// RUN: mlir-hlo-opt %s -hlo-legalize-to-linalg -split-input-file | FILECHECK_OPTS="" FileCheck %s

// CHECK: #map = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-LABEL: func @float_add
func @float_add(%lhs: tensor<2x2xf32>,
                %rhs: tensor<2x2xf32>) -> tensor<2x2xf32> {
  // CHECK: linalg.generic
  // CHECK: ^{{[a-z0-9_]*}}
  // CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]*]]: f32
  // CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]*]]: f32
  // CHECK: %[[RESULT:[a-zA-Z0-9_]*]] = addf %[[ARG0]], %[[ARG1]]
  // CHECK: linalg.yield %[[RESULT]]
  %0 = "mhlo.add"(%lhs, %rhs) : (tensor<2x2xf32>,
                                    tensor<2x2xf32>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}

// -----

// CHECK-LABEL: integer_add
func @integer_add(%lhs: tensor<2x2xi32>,
                  %rhs: tensor<2x2xi32>) -> tensor<2x2xi32> {
  // CHECK: linalg.generic
  // CHECK: addi
  %0 = "mhlo.add"(%lhs, %rhs) : (tensor<2x2xi32>,
                                    tensor<2x2xi32>) -> tensor<2x2xi32>
  return %0 : tensor<2x2xi32>
}

// -----

// CHECK-LABEL: complex_add
func @complex_add(%lhs: tensor<2x2xcomplex<f32>>,
                  %rhs: tensor<2x2xcomplex<f32>>) -> tensor<2x2xcomplex<f32>> {
  // CHECK: linalg.generic
  // CHECK: complex.add
  %0 = "mhlo.add"(%lhs, %rhs) : (tensor<2x2xcomplex<f32>>,
      tensor<2x2xcomplex<f32>>) -> tensor<2x2xcomplex<f32>>
  return %0 : tensor<2x2xcomplex<f32>>
}

// -----

// CHECK-LABEL: func @float_mul
func @float_mul(%lhs: tensor<2x2xf32>,
                %rhs: tensor<2x2xf32>) -> tensor<2x2xf32> {
  // CHECK: linalg.generic
  // CHECK: mulf
  %0 = "mhlo.multiply"(%lhs, %rhs) : (tensor<2x2xf32>,
                                    tensor<2x2xf32>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}

// -----

// CHECK-LABEL: func @integer_mul
func @integer_mul(%lhs: tensor<2x2xi32>,
                  %rhs: tensor<2x2xi32>) -> tensor<2x2xi32> {
  // CHECK: linalg.generic
  // CHECK: muli
  %0 = "mhlo.multiply"(%lhs, %rhs) : (tensor<2x2xi32>,
                                    tensor<2x2xi32>) -> tensor<2x2xi32>
  return %0 : tensor<2x2xi32>
}

// -----

// CHECK-LABEL: func @float_remainder
func @float_remainder(%lhs: tensor<2x2xf32>,
                      %rhs: tensor<2x2xf32>) -> tensor<2x2xf32> {
  // CHECK: linalg.generic
  // CHECK: remf
  %0 = "mhlo.remainder"(%lhs, %rhs) : (tensor<2x2xf32>,
                                    tensor<2x2xf32>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}

// -----

// CHECK-LABEL: func @integer_remainder
func @integer_remainder(%lhs: tensor<2x2xi32>,
                        %rhs: tensor<2x2xi32>) -> tensor<2x2xi32> {
  // CHECK: linalg.generic
  // CHECK: remi_signed
  %0 = "mhlo.remainder"(%lhs, %rhs) : (tensor<2x2xi32>,
                                          tensor<2x2xi32>) -> tensor<2x2xi32>
  return %0 : tensor<2x2xi32>
}

// -----

// CHECK-LABEL: func @float_rsqrt
func @float_rsqrt(%operand: tensor<2x2xf32>) -> tensor<2x2xf32> {
  %tensor_result = "mhlo.rsqrt"(%operand)
      : (tensor<2x2xf32>) -> tensor<2x2xf32>
  // CHECK: linalg.generic
  // CHECK: rsqrt
  return %tensor_result : tensor<2x2xf32>
}

// -----

// CHECK-LABEL: func @float_sub
func @float_sub(%lhs: tensor<2x2xf32>,
                %rhs: tensor<2x2xf32>) -> tensor<2x2xf32> {
  // CHECK: linalg.generic
  // CHECK: subf
  %0 = "mhlo.subtract"(%lhs, %rhs) : (tensor<2x2xf32>,
                                    tensor<2x2xf32>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}

// -----

// CHECK-LABEL: func @integer_sub
func @integer_sub(%lhs: tensor<2x2xi32>,
                  %rhs: tensor<2x2xi32>) -> tensor<2x2xi32> {
  // CHECK: linalg.generic
  // CHECK: subi
  %0 = "mhlo.subtract"(%lhs, %rhs) : (tensor<2x2xi32>,
                                    tensor<2x2xi32>) -> tensor<2x2xi32>
  return %0 : tensor<2x2xi32>
}

// -----

// CHECK-LABEL: complex_sub
func @complex_sub(%lhs: tensor<2x2xcomplex<f32>>,
                  %rhs: tensor<2x2xcomplex<f32>>) -> tensor<2x2xcomplex<f32>> {
  // CHECK: linalg.generic
  // CHECK: complex.sub
  %0 = "mhlo.subtract"(%lhs, %rhs) : (tensor<2x2xcomplex<f32>>,
      tensor<2x2xcomplex<f32>>) -> tensor<2x2xcomplex<f32>>
  return %0 : tensor<2x2xcomplex<f32>>
}

// -----

// CHECK-LABEL: func @float_abs
func @float_abs(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
  // CHECK: linalg.generic
  // CHECK: absf
  %0 = "mhlo.abs"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}

// -----

// CHECK-LABEL: func @float_exp
func @float_exp(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
  // CHECK: linalg.generic
  // CHECK: exp
  %0 = "mhlo.exponential"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}

// -----

// CHECK-LABEL: func @float_expm1
func @float_expm1(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
  // CHECK: linalg.generic
  // CHECK: expm1
  %0 = "mhlo.exponential_minus_one"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}

// -----

// CHECK-LABEL: func @float_log
func @float_log(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
  // CHECK: linalg.generic
  // CHECK: log
  %0 = "mhlo.log"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}

// -----

// CHECK-LABEL: func @float_log1p
func @float_log1p(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
  // CHECK: linalg.generic
  // CHECK: log1p
  %0 = "mhlo.log_plus_one"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}

// -----

// CHECK-LABEL: func @float_logistic
func @float_logistic(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
  // CHECK: linalg.generic
  // CHECK: ^bb0(%[[ARG:.*]]: f32, %{{.*}}: f32):
  // CHECK: %[[C1:.*]] = constant 1.{{.*}}e+00
  // CHECK: %[[NEG_ARG:.*]] = negf %[[ARG]]
  // CHECK: %[[EXP_NEG_ARG:.*]] = math.exp %[[NEG_ARG]]
  // CHECK: %[[ONE_ADD_EXP_NEG_ARG:.*]] = addf %[[C1]], %[[EXP_NEG_ARG]]
  // CHECK: %[[RESULT:.*]] = divf %[[C1]], %[[ONE_ADD_EXP_NEG_ARG]]
  // CHECK: linalg.yield %[[RESULT]]
  %0 = "mhlo.logistic"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}

// -----

// CHECK-LABEL: func @float_ceil
func @float_ceil(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
  // CHECK: linalg.generic
  // CHECK: ceilf
  %0 = "mhlo.ceil"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}

// -----

// CHECK-LABEL: func @floor
func @floor(%input: tensor<2x2xf32>) -> tensor<2x2xf32> {
  // CHECK: linalg.generic
  // CHECK: floorf
  %0 = "mhlo.floor"(%input) : (tensor<2x2xf32>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}

// -----

// CHECK-LABEL: func @float_neg
func @float_neg(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
  // CHECK: linalg.generic
  // CHECK: negf
  %0 = "mhlo.negate"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}

