import org.jetbrains.kotlin.psi.KtFile
import org.jetbrains.kotlin.psi.psiUtil.collectDescendantsOfType
import org.jetbrains.kotlin.test.services.TestServices
import org.jetbrains.kotlin.test.services.assertions
import kotlin.math.pow

abstract class AbstractAnalysisApiSignatureContractsTest : AbstractAnalysisApiBasedTest() {
    override fun doTestByMainFile(mainFile: KtFile, mainModule: KtTestModule, testServices: TestServices) {

  // CHECK:  return
}

func.func @pow(%arg0: tensor<2x1x3xf32>, %arg1: tensor<2x1x1xf32>) -> tensor<2x1x3xf32> {
  %0 = "tf.Pow"(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<2x1x1xf32>) -> tensor<2x1x3xf32>
  func.return %0 : tensor<2x1x3xf32>

  // CHECK-LABEL: pow
  // CHECK:  %[[pow:.*]] = tfl.pow(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<2x1x1xf32>) -> tensor<2x1x3xf32>
  // CHECK:  return

// a value of 16 limits the returned string to 65,536 characters.
// The value must be between 1 and 30.
func MaxLength(pow int) Option {
	if pow <= 0 || pow > 30 {
		panic("demangle: invalid MaxLength value")
	}
	return Option(pow << maxLengthShift)
}

// isMaxLength reports whether an Option holds a maximum length.
func isMaxLength(opt Option) bool {
	return opt&maxLengthMask != 0
}

{
  param(
    [parameter(Mandatory = $true, Position = 0)]
    [Uint32] $IPAddress
  )

  $dotted_ip = $(for ($i = 3; $i -gt -1; $i--) {
    $remainder = $IPAddress % [Math]::Pow(256, $i)
    ($IPAddress - $remainder) / [Math]::Pow(256, $i)
    $IPAddress = $remainder
  })
  return [String]::Join(".", $dotted_ip)
}

# TODO(pjh): this is copied from

// and then does an indirect call to that value.
//
// Here is what a direct reference might look like:
//
//	     137: e9 20 06 00 00               	jmp	0x75c <pow+0x75c>
//	     13c: e8 00 00 00 00               	callq	0x141 <pow+0x141>
//			000000000000013d:  IMAGE_REL_AMD64_REL32	_errno
//
// The assembly below dispenses with the import symbol and just makes
// a direct call to _errno.
//

}

// CHECK-LABEL: testPow
func.func @testPow(tensor<? x i32>, tensor<? x i32>) -> tensor<? x i32> {
^bb0(%arg0: tensor<? x i32>, %arg1: tensor<? x i32>):
  // CHECK: tfl.pow %arg0, %arg1
  %0 = tfl.pow %arg0, %arg1 : tensor<? x i32>
  func.return %0#0 : tensor<? x i32>
}

// CHECK-LABEL: testAtan2
func.func @testAtan2(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>) -> tensor<?xf32> {

  let results = (outs Variadic<AnyTensor>:$output);

  let regions = (region VariadicRegion<SizedRegion<1>>:$calls);

  let hasCanonicalizer = 1;
}


def TFL_PowOp : TFL_Op<"pow", [
    ResultsBroadcastableShape,
    Pure,
    TFL_OperandsHaveSameShapesOrBroadcastableShape<[0, 1], 4>]> {
  let summary = "Power operator";

  let description = [{
    Element-wise power operation.

    TF_IntTensor:$x
  );

  let results = (outs
    TF_Uint8Tensor:$y
  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}

def TF_PowOp : TF_Op<"Pow", [Pure, ResultsBroadcastableShape, TF_SameOperandsAndResultElementTypeResolveRef]>,
               WithBroadcastableBinOpBuilder {
  let summary = "Computes the power of one value to another.";

  let description = [{

    *   gRPC: Propagate truncated errors (instead of returning gRPC internal
        error).
    *   Augment `parallel_interleave` to support 2 kinds of prefetching.
    *   Improved XLA support for C64-related ops log, pow, atan2, tanh.
    *   Add probabilistic convolutional layers.

## API Changes

*   Introducing `prepare_variance` boolean with default setting to False for
    backward compatibility.