// CHECK: return %[[CUSTOM_0]] : tensor<1x2x2x1xf32>
}

// CHECK-LABEL: floor_mod
func.func @floor_mod(%arg0: tensor<5xf32>, %arg1: tensor<5xf32>) -> tensor<5xf32> {
  %0 = "tf.FloorMod"(%arg0, %arg1) : (tensor<5xf32>, tensor<5xf32>) -> tensor<5xf32>
  func.return %0 : tensor<5xf32>

// CHECK: return %[[CUSTOM_0]] : tensor<1x2x2x1xf32>
}

// CHECK-LABEL: floor_mod
func.func @floor_mod(%arg0: tensor<5xf32>, %arg1: tensor<5xf32>) -> tensor<5xf32> {
  %0 = "tf.FloorMod"(%arg0, %arg1) : (tensor<5xf32>, tensor<5xf32>) -> tensor<5xf32>
  func.return %0 : tensor<5xf32>

 Assert 7
 Assign 47
 AssignAdd 2
 AssignSub 2
 BroadcastGradientArgs 44
 Cast 38
 ConcatV2 3
 Const 875
 ControlTrigger 5
 Enter 874
 Equal 4
 Exit 69
 ExpandDims 9
 Fill 5
 FloorMod 1
 GreaterEqual 7
 Identity 113
 IsVariableInitialized 1
 IteratorGetNext 1
 IteratorV2 1
 Less 9
 LogicalAnd 3
 LoopCond 8
 Max 4
 Maximum 44
 Merge 145
 Minimum 43
 Mul 8

           (FloatOrDefaultCompare $compare_type0),
           (FloatOrDefaultCompare $compare_type1),
           (FloatOrDefaultCompare $compare_type2)]>;

// Converts a dag of HLOs representing floor_mod to tf.FloorMod.
// The pattern matched executes the following computation:
//
// rem = remainder(arg0, arg1)
// for i in 0 to len(arg1):
//    if ((rem[i] < 0) != (arg0[i] < 0) && arg0[i] != 0)
//       rem[i] += arg1[i]

def : Pat<(TF_FloorDivOp AnyTensor:$l, AnyTensor:$r),
          (CHLO_BroadcastDivOp $l, $r, (BinBroadcastDimensions $l, $r)),
          [(UnsignedIntTensor $l)]>;

// Performs a substitution of FloorMod designed to correct for possibly negative
// values. Pseudocode shown below:
//
//   T trunc_mod = std::fmod(x, y);
//   return trunc_mod != 0 && (y < 0 != trunc_mod < 0) ? trunc_mod + y

  // CHECK-NEXT: return [[SELECT]]
  %0 = "tf.FloorMod"(%arg0, %arg1) : (tensor<?x?xi32>, tensor<?xi32>) -> tensor<?x?xi32>
  func.return %0: tensor<?x?xi32>
}

// -----

// CHECK-LABEL: func @floormod_dynamic_broadcast_denominator
func.func @floormod_dynamic_broadcast_denominator_(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
  // CHECK-NOT: tf.FloorMod

  // CHECK: "tfl.where"(%arg0) : (tensor<3x5xi1>) -> tensor<?x2xi64>
}

func.func @floor_mod(%arg0: tensor<5xf32>, %arg1: tensor<5xf32>) -> tensor<5xf32> {
  %0 = "tf.FloorMod"(%arg0, %arg1) : (tensor<5xf32>, tensor<5xf32>) -> tensor<5xf32>
  func.return %0 : tensor<5xf32>
  // CHECK-LABEL: floor_mod
  // CHECK: "tfl.floor_mod"(%arg0, %arg1) : (tensor<5xf32>, tensor<5xf32>) -> tensor<5xf32>
}

  func.return %15 : tensor<8x128xbf16>
}

// CHECK-LABEL: func @convert_floor_mod_float
// CHECK: %[[RESULT:.*]] = "tf.FloorMod"(%arg0, %arg1) : (tensor<192x8xbf16>, tensor<192x8xbf16>) -> tensor<192x8xbf16>
// CHECK: return %[[RESULT]]
// CHECK: }

            "Lgamma", "Digamma",
            // Binary
            "Add", "AddV2", "Sub", "Mul", "Div", "Atan2", "Complex", "DivNoNan",
            "MulNoNan", "FloorDiv", "Xlogy", "Xlog1py", "Xdivy", "FloorMod",
            "BitwiseAnd", "BitwiseOr", "BitwiseXor", "LeftShift", "RightShift",
            "LogicalAnd", "LogicalOr", "Mod", "Maximum", "Minimum", "RealDiv",

      }
    }

    int floorLog = approxLog10;
    BigInteger floorPow = approxPow;
    int floorCmp = approxCmp;

    switch (mode) {
      case UNNECESSARY:
        checkRoundingUnnecessary(floorCmp == 0);
        // fall through
      case FLOOR:
      case DOWN:
        return floorLog;

      case CEILING:
      case UP:
        return floorPow.equals(x) ? floorLog : floorLog + 1;