// -----

// CHECK-LABEL: func @float_tanh
func @float_tanh(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
  // CHECK: linalg.generic
  // CHECK: tanh
  %0 = "mhlo.tanh"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}

// -----

// CHECK-LABEL: func @integer_and
func @integer_and(%lhs: tensor<2x2xi32>,
                  %rhs: tensor<2x2xi32>) -> tensor<2x2xi32> {
  // CHECK: linalg.generic
  // CHECK: and
  %0 = "mhlo.and"(%lhs, %rhs) : (tensor<2x2xi32>,
                                    tensor<2x2xi32>) -> tensor<2x2xi32>
  return %0 : tensor<2x2xi32>
}

// -----

// CHECK-LABEL: func @integer_or
func @integer_or(%lhs: tensor<2x2xi32>,
                  %rhs: tensor<2x2xi32>) -> tensor<2x2xi32> {
  // CHECK: linalg.generic
  // CHECK: or
  %0 = "mhlo.or"(%lhs, %rhs) : (tensor<2x2xi32>,
                                    tensor<2x2xi32>) -> tensor<2x2xi32>
  return %0 : tensor<2x2xi32>
}

// -----

// CHECK-LABEL: func @integer_xor
func @integer_xor(%lhs: tensor<2x2xi32>,
                  %rhs: tensor<2x2xi32>) -> tensor<2x2xi32> {
  // CHECK: linalg.generic
  // CHECK: xor
  %0 = "mhlo.xor"(%lhs, %rhs) : (tensor<2x2xi32>,
                                    tensor<2x2xi32>) -> tensor<2x2xi32>
  return %0 : tensor<2x2xi32>
}

// -----

// CHECK-LABEL: func @float_cmp
func @float_cmp(%lhs: tensor<2x2xf32>,
                %rhs: tensor<2x2xf32>) -> (tensor<2x2xi1>) {
  %0 = "mhlo.compare"(%lhs, %rhs) {comparison_direction = "EQ"}
          : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xi1>
  return %0 : tensor<2x2xi1>
}
// CHECK: linalg.init_tensor [2, 2] : tensor<2x2xi1>
// CHECK: linalg.generic
// CHECK-NEXT: ^bb0(%[[LHS_IN:.*]]: f32, %[[RHS_IN:.*]]: f32, %{{.*}}: i1):
// CHECK-NEXT:   %[[RESULT:.*]] = cmpf oeq, %[[LHS_IN]], %[[RHS_IN]] : f32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i1

// -----

// CHECK-LABEL: func @float_cmp_ne
func @float_cmp_ne(%lhs: tensor<2x2xf32>,
                %rhs: tensor<2x2xf32>) -> (tensor<2x2xi1>) {
  %0 = "mhlo.compare"(%lhs, %rhs) {comparison_direction = "NE"}
          : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xi1>
  return %0 : tensor<2x2xi1>
}
// CHECK: linalg.init_tensor [2, 2] : tensor<2x2xi1>
// CHECK: linalg.generic
// CHECK-NEXT: ^bb0(%[[LHS_IN:.*]]: f32, %[[RHS_IN:.*]]: f32, %{{.*}}: i1):
// CHECK-NEXT:   %[[RESULT:.*]] = cmpf une, %[[LHS_IN]], %[[RHS_IN]] : f32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i1

// -----

// CHECK-LABEL: func @int_cmp
func @int_cmp(%lhs: tensor<2x2xi32>,
              %rhs: tensor<2x2xi32>) -> tensor<2x2xi1> {
  %0 = "mhlo.compare"(%lhs, %rhs) {comparison_direction = "LT"}
          : (tensor<2x2xi32>, tensor<2x2xi32>) -> (tensor<2x2xi1>)
  return %0 : tensor<2x2xi1>
}
// CHECK: linalg.init_tensor [2, 2] : tensor<2x2xi1>
// CHECK: linalg.generic
// CHECK-NEXT: ^bb0(%[[LHS_IN:.*]]: i32, %[[RHS_IN:.*]]: i32, %{{.*}}: i1):
// CHECK-NEXT:   %[[RESULT:.*]] = cmpi slt, %[[LHS_IN]], %[[RHS_IN]] : i32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i1

// -----

// CHECK-LABEL: func @float_cos
func @float_cos(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
  // CHECK: linalg.generic
  // CHECK: cos
  %0 = "mhlo.cosine"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}

// -----

// CHECK-LABEL: func @float_sin
func @float_sin(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
  // CHECK: linalg.generic
  // CHECK: sin
  %0 = "mhlo.sine"(%arg0) : (tensor<2x2xf32>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}

// -----

// CHECK-LABEL: func @copy
// CHECK-SAME: [[ARG:%[a-zA-Z0-9]+]]
func @copy(%input: tensor<2x4x8xf32>) -> tensor<2x4x8xf32> {
  %0 = "mhlo.copy"(%input) : (tensor<2x4x8xf32>) -> (tensor<2x4x8xf32>)
  return %0 : tensor<2x4x8xf32>
}
// CHECK: return [[ARG]] : tensor<2x4x8xf32>

// -----

// CHECK-LABEL: func @is_finte
func @is_finte(%input: tensor<2x2xf32>) -> tensor<2x2xi1> {
  %0 = "mhlo.is_finite"(%input) : (tensor<2x2xf32>) -> tensor<2x2xi1>
  return %0 : tensor<2x2xi1>
}
// CHECK: linalg.generic
// CHECK-NEXT: ^bb0(%[[OPERAND_IN:.*]]: f32
// CHECK-NEXT:   %[[POS_INF:.+]] = constant 0x7F800000 : f32
// CHECK-NEXT:   %[[ABS_X:.+]] = absf %[[OPERAND_IN]] : f32
// CHECK-NEXT:   %[[RESULT:.+]] = cmpf one, %[[ABS_X]], %[[POS_INF]] : f32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i1

// -----

// CHECK-LABEL: func @select
func @select(%pred: tensor<2x2xi1>, %lhs: tensor<2x2xf32>,
             %rhs: tensor<2x2xf32>) -> tensor<2x2xf32> {
  %0 = "mhlo.select"(%pred, %lhs, %rhs)
         : (tensor<2x2xi1>, tensor<2x2xf32>, tensor<2x2xf32>) -> (tensor<2x2xf32>)
  return %0 : tensor<2x2xf32>
}
// CHECK: linalg.init_tensor [2, 2] : tensor<2x2xf32>
// CHECK: linalg.generic
// CHECK-NEXT: ^bb0(%[[PRED_IN:.*]]: i1, %[[LHS_IN:.*]]: f32, %[[RHS_IN:.*]]: f32, %{{.*}}: f32):
// CHECK-NEXT:   %[[RESULT:.*]] = select %[[PRED_IN]], %[[LHS_IN]], %[[RHS_IN]] : f32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : f32

// -----

// CHECK-DAG: #[[OPERAND_MAP:.+]] = affine_map<(d0, d1, d2) -> ()>
// CHECK-DAG: #[[RESULT_MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK-LABEL: func @broadcast_scalar
func @broadcast_scalar(%arg: tensor<f32>) -> tensor<4x2x1xf32> {
  %0 = "mhlo.broadcast"(%arg) {broadcast_sizes = dense<[4, 2, 1]> : tensor<3xi64>} : (tensor<f32>) -> tensor<4x2x1xf32>
  return %0: tensor<4x2x1xf32>
}
// CHECK: linalg.init_tensor [4, 2, 1] : tensor<4x2x1xf32>
// CHECK: linalg.generic {{{.*}}indexing_maps = [#[[OPERAND_MAP]], #[[RESULT_MAP]]]
// CHECK-NEXT: ^bb0(%[[OPERAND:.*]]: f32, %{{.*}}: f32):
// CHECK-NEXT:   linalg.yield %[[OPERAND]] : f32

