfl.square"(%6) : (tensor<f32>) -> tensor<f32>

}

// CHECK-LABEL:   func @sqrt(
// CHECK-SAME:               %[[VAL_0:.*]]: tensor<2xf32>) -> tensor<2xf32> {
// CHECK:           %[[VAL_1:.*]] = "tf.Sqrt"(%[[VAL_0]]) : (tensor<2xf32>) -> tensor<2xf32>
// CHECK:           return %[[VAL_1]] : tensor<2xf32>
// CHECK:         }
func.func @sqrt(%arg0: tensor<2xf32>) -> tensor<2xf32> {
  %0 = "mhlo.sqrt"(%arg0) : (tensor<2xf32>) -> tensor<2xf32>

(And(64|32|16|8) ...) => (AND ...)
(Or(64|32|16|8) ...) => (OR ...)
(Xor(64|32|16|8) ...) => (XOR ...)

(Neg(64|32|16|8) ...) => (NEG ...)
(Neg(64|32)F ...) => (FNEG(D|S) ...)

(Com(64|32|16|8) ...) => (NOT ...)


(Sqrt ...) => (FSQRTD ...)
(Sqrt32 ...) => (FSQRTS ...)

(Copysign ...) => (FSGNJD ...)

(Abs ...) => (FABSD ...)

(FMA ...) => (FMADDD ...)

(Min(64|32)F ...) => (LoweredFMIN(D|S) ...)

		// have one chunk. We want to run as many parallel scripts as possible, but
		// also want to give each script as high a limit as possible.
		// We arbitrarily split by sqrt(n) to try to balance those two goals.
		netTestLimit := int(math.Sqrt(float64(n)))
		netTestSem = make(chan struct{}, netTestLimit)
		reducedLimit := fmt.Sprintf(",%s=%d", base.NetLimitGodebug.Name(), n/netTestLimit)

	{name: "RotateLeft16", argLength: 2},
	{name: "RotateLeft8", argLength: 2},

	// Square root.
	// Special cases:
	//   +∞  → +∞
	//   ±0  → ±0 (sign preserved)
	//   x<0 → NaN
	//   NaN → NaN
	{name: "Sqrt", argLength: 1},   // √arg0 (floating point, double precision)
	{name: "Sqrt32", argLength: 1}, // √arg0 (floating point, single precision)

	// Round to integer, float64 only.
	// Special cases:

    assertEquals(ImmutableSet.of(1, 2, 3), bimap.inverse().keySet());
    assertEquals(ImmutableSet.of(1, 2, 3), sync.inverse().keySet());
  }

  private static final Function<Integer, Double> SQRT_FUNCTION = in -> Math.sqrt(in);

  public void testTransformValues() {
    Map<String, Integer> map = ImmutableMap.of("a", 4, "b", 9);
    Map<String, Double> transformed = transformValues(map, SQRT_FUNCTION);