CPred<"getElementTypeOrSelf($0).isa<mlir::quant::UniformQuantizedType>()">>;

// Checks if the given two values have the same type.
def AreTheSameElementType : Constraint<
  CPred<"$0.getType() == $1.getType()">>;

// Checks if the given two values are the same.
def AreTheSameValue : Constraint<
  CPred<"$0 == $1">>;

// Checks if the value has rank.
def HasRank : Constraint<

def IsRankedShapeLastDimStatic : Constraint<And<[
  CPred<"$0.getType().isa<RankedTensorType>()">,
  CPred<"!$0.getType().cast<ShapedType>().isDynamicDim( "
  "  $0.getType().cast<RankedTensorType>().getRank() - 1)">]>>;

def IsNotComplexType : Constraint<And<[
  CPred<"$0.getType().isa<RankedTensorType>()">,
  CPred<"!$0.getType().cast<ShapedType>().getElementType().isa<ComplexType>()">
]>>;

  NativeCodeCall<"TransposeLastTwoDims($0[0].getType())">;

def AreLastTwoDimsTransposed : Constraint<CPred<
  "TFL::AreLastTwoDimsTransposed($0)">>;

// Checks if the param passed is of NoneType.
def IsNoneType : Constraint<CPred<"$0.getType().isa<NoneType>()">>;

def ConstantLikePred : CPred<"::mlir::matchPattern($0, ::mlir::m_Constant())">;
def IsConstantLike : Constraint<ConstantLikePred>;

include "mhlo/IR/hlo_ops.td"
include "stablehlo/dialect/ChloOps.td"

def IsDefinedByConvOrDotGeneralOp : Constraint<Or<[
  CPred<"$0.getDefiningOp<mhlo::ConvolutionOp>()">,
  CPred<"$0.getDefiningOp<mhlo::DotGeneralOp>()">]>>;

def IsNull : Constraint<CPred<"!$0">>;

// This pattern optimizes:
//   conv/dot_general + a + b -> conv/dot_general + (a + b)
//   conv/dot_general - a - b -> conv/dot_general - (a + b)

// Here, the element type can be any integer or float type. But, note that only
// 32 bit integers are supported for the values.
class IsConstantValueOf<int value> : Constraint<CPred<
  "IsConstantValueOf($0," # value # ")">>;

def IsOnGpuDevicePred : CPred<"IsOnGpuDevice($0.getOwner())">;

class ValueEquals<string val> : Constraint<CPred<"ValueEquals($0, " # val # ")">>;
def ValueGreaterThanZero : Constraint<CPred<"ValueGreaterThanZero($0)">>;
def ValueIsReciprocal : Constraint<CPred<"ValueIsReciprocal($0, $1)">>;
def TensorIsSign : Constraint<CPred<"TensorIsSign($_builder, $0, $1)">>;
def SameValue : Constraint<CPred<"$0 == $1">>;
def FloatOrDefaultCompare : Constraint<CPred<

  let description = [{
    The TFRT Fallback Dialect.
  }];

  let cppNamespace = "::tfrt::fallback";
}

// This corresponds to tensorflow::Tensor.
def TFTensorType : DialectType<Fallback_Dialect,
    CPred<"$_self.isa<::tfrt::fallback::TFTensorType>()">, "!tfrt_fallback.tf_tensor type">,
    BuildableType<"$_builder.getType<::tfrt::fallback::TFTensorType>()"> {
  let description = [{

  "FloatValueEquals($0, " # val # ")">>;

class IsBoolAttrEqual<string true_or_false> : Constraint<CPred<
  "$0.getValue() == "#true_or_false#"">>;

// Flattens a constant tensor to 1D.
def FlattenTo1D : NativeCodeCall<"FlattenTo1D($0)">;

def HasOneUse : Constraint<CPred<"$0.hasOneUse()">>;

def HasSameStaticShapes : Constraint<
    CPred<"$0.getType().cast<ShapedType>().hasStaticShape() && "

// BatchNorm op patterns.
//===----------------------------------------------------------------------===//

def FalseBoolAttr : AttrConstraint<CPred<"!$_self.cast<BoolAttr>().getValue()">>;
def TrueBoolAttr : AttrConstraint<CPred<"$_self.cast<BoolAttr>().getValue()">>;

def CastValueToI64: NativeCodeCall<
  "CastValueToI64($0.getLoc(), $1, &$_builder)">;

def CastValueToElementType: NativeCodeCall<

def NoFallbackAttrNotSet : Constraint<CPred<
  "!$0.getDefiningOp()->hasAttr(kNoFallbackAttr)">>;

class FloatValueEquals<string val> : Constraint<CPred<
  "FloatValueEquals($0, " # val # ")">>;

class RankEquals<string rank> : Constraint<CPred<
  "RankEquals($0, " # rank # ")">>;

def IsFusibleWithBias : Constraint<CPred<
  "IsFusibleWithBiasOp($0.getDefiningOp())">>;