// -----

// CHECK-DAG: #[[OPERAND_MAP:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d3, d4, d5)>
// CHECK-DAG: #[[RESULT_MAP:.+]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1, d2, d3, d4, d5)>
// CHECK-LABEL: func @broadcast
func @broadcast(%arg: tensor<4x?x16xf32>) -> tensor<4x2x1x4x?x16xf32> {
  %0 = "mhlo.broadcast"(%arg) {broadcast_sizes = dense<[4, 2, 1]> : tensor<3xi64>} : (tensor<4x?x16xf32>) -> tensor<4x2x1x4x?x16xf32>
  return %0: tensor<4x2x1x4x?x16xf32>
}
// CHECK: %[[C1:.*]] = constant 1 : index
// CHECK: %[[D1:.*]] = memref.dim %{{.*}}, %[[C1]] : tensor<4x?x16xf32>
// CHECK: linalg.init_tensor [4, 2, 1, 4, %[[D1]], 16] : tensor<4x2x1x4x?x16xf32>
// CHECK: linalg.generic {{{.*}}indexing_maps = [#[[OPERAND_MAP]], #[[RESULT_MAP]]]
// CHECK-NEXT: ^bb0(%[[OPERAND:.*]]: f32, %{{.*}}: f32):
// CHECK-NEXT:   linalg.yield %[[OPERAND]] : f32

// -----

// CHECK-DAG: #[[OPERAND_MAP:.*]] = affine_map<(d0, d1, d2, d3, d4) -> (d4, d0, 0)>
// CHECK-DAG: #[[RESULT_MAP:.*]] = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2, d3, d4)>
// CHECK-LABEL: func @broadcast_in_dim
func @broadcast_in_dim(%operand: tensor<5x7x1xf32>) -> tensor<7x10x6x4x5xf32> {
  %0 = "mhlo.broadcast_in_dim"(%operand)
         {broadcast_dimensions = dense<[4,0,2]> : tensor<3xi64>}
         : (tensor<5x7x1xf32>) -> tensor<7x10x6x4x5xf32>
  return %0 : tensor<7x10x6x4x5xf32>
}
// CHECK: linalg.init_tensor [7, 10, 6, 4, 5] : tensor<7x10x6x4x5xf32>
// CHECK: linalg.generic {{{.*}}indexing_maps = [#[[OPERAND_MAP]], #[[RESULT_MAP]]]
// CHECK-NEXT: ^bb0(%[[OPERAND:.*]]: f32, %{{.*}}: f32):
// CHECK-NEXT:   linalg.yield %[[OPERAND]] : f32

// -----

// CHECK-DAG: #[[OPERAND_MAP:.+]] = affine_map<(d0, d1) -> (d0)>
// CHECK-DAG: #[[RESULT_MAP:.+]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-LABEL: func @broadcast_in_dim_with_one_to_one
func @broadcast_in_dim_with_one_to_one(
         %operand: tensor<1xf32>) -> tensor<1x5xf32> {
  %0 = "mhlo.broadcast_in_dim"(%operand)
         {broadcast_dimensions = dense<[0]> : tensor<1xi64>}
         : (tensor<1xf32>) -> tensor<1x5xf32>
  return %0 : tensor<1x5xf32>
}
// CHECK: linalg.init_tensor [1, 5] : tensor<1x5xf32>
// CHECK: linalg.generic {{{.*}}indexing_maps = [#[[OPERAND_MAP]], #[[RESULT_MAP]]]
// CHECK-NEXT: ^bb0(%[[OPERAND:.*]]: f32, %{{.*}}: f32):
// CHECK-NEXT:   linalg.yield %[[OPERAND]] : f32

// -----

// CHECK-DAG: #[[OPERAND_MAP:.*]] = affine_map<(d0, d1, d2) -> ()>
// CHECK-DAG: #[[RESULT_MAP:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK-LABEL: func @broadcast_scalar
func @broadcast_scalar(%operand: tensor<f32>) -> tensor<7x10x6xf32> {
  %0 = "mhlo.broadcast_in_dim"(%operand)
        {broadcast_dimensions = dense<[]> : tensor<0xi64>}
        : (tensor<f32>) -> tensor<7x10x6xf32>
  return %0 : tensor<7x10x6xf32>
}
// CHECK: linalg.init_tensor [7, 10, 6] : tensor<7x10x6xf32>
// CHECK: linalg.generic {{{.*}}indexing_maps = [#[[OPERAND_MAP]], #[[RESULT_MAP]]]
// CHECK-NEXT: ^bb0(%[[OPERAND:.*]]: f32, %{{.*}}: f32):
// CHECK-NEXT:   linalg.yield %[[OPERAND]] : f32

// -----

// CHECK-DAG: #[[OPERAND_MAP:.*]] = affine_map<(d0, d1, d2, d3) -> (d1, d0, d3, d2)>
// CHECK-DAG: #[[RESULT_MAP:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
// CHECK-LABEL: func @transpose
func @transpose(%arg0: tensor<2x3x9x5xi32>) -> tensor<3x2x5x9xi32> {
  %0 = "mhlo.transpose"(%arg0) {permutation = dense<[1, 0, 3, 2]> : tensor<4xi64>}
        : (tensor<2x3x9x5xi32>) -> tensor<3x2x5x9xi32>
  return %0 : tensor<3x2x5x9xi32>
}
// CHECK: linalg.generic {{{.*}}indexing_maps = [#[[OPERAND_MAP]], #[[RESULT_MAP]]]

// -----

// CHECK-LABEL: func @reshape_0D_1D
func @reshape_0D_1D(%arg0: tensor<i32>) -> tensor<1xi32> {
  %0 = "mhlo.reshape"(%arg0) : (tensor<i32>) -> tensor<1xi32>
  return %0 : tensor<1xi32>
}
// CHECK: linalg.tensor_reshape %{{.*}} [] : tensor<i32> into tensor<1xi32>

// -----

// CHECK-LABEL: func @reshape_1D_0D
func @reshape_1D_0D(%arg0: tensor<1xi32>) -> tensor<i32> {
  %0 = "mhlo.reshape"(%arg0) : (tensor<1xi32>) -> tensor<i32>
  return %0 : tensor<i32>
}
// CHECK: linalg.tensor_reshape %{{.*}} [] : tensor<1xi32> into tensor<i32>

// -----

// CHECK-DAG: #[[RESHAPE_MAP1:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
// CHECK-DAG: #[[RESHAPE_MAP2:.*]] = affine_map<(d0, d1, d2) -> (d2)>
// CHECK-LABEL: func @reshape_3D_2D
func @reshape_3D_2D(%arg0: tensor<12x1x42xi32>) -> tensor<12x42xi32> {
  %0 = "mhlo.reshape"(%arg0) : (tensor<12x1x42xi32>) -> tensor<12x42xi32>
  return %0 : tensor<12x42xi32>
}
// CHECK: linalg.tensor_reshape %{{.*}} [#[[RESHAPE_MAP1]], #[[RESHAPE_MAP2]]]

// -----

// CHECK-DAG: #[[RESHAPE_MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0)>
// CHECK-DAG: #[[RESHAPE_MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d1, d2, d3)>
// CHECK-LABEL: func @reshape_4D_2D
func @reshape_4D_2D(%arg0: tensor<12x42x1x1xi32>) -> tensor<12x42xi32> {
  %0 = "mhlo.reshape"(%arg0) : (tensor<12x42x1x1xi32>) -> tensor<12x42xi32>
  return %0 : tensor<12x42xi32>
}
// CHECK: linalg.tensor_reshape %{{.*}} [#[[RESHAPE_MAP1]], #[[RESHAPE_MAP2]]]

// -----

// CHECK-DAG: #[[RESHAPE_MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1)>
// CHECK-DAG: #[[RESHAPE_MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d2, d3)>
// CHECK-LABEL: func @reshape_2D_4D
func @reshape_2D_4D(%arg0: tensor<12x42xi32>) -> tensor<12x1x42x1xi32> {
  %0 = "mhlo.reshape"(%arg0) : (tensor<12x42xi32>) -> tensor<12x1x42x1xi32>
  return %0 : tensor<12x1x42x1xi32>
}
// CHECK: linalg.tensor_reshape %{{.*}} [#[[RESHAPE_MAP1]], #[[RESHAPE_MAP2]]]

// -----

// CHECK-DAG: #[[RESHAPE_MAP1:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK-DAG: #[[RESHAPE_MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
// CHECK-LABEL: func @reshape_3D_4D
func @reshape_3D_4D(%arg0: tensor<1x49x16xf32>) -> tensor<1x784x1x1xf32> {
  %0 = "mhlo.reshape"(%arg0) : (tensor<1x49x16xf32>) -> tensor<1x784x1x1xf32>
  return %0 : tensor<1x784x1x1xf32>
}
// CHECK: linalg.tensor_reshape %{{.*}} [#[[RESHAPE_MAP1]]]
// CHECK: linalg.tensor_reshape %{{.*}} [#[[RESHAPE_MAP2]]]

// -----

// CHECK-DAG: #[[RESHAPE_MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
// CHECK-DAG: #[[RESHAPE_MAP2:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK-LABEL: func @reshape_4D_3D
func @reshape_4D_3D(%arg0: tensor<1x8x10x3xf32>) -> tensor<1x240x1xf32> {
  %0 = "mhlo.reshape"(%arg0) : (tensor<1x8x10x3xf32>) -> tensor<1x240x1xf32>
  return %0 : tensor<1x240x1xf32>
}
// CHECK: linalg.tensor_reshape %{{.*}} [#[[RESHAPE_MAP1]]]
// CHECK: linalg.tensor_reshape %{{.*}} [#[[RESHAPE_MAP2]]]

// -----

// CHECK-DAG: #[[MAP:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
// CHECK-LABEL: func @reshape1_4D_4D
func @reshape1_4D_4D(%arg0: tensor<4x512x1x1xi32>) -> tensor<1x4x1x512xi32> {
  %0 = "mhlo.reshape"(%arg0) : (tensor<4x512x1x1xi32>) -> tensor<1x4x1x512xi32>
  return %0 : tensor<1x4x1x512xi32>
}
// CHECK: linalg.tensor_reshape %{{.*}} [#[[MAP]]]
// CHECK: linalg.tensor_reshape %{{.*}} [#[[MAP]]]

// -----

// CHECK-DAG: #[[MAP:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
// CHECK-LABEL: func @reshape2_4D_4D
func @reshape2_4D_4D(%arg0: tensor<4x1x1x1024xi32>) -> tensor<4x1024x1x1xi32> {
  %0 = "mhlo.reshape"(%arg0) : (tensor<4x1x1x1024xi32>) -> tensor<4x1024x1x1xi32>
  return %0 : tensor<4x1024x1x1xi32>
}
// CHECK: linalg.tensor_reshape %{{.*}} [#[[MAP]]]
// CHECK: linalg.tensor_reshape %{{.*}} [#[[MAP]]]

// -----

// CHECK-LABEL: func @minf
func @minf(%lhs: tensor<2x2xf32>, %rhs: tensor<2x2xf32>) -> tensor<2x2xf32> {
  %0 = "mhlo.minimum"(%lhs, %rhs)
          : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}
// CHECK: linalg.init_tensor [2, 2] : tensor<2x2xf32>
// CHECK: linalg.generic
// CHECK-NEXT: ^bb0(%[[LHS_IN:.*]]: f32, %[[RHS_IN:.*]]: f32, %{{.*}}: f32):
// CHECK-NEXT:   %[[CMP:.*]] = cmpf olt, %[[LHS_IN]], %[[RHS_IN]] : f32
// CHECK-NEXT:   %[[MIN:.*]] = select %[[CMP]], %[[LHS_IN]], %[[RHS_IN]] : f32
// CHECK-NEXT:   %[[ISNAN:.*]] = cmpf uno, %[[LHS_IN]], %[[RHS_IN]] : f32
// CHECK-NEXT:   %[[NAN:.*]] = constant 0x7FC00000 : f32
// CHECK-NEXT:   %[[RESULT:.*]] = select %[[ISNAN]], %[[NAN]], %[[MIN]] : f32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : f32

// -----

// CHECK-LABEL: func @maxi
func @maxi(%lhs: tensor<2x2xi32>, %rhs: tensor<2x2xi32>) -> tensor<2x2xi32> {
  %0 = "mhlo.maximum"(%lhs, %rhs)
          : (tensor<2x2xi32>, tensor<2x2xi32>) -> tensor<2x2xi32>
  return %0 : tensor<2x2xi32>
}
// CHECK: linalg.init_tensor [2, 2] : tensor<2x2xi32>
// CHECK: linalg.generic
// CHECK-NEXT: ^bb0(%[[LHS_IN:.*]]: i32, %[[RHS_IN:.*]]: i32, %{{.*}}: i32):
// CHECK-NEXT:   %[[CMP:.*]] = cmpi sgt, %[[LHS_IN]], %[[RHS_IN]] : i32
// CHECK-NEXT:   %[[RESULT:.*]] = select %[[CMP]], %[[LHS_IN]], %[[RHS_IN]] : i32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i32

// -----

// CHECK-DAG: #[[MAP:.*]] = affine_map<() -> ()>
// CHECK-LABEL: func @add_scalar
func @add_scalar(%lhs: tensor<f32>, %rhs: tensor<f32>) -> tensor<f32> {
  %0 = "mhlo.add"(%lhs, %rhs) : (tensor<f32>, tensor<f32>) -> tensor<f32>
  return %0 : tensor<f32>
}
// CHECK: linalg.init_tensor
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]], #[[MAP]]]
// CHECK-NEXT: ^bb0(%[[LHS:.*]]: f32, %[[RHS:.*]]: f32, %{{.*}}: f32):
// CHECK: %[[RESULT:.*]] = addf %[[LHS]], %[[RHS]]
// CHECK-NEXT:   linalg.yield %[[RESULT]] : f32

// -----

func @reshape_collapse_single_dim
  (%arg0: tensor<1x28x28x1xf32>) -> tensor<1x784xf32> {
  %0 = "mhlo.reshape"(%arg0) : (tensor<1x28x28x1xf32>) -> tensor<1x784xf32>
  return %0 : tensor<1x784xf32>
}
//   CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d0)>
//   CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d1, d2, d3)>
// CHECK-LABEL: func @reshape_collapse_single_dim
//       CHECK: linalg.tensor_reshape %{{.*}} [#[[MAP0]], #[[MAP1]]]

// -----

func @reshape_collapse(%arg0: tensor<2x2x2x3xf32>) -> tensor<2x4x3xf32> {
    %0 = "mhlo.reshape"(%arg0) : (tensor<2x2x2x3xf32>) -> tensor<2x4x3xf32>
    return %0 : tensor<2x4x3xf32>
}
//   CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d0)>
//   CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d1, d2)>
//   CHECK-DAG: #[[MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d3)>
// CHECK-LABEL: func @reshape_collapse
//       CHECK: linalg.tensor_reshape %{{.*}} [#[[MAP0]], #[[MAP1]], #[[MAP2]]]

// -----

func @reshape_expand(%arg0: tensor<2x8xf32>) -> tensor<2x4x2xf32> {
    %0 = "mhlo.reshape"(%arg0) : (tensor<2x8xf32>) -> tensor<2x4x2xf32>
    return %0 : tensor<2x4x2xf32>
}
//   CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1, d2) -> (d0)>
//   CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1, d2) -> (d1, d2)>
// CHECK-LABEL: func @reshape_expand
//       CHECK: linalg.tensor_reshape %{{.*}} [#[[MAP0]], #[[MAP1]]]

// -----

func @reshape_single_expand(%arg0 : tensor<8xf32>) -> tensor<1x4x2xf32> {
    %0 = "mhlo.reshape"(%arg0) : (tensor<8xf32>) -> tensor<1x4x2xf32>
    return %0 : tensor<1x4x2xf32>
}
//       CHECK: #[[MAP0:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK-LABEL: func @reshape_single_expand
//       CHECK: linalg.tensor_reshape %{{.*}} [#[[MAP0]]]

// -----

func @reshape_multiple_collapse
  (%arg0 : tensor<1x2x2x5x3x2xf32>) -> tensor<1x4x5x6xf32> {
    %0 = "mhlo.reshape"(%arg0) : (tensor<1x2x2x5x3x2xf32>) -> tensor<1x4x5x6xf32>
    return %0 : tensor<1x4x5x6xf32>
}
//   CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0)>
//   CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d1, d2)>
//   CHECK-DAG: #[[MAP2:.*]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d3)>
//   CHECK-DAG: #[[MAP3:.*]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d4, d5)>
// CHECK-LABEL: func @reshape_multiple_collapse
//       CHECK: linalg.tensor_reshape %{{.*}} [#[[MAP0]], #[[MAP1]], #[[MAP2]], #[[MAP3]]]

// -----

// CHECK-LABEL: func @convert_i1_to_f32
func @convert_i1_to_f32(%input: tensor<2x2xi1>) -> tensor<2x2xf32> {
  %result = "mhlo.convert"(%input) : (tensor<2x2xi1>) -> tensor<2x2xf32>
  return %result : tensor<2x2xf32>
}
// CHECK: linalg.init_tensor
// CHECK: linalg.generic
// CHECK-NEXT: ^bb0(%[[OPERAND_IN:.*]]: i1, %{{.*}}: f32):
// CHECK-NEXT:   %[[RESULT:.*]] = uitofp %[[OPERAND_IN]] : i1 to f32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : f32

// -----

// CHECK-LABEL: func @convert_i1_to_i32
func @convert_i1_to_i32(%input: tensor<2x2xi1>) -> tensor<2x2xi32> {
  %result = "mhlo.convert"(%input) : (tensor<2x2xi1>) -> tensor<2x2xi32>
  return %result : tensor<2x2xi32>
}
// CHECK: linalg.init_tensor
// CHECK: linalg.generic
// CHECK-NEXT: ^bb0(%[[OPERAND_IN:.*]]: i1, %{{.*}}: i32):
// CHECK-NEXT:   %[[RESULT:.*]] = zexti %[[OPERAND_IN]] : i1 to i32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i32

// -----

// CHECK-LABEL: func @convert_i32_to_f32
func @convert_i32_to_f32(%input: tensor<2x2xi32>) -> tensor<2x2xf32> {
  %result = "mhlo.convert"(%input) : (tensor<2x2xi32>) -> tensor<2x2xf32>
  return %result : tensor<2x2xf32>
}
// CHECK: linalg.init_tensor
// CHECK: linalg.generic
// CHECK-NEXT: ^bb0(%[[OPERAND_IN:.*]]: i32, %{{.*}}: f32):
// CHECK-NEXT:   %[[RESULT:.*]] = sitofp %[[OPERAND_IN]] : i32 to f32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : f32

// -----

// CHECK-LABEL: func @convert_i16_to_i32
func @convert_i16_to_i32(%input: tensor<2x2xi16>) -> tensor<2x2xi32> {
  %result = "mhlo.convert"(%input) : (tensor<2x2xi16>) -> tensor<2x2xi32>
  return %result : tensor<2x2xi32>
}
// CHECK: linalg.init_tensor
// CHECK: linalg.generic
// CHECK-NEXT: ^bb0(%[[OPERAND_IN:.*]]: i16, %{{.*}}: i32):
// CHECK-NEXT:   %[[RESULT:.*]] = sexti %[[OPERAND_IN]] : i16 to i32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i32

// -----

// CHECK-LABEL: func @convert_i32_to_i16
func @convert_i32_to_i16(%input: tensor<2x2xi32>) -> tensor<2x2xi16> {
  %result = "mhlo.convert"(%input) : (tensor<2x2xi32>) -> tensor<2x2xi16>
  return %result : tensor<2x2xi16>
}
// CHECK: linalg.init_tensor
// CHECK: linalg.generic
// CHECK-NEXT: ^bb0(%[[OPERAND_IN:.*]]: i32, %{{.*}}: i16):
// CHECK-NEXT:   %[[RESULT:.*]] = trunci %[[OPERAND_IN]] : i32 to i16
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i16

// -----

// CHECK-LABEL: func @convert_f32_to_f64
func @convert_f32_to_f64(%input: tensor<2x2xf32>) -> tensor<2x2xf64> {
  %result = "mhlo.convert"(%input) : (tensor<2x2xf32>) -> tensor<2x2xf64>
  return %result : tensor<2x2xf64>
}
// CHECK: linalg.init_tensor
// CHECK: linalg.generic
// CHECK-NEXT: ^bb0(%[[OPERAND_IN:.*]]: f32, %{{.*}}: f64):
// CHECK-NEXT:   %[[RESULT:.*]] = fpext %[[OPERAND_IN]] : f32 to f64
// CHECK-NEXT:   linalg.yield %[[RESULT]] : f64

// -----

// CHECK-LABEL: func @convert_f64_to_f32
func @convert_f64_to_f32(%input: tensor<2x2xf64>) -> tensor<2x2xf32> {
  %result = "mhlo.convert"(%input) : (tensor<2x2xf64>) -> tensor<2x2xf32>
  return %result : tensor<2x2xf32>
}
// CHECK: linalg.init_tensor
// CHECK: linalg.generic
// CHECK-NEXT: ^bb0(%[[OPERAND_IN:.*]]: f64, %{{.*}}: f32):
// CHECK-NEXT:   %[[RESULT:.*]] = fptrunc %[[OPERAND_IN]] : f64 to f32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : f32

// -----

// CHECK-LABEL: func @convert_i32_to_i1
func @convert_i32_to_i1(%input: tensor<2x2xi32>) -> tensor<2x2xi1> {
  %result = "mhlo.convert"(%input) : (tensor<2x2xi32>) -> tensor<2x2xi1>
  return %result : tensor<2x2xi1>
}
// CHECK: linalg.init_tensor
// CHECK: linalg.generic
// CHECK-NEXT: ^bb0(%[[OPERAND_IN:.*]]: i32, %{{.*}}: i1):
// CHECK-NEXT:   %[[ZERO:.*]] = constant 0 : i32
// CHECK-NEXT:   %[[RESULT:.*]] = cmpi ne, %[[OPERAND_IN]], %[[ZERO]] : i32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i1

// -----

// CHECK-LABEL: func @convert_f32_to_i1
func @convert_f32_to_i1(%input: tensor<2x2xf32>) -> tensor<2x2xi1> {
  %result = "mhlo.convert"(%input) : (tensor<2x2xf32>) -> tensor<2x2xi1>
  return %result : tensor<2x2xi1>
}
// CHECK: linalg.init_tensor
// CHECK: linalg.generic
// CHECK-NEXT: ^bb0(%[[OPERAND_IN:.*]]: f32, %{{.*}}: i1):
// CHECK-NEXT:   %[[ZERO:.*]] = constant 0.000000e+00 : f32
// CHECK-NEXT:   %[[RESULT:.*]] = cmpf une, %[[OPERAND_IN]], %[[ZERO]] : f32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i1

// -----

// CHECK-LABEL: func @convert_f32_to_i32
func @convert_f32_to_i32(%input: tensor<2x2xf32>) -> tensor<2x2xi32> {
  %result = "mhlo.convert"(%input) : (tensor<2x2xf32>) -> tensor<2x2xi32>
  return %result : tensor<2x2xi32>
}
// CHECK: linalg.init_tensor
// CHECK: linalg.generic
// CHECK-NEXT: ^bb0(%[[OPERAND_IN:.*]]: f32, %{{.*}}: i32):
// CHECK-NEXT:   %[[RESULT:.*]] = fptosi %[[OPERAND_IN]] : f32 to i32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i32

// -----

// CHECK-DAG: #[[OPERAND_MAP:.*]] = affine_map<(d0, d1) -> (d0, -d1 + 2)>
// CHECK-DAG: #[[RESULT_MAP:.*]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-LABEL: func @reverse
func @reverse(%input: tensor<2x3xf32>) -> tensor<2x3xf32> {
  %result = "mhlo.reverse"(%input) {
    dimensions = dense<1> : tensor<1xi64>
  } : (tensor<2x3xf32>) -> tensor<2x3xf32>
  return %result : tensor<2x3xf32>
}
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[OPERAND_MAP]], #[[RESULT_MAP]]]

// -----

// CHECK: #[[RESULT_MAP:.*]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-LABEL: func @iota
func @iota() -> tensor<7x10xf32> {
  %result = "mhlo.iota"() {iota_dimension = 1 : i64} : () -> (tensor<7x10xf32>)
  return %result : tensor<7x10xf32>
}
// CHECK: linalg.init_tensor
// CHECK: linalg.indexed_generic
// CHECK-SAME: indexing_maps = [#[[RESULT_MAP]]]
// CHECK-NEXT: ^bb0(%[[D0:.*]]: index, %[[D1:.*]]: index, %{{.*}}: f32):
// CHECK-NEXT:   %[[INT_CAST:.*]] = index_cast %[[D1]] : index to i32
// CHECK-NEXT:   %[[FLOAT_CAST:.*]] = sitofp %[[INT_CAST]] : i32 to f32
// CHECK-NEXT:   linalg.yield %[[FLOAT_CAST]] : f32

// -----

// CHECK: #[[RESULT_MAP:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK-LABEL: func @iota
// CHECK-SAME: %[[SHAPE:.*]]: tensor<?xi32>
func @iota(%shape: tensor<?xi32>) -> tensor<?x?x8xf32> {
  %result = "mhlo.dynamic_iota"(%shape) {iota_dimension = 1 : i64} : (tensor<?xi32>) -> (tensor<?x?x8xf32>)
  return %result : tensor<?x?x8xf32>
}
// CHECK: %[[E1:.*]] = tensor.extract %[[SHAPE]][%c0] : tensor<?xi32>
// CHECK: %[[I1:.*]] = index_cast %[[E1]] : i32 to index
// CHECK: %[[E2:.*]] = tensor.extract %[[SHAPE]][%c1] : tensor<?xi32>
// CHECK: %[[I2:.*]] = index_cast %[[E2]] : i32 to index
// CHECK: linalg.init_tensor [%[[I1]], %[[I2]], 8] : tensor<?x?x8xf32>
// CHECK: linalg.indexed_generic
// CHECK-SAME: indexing_maps = [#[[RESULT_MAP]]]
// CHECK-NEXT: ^bb0(%[[D0:.*]]: index, %[[D1:.*]]: index, %[[D2:.*]]: index, %{{.*}}: f32):
// CHECK-NEXT:   %[[INT_CAST:.*]] = index_cast %[[D1]] : index to i32
// CHECK-NEXT:   %[[FLOAT_CAST:.*]] = sitofp %[[INT_CAST]] : i32 to f32
// CHECK-NEXT:   linalg.yield %[[FLOAT_CAST]] : f32

// -----

func @shift_left(%lhs: tensor<2x2xi32>,
                 %rhs: tensor<2x2xi32>) -> tensor<2x2xi32> {
  %result = "mhlo.shift_left"(%lhs, %rhs)
      : (tensor<2x2xi32>, tensor<2x2xi32>) -> tensor<2x2xi32>
  return %result : tensor<2x2xi32>
}
// CHECK-LABEL: func @shift_left
// CHECK: linalg.init_tensor
// CHECK: linalg.generic
// CHECK-NEXT: ^bb0(%[[LHS:.*]]: i32, %[[RHS:.*]]: i32, %{{.*}}: i32):
// CHECK-NEXT:   %[[RESULT:.*]] = shift_left %[[LHS]], %[[RHS]] : i32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i32

// -----

func @shift_right_arithmetic(%lhs: tensor<2x2xi32>,
                             %rhs: tensor<2x2xi32>) -> tensor<2x2xi32> {
  %result = "mhlo.shift_right_arithmetic"(%lhs, %rhs)
      : (tensor<2x2xi32>, tensor<2x2xi32>) -> tensor<2x2xi32>
  return %result : tensor<2x2xi32>
}
// CHECK-LABEL: func @shift_right_arithmetic
// CHECK: linalg.init_tensor
// CHECK: linalg.generic
// CHECK-NEXT: ^bb0(%[[LHS:.*]]: i32, %[[RHS:.*]]: i32, %{{.*}}: i32):
// CHECK-NEXT:   %[[RESULT:.*]] = shift_right_signed %[[LHS]], %[[RHS]] : i32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i32

// -----

func @shift_right_logical(%lhs: tensor<2x2xi32>,
                          %rhs: tensor<2x2xi32>) -> tensor<2x2xi32> {
  %result = "mhlo.shift_right_logical"(%lhs, %rhs)
      : (tensor<2x2xi32>, tensor<2x2xi32>) -> tensor<2x2xi32>
  return %result : tensor<2x2xi32>
}
// CHECK-LABEL: func @shift_right_logical
// CHECK: linalg.init_tensor
// CHECK: linalg.generic
// CHECK-NEXT: ^bb0(%[[LHS:.*]]: i32, %[[RHS:.*]]: i32, %{{.*}}: i32):
// CHECK-NEXT:   %[[RESULT:.*]] = shift_right_unsigned %[[LHS]], %[[RHS]] : i32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i32

// -----

// CHECK-LABEL: func @constant
func @constant() {
  %result = "mhlo.constant"() {
    value = dense<10> : tensor<i32>
  } : () -> (tensor<i32>)
  return
}
// CHECK: %[[CONSTANT:.*]] = constant dense<10> : tensor<i32>

// -----

// CHECK: #map = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-LABEL: func @float_pow
func @float_pow(%lhs: tensor<2x2xf32>,
                %rhs: tensor<2x2xf32>) -> tensor<2x2xf32> {
  // CHECK: linalg.generic
  // CHECK: ^{{[a-z0-9_]*}}
  // CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]*]]: f32
  // CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]*]]: f32
  // CHECK: %[[RESULT:[a-zA-Z0-9_]*]] = math.powf %[[ARG0]], %[[ARG1]]
  // CHECK: linalg.yield %[[RESULT]]
  %0 = "mhlo.power"(%lhs, %rhs) : (tensor<2x2xf32>,
                                   tensor<2x2xf32>) -> tensor<2x2xf32>
  return %0 : tensor<2x2xf32>
}

// -----

// CHECK: #map = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-LABEL: func @integer_pow
func @integer_pow(%lhs: tensor<2x2xi32>,
                  %rhs: tensor<2x2xi32>) -> tensor<2x2xi32> {
                    // CHECK: linalg.generic
  // CHECK: ^{{[a-z0-9_]*}}
  // CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]*]]: i32
  // CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]*]]: i32
  // CHECK: %[[FOR_RESULT:[a-zA-Z0-9_]*]]:3 = scf.for {{.*}} to %c6 step %c1
  // CHECK-SAME: iter_args(
  // CHECK-SAME:   %[[ITER0:.*]] = %c1
  // CHECK-SAME:   %[[ITER1:.*]] = %[[ARG0]]
  // CHECK-SAME:   %[[ITER2:.*]] = %[[ARG1]]
  // CHECK-SAME: ) -> (i32, i32, i32) {
  //   CHECK: %[[AND:[a-zA-Z0-9_]*]] = and %[[ITER2]], %c1
  //   CHECK: %[[COND:[a-zA-Z0-9_]*]] = cmpi eq, %[[AND]], %c1
  //   CHECK: %[[MUL:[a-zA-Z0-9_]*]] = muli %[[ITER0]], %[[ITER1]]
  //   CHECK: %[[ACCUM:[a-zA-Z0-9_]*]] = select %[[COND]], %[[MUL]], %[[ITER0]]
  //   CHECK: %[[BASE:[a-zA-Z0-9_]*]] = muli %[[ITER1]], %[[ITER1]]
  //   CHECK: %[[EXP:[a-zA-Z0-9_]*]] = shift_right_unsigned %[[ITER2]], %c1
  //   CHECK: scf.yield %[[ACCUM]], %[[BASE]], %[[EXP]]
  // CHECK: %[[RHS_PARITY:.*]] = remi_signed %[[ARG1]], %c2
  // CHECK: %[[RHS_EVEN:.*]] = cmpi eq, %[[RHS_PARITY]], %c0
  // CHECK: %[[RHS_NEG:.*]] = cmpi slt, %[[ARG1]], %c0
  // CHECK: %[[LHS_ONE:.*]] = cmpi eq, %[[ARG0]], %c1
  // CHECK: %[[LHS_NEG_ONE:.*]] = cmpi eq, %[[ARG0]], %c-1
  // CHECK: %[[VAL5:.*]] = select %[[LHS_ONE]], %c1_i32, %c0
  // CHECK: %[[VAL6:.*]] = select %[[RHS_EVEN]], %c1{{.*}}, %c-1
  // CHECK: %[[VAL7:.*]] = select %[[LHS_NEG_ONE]], %[[VAL6]], %[[VAL5]]
  // CHECK: %[[RESULT:.*]] = select %[[RHS_NEG]], %[[VAL7]], %[[FOR_RESULT]]#0
  // CHECK: linalg.yield %[[RESULT]]
  %0 = "mhlo.power"(%lhs, %rhs) : (tensor<2x2xi32>,
                                   tensor<2x2xi32>) -> tensor<2x2xi32>
  return %0 : tensor<2x2xi32>
}

// -----

// CHECK-DAG: #[[OPERAND_MAP:.*]] = affine_map<(d0) -> ()>
// CHECK-DAG: #[[RESULT_MAP:.*]] = affine_map<(d0) -> (d0)>

// CHECK-LABEL: func @dynamic_broadcast_in_dim(
// CHECK-SAME: [[SHAPE:%.*]]: tensor<1xindex>
func @dynamic_broadcast_in_dim(%shape: tensor<1xindex>) -> tensor<?xf32> {
  %cst = mhlo.constant dense<0x7F800000> : tensor<f32>
  %result = "mhlo.dynamic_broadcast_in_dim"(%cst, %shape) {
     broadcast_dimensions = dense<> : tensor<0xi64>
  } : (tensor<f32>, tensor<1xindex>) -> tensor<?xf32>
  return %result : tensor<?xf32>
}
// CHECK: [[CST:%.*]] = constant
// CHECK: [[INIT:%.*]] = linalg.init_tensor
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[OPERAND_MAP]], #[[RESULT_MAP]]]
// CHECK-SAME: ins([[CST]] : tensor<f32>) outs([[INIT]] : tensor<?xf32>)
// CHECK-NEXT: ^bb0(%[[OPERAND:.*]]: f32, %[[RESULT:.*]]: f32):
// CHECK-NEXT:   linalg.yield %[[OPERAND]] : f32

// -----

// CHECK-DAG: #[[OPERAND_MAP:.*]] = affine_map<(d0, d1) -> ()>
// CHECK-DAG: #[[RESULT_MAP:.*]] = affine_map<(d0, d1) -> (d0, d1)>

// CHECK-LABEL: func @dynamic_broadcast_in_dim(
// CHECK-SAME: [[SCALAR:%.*]]: tensor<f32>
// CHECK-SAME: [[SHAPE:%.*]]: tensor<2xindex>
func @dynamic_broadcast_in_dim(%scalar: tensor<f32>, %shape: tensor<2xindex>)
    -> tensor<?x32xf32> {
  %result = "mhlo.dynamic_broadcast_in_dim"(%scalar, %shape) {
     broadcast_dimensions = dense<> : tensor<0xi64>
  } : (tensor<f32>, tensor<2xindex>) -> tensor<?x32xf32>
  return %result : tensor<?x32xf32>
}
// CHECK: [[INIT:%.*]] = linalg.init_tensor
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[OPERAND_MAP]], #[[RESULT_MAP]]]
// CHECK-SAME: ins([[SCALAR]] : tensor<f32>) outs([[INIT]] : tensor<?x32xf32>)
// CHECK-NEXT: ^bb0(%[[OPERAND:.*]]: f32, %[[RESULT:.*]]: f32):
// CHECK-NEXT:   linalg.yield %[[OPERAND]] : f32

// -----

// CHECK-DAG: #[[OPERAND_MAP:.*]] = affine_map<(d0, d1, d2) -> (d1)>
// CHECK-DAG: #[[RESULT_MAP:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>

// CHECK-LABEL: func @dynamic_broadcast_in_dim(
// CHECK-SAME: [[VECTOR:%.*]]: tensor<42xf32>
// CHECK-SAME: [[SHAPE:%.*]]: tensor<3xindex>
func @dynamic_broadcast_in_dim(%vector: tensor<42xf32>, %shape: tensor<3xindex>)
    -> tensor<?x?x?xf32> {
  %result = "mhlo.dynamic_broadcast_in_dim"(%vector, %shape) {
     broadcast_dimensions = dense<1> : tensor<1xi64>
  } : (tensor<42xf32>, tensor<3xindex>) -> tensor<?x?x?xf32>
  return %result : tensor<?x?x?xf32>
}
// CHECK: [[INIT:%.*]] = linalg.init_tensor
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[OPERAND_MAP]], #[[RESULT_MAP]]]
// CHECK-SAME: ins([[VECTOR]] : tensor<42xf32>) outs([[INIT]] : tensor<?x?x?xf32>)
// CHECK-NEXT: ^bb0(%[[OPERAND:.*]]: f32, %[[RESULT:.*]]: f32):
// CHECK-NEXT:   linalg.yield %[[OPERAND]] : f32

// -----

func @dot_matmul(%arg0: tensor<2x3xf32>,
                 %arg1: tensor<3x?xf32>) -> tensor<2x?xf32> {
  %0 = "mhlo.dot"(%arg0, %arg1) : (tensor<2x3xf32>,
                                   tensor<3x?xf32>) -> tensor<2x?xf32>
  return %0 : tensor<2x?xf32>
}
// CHECK-LABEL: func @dot_matmul(
// CHECK-SAME: %[[ARG0:.*]]: tensor<2x3xf32>, %[[ARG1:.*]]: tensor<3x?xf32>)
// CHECK: %[[C1:.*]] = constant 1 : index
// CHECK: %[[D1:.*]] = memref.dim %[[ARG1]], %[[C1]]
// CHECK: %[[INIT:.*]] = linalg.init_tensor [2, %[[D1]]]
// CHECK: %[[FILL:.*]] = linalg.fill(%[[INIT]]
// CHECK: linalg.matmul
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] : tensor<2x3xf32>, tensor<3x?xf32>)
// CHECK-SAME: outs(%[[FILL]] : tensor<2x?xf32>)

func @dot_matmul_i8_i8_i32(%arg0: tensor<2x3xi8>,
                 %arg1: tensor<3x?xi8>) -> tensor<2x?xi32> {
  %0 = "mhlo.dot"(%arg0, %arg1) : (tensor<2x3xi8>,
                                   tensor<3x?xi8>) -> tensor<2x?xi32>
  return %0 : tensor<2x?xi32>
}
// CHECK-LABEL: func @dot_matmul_i8_i8_i32(
// CHECK-SAME: %[[ARG0:.*]]: tensor<2x3xi8>, %[[ARG1:.*]]: tensor<3x?xi8>)
// CHECK: %[[C1:.*]] = constant 1 : index
// CHECK: %[[D1:.*]] = memref.dim %[[ARG1]], %[[C1]]
// CHECK: %[[INIT:.*]] = linalg.init_tensor [2, %[[D1]]]
// CHECK: %[[FILL:.*]] = linalg.fill(%[[INIT]]
// CHECK: linalg.matmul
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] : tensor<2x3xi8>, tensor<3x?xi8>)
// CHECK-SAME: outs(%[[FILL]] : tensor<2x?xi32>)

// -----

func @dot_matmul_i16_i16_i32(%arg0: tensor<2x3xi16>,
                 %arg1: tensor<3x?xi16>) -> tensor<2x?xi32> {
  %0 = "mhlo.dot"(%arg0, %arg1) : (tensor<2x3xi16>,
                                   tensor<3x?xi16>) -> tensor<2x?xi32>
  return %0 : tensor<2x?xi32>
}
// CHECK-LABEL: func @dot_matmul_i16_i16_i32(
// CHECK-SAME: %[[ARG0:.*]]: tensor<2x3xi16>, %[[ARG1:.*]]: tensor<3x?xi16>)
// CHECK: %[[C1:.*]] = constant 1 : index
// CHECK: %[[D1:.*]] = memref.dim %[[ARG1]], %[[C1]]
// CHECK: %[[INIT:.*]] = linalg.init_tensor [2, %[[D1]]]
// CHECK: %[[FILL:.*]] = linalg.fill(%[[INIT]]
// CHECK: linalg.matmul
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] : tensor<2x3xi16>, tensor<3x?xi16>)
// CHECK-SAME: outs(%[[FILL]] : tensor<2x?xi32>)

// -----

func @dot_matmul_i32_i32_i32(%arg0: tensor<2x3xi32>,
                 %arg1: tensor<3x?xi32>) -> tensor<2x?xi32> {
  %0 = "mhlo.dot"(%arg0, %arg1) : (tensor<2x3xi32>,
                                   tensor<3x?xi32>) -> tensor<2x?xi32>
  return %0 : tensor<2x?xi32>
}
// CHECK-LABEL: func @dot_matmul_i32_i32_i32(
// CHECK-SAME: %[[ARG0:.*]]: tensor<2x3xi32>, %[[ARG1:.*]]: tensor<3x?xi32>)
// CHECK: %[[C1:.*]] = constant 1 : index
// CHECK: %[[D1:.*]] = memref.dim %[[ARG1]], %[[C1]]
// CHECK: %[[INIT:.*]] = linalg.init_tensor [2, %[[D1]]]
// CHECK: %[[FILL:.*]] = linalg.fill(%[[INIT]]
// CHECK: linalg.matmul
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] : tensor<2x3xi32>, tensor<3x?xi32>)
// CHECK-SAME: outs(%[[FILL]] : tensor<2x?xi32>)

// -----

func @dot_matvec(%arg0: tensor<?x3xf32>,
                 %arg1: tensor<3xf32>) -> tensor<?xf32> {
  %0 = "mhlo.dot"(%arg0, %arg1) : (tensor<?x3xf32>,
                                   tensor<3xf32>) -> tensor<?xf32>
  return %0 : tensor<?xf32>
}
// CHECK-LABEL: func @dot_matvec(
// CHECK-SAME: %[[ARG0:.*]]: tensor<?x3xf32>, %[[ARG1:.*]]: tensor<3xf32>)
// CHECK: %[[C0:.*]] = constant 0 : index
// CHECK: %[[D0:.*]] = memref.dim %[[ARG0]], %[[C0]]
// CHECK: %[[INIT:.*]] = linalg.init_tensor [%[[D0]]]
// CHECK: %[[FILL:.*]] = linalg.fill(%[[INIT]]
// CHECK: linalg.matvec
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] : tensor<?x3xf32>, tensor<3xf32>)
// CHECK-SAME: outs(%[[FILL]] : tensor<?xf32>)

// -----

func @dot_dot(%arg0: tensor<?xf32>,
              %arg1: tensor<?xf32>) -> tensor<f32> {
  %0 = "mhlo.dot"(%arg0, %arg1) : (tensor<?xf32>, tensor<?xf32>) -> tensor<f32>
  return %0 : tensor<f32>
}
// CHECK-LABEL: func @dot_dot(
// CHECK-SAME: %[[ARG0:.*]]: tensor<?xf32>, %[[ARG1:.*]]: tensor<?xf32>)
// CHECK: %[[INIT:.*]] = linalg.init_tensor []
// CHECK: %[[FILL:.*]] = linalg.fill(%[[INIT]]
// CHECK: linalg.dot
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] : tensor<?xf32>, tensor<?xf32>)
// CHECK-SAME: outs(%[[FILL]] : tensor<f32>)

// -----

func @dot_general_batch_matmul(%arg0: tensor<?x?x3xf32>,
                  %arg1: tensor<?x3x?xf32>) -> tensor<?x?x?xf32> {
  %0 = "mhlo.dot_general"(%arg0, %arg1) {
      dot_dimension_numbers = {
          lhs_batching_dimensions = dense<0> : tensor<1xi64>,
          lhs_contracting_dimensions = dense<2> : tensor<1xi64>,
          rhs_batching_dimensions = dense<0> : tensor<1xi64>,
          rhs_contracting_dimensions = dense<1> : tensor<1xi64>
      },
      precision_config = ["DEFAULT", "DEFAULT"]
  } : (tensor<?x?x3xf32>, tensor<?x3x?xf32>) -> tensor<?x?x?xf32>
  return %0 : tensor<?x?x?xf32>
}
// CHECK-LABEL: func @dot_general_batch_matmul(
// CHECK-SAME: %[[ARG0:.*]]: tensor<?x?x3xf32>, %[[ARG1:.*]]: tensor<?x3x?xf32>)
// CHECK: %[[C0:.*]] = constant 0 : index
// CHECK: %[[D0:.*]] = memref.dim %[[ARG0]], %[[C0]]
// CHECK: %[[C1:.*]] = constant 1 : index
// CHECK: %[[D1:.*]] = memref.dim %[[ARG0]], %[[C1]]
// CHECK: %[[C2:.*]] = constant 2 : index
// CHECK: %[[D2:.*]] = memref.dim %[[ARG1]], %[[C2]]
// CHECK: %[[INIT:.*]] = linalg.init_tensor [%[[D0]], %[[D1]], %[[D2]]]
// CHECK: %[[FILL:.*]] = linalg.fill(%[[INIT]]
// CHECK: linalg.batch_matmul
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] : tensor<?x?x3xf32>, tensor<?x3x?xf32>)
// CHECK-SAME: outs(%[[FILL]] : tensor<?x?x?xf32>)

// -----

func @dot_general_batch_matmul_i8_i8_i32(%arg0: tensor<?x?x3xi8>,
                  %arg1: tensor<?x3x?xi8>) -> tensor<?x?x?xi32> {
  %0 = "mhlo.dot_general"(%arg0, %arg1) {
      dot_dimension_numbers = {
          lhs_batching_dimensions = dense<0> : tensor<1xi64>,
          lhs_contracting_dimensions = dense<2> : tensor<1xi64>,
          rhs_batching_dimensions = dense<0> : tensor<1xi64>,
          rhs_contracting_dimensions = dense<1> : tensor<1xi64>
      },
      precision_config = ["DEFAULT", "DEFAULT"]
  } : (tensor<?x?x3xi8>, tensor<?x3x?xi8>) -> tensor<?x?x?xi32>
  return %0 : tensor<?x?x?xi32>
}
// CHECK-LABEL: func @dot_general_batch_matmul_i8_i8_i32(
// CHECK-SAME: %[[ARG0:.*]]: tensor<?x?x3xi8>, %[[ARG1:.*]]: tensor<?x3x?xi8>)
// CHECK: %[[C0:.*]] = constant 0 : index
// CHECK: %[[D0:.*]] = memref.dim %[[ARG0]], %[[C0]]
// CHECK: %[[C1:.*]] = constant 1 : index
// CHECK: %[[D1:.*]] = memref.dim %[[ARG0]], %[[C1]]
// CHECK: %[[C2:.*]] = constant 2 : index
// CHECK: %[[D2:.*]] = memref.dim %[[ARG1]], %[[C2]]
// CHECK: %[[INIT:.*]] = linalg.init_tensor [%[[D0]], %[[D1]], %[[D2]]]
// CHECK: %[[FILL:.*]] = linalg.fill(%[[INIT]]
// CHECK: linalg.batch_matmul
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] : tensor<?x?x3xi8>, tensor<?x3x?xi8>)
// CHECK-SAME: outs(%[[FILL]] : tensor<?x?x?xi32>)

// -----

func @dot_general_batch_matmul_i16_i16_i32(%arg0: tensor<?x?x3xi16>,
                  %arg1: tensor<?x3x?xi16>) -> tensor<?x?x?xi32> {
  %0 = "mhlo.dot_general"(%arg0, %arg1) {
      dot_dimension_numbers = {
          lhs_batching_dimensions = dense<0> : tensor<1xi64>,
          lhs_contracting_dimensions = dense<2> : tensor<1xi64>,
          rhs_batching_dimensions = dense<0> : tensor<1xi64>,
          rhs_contracting_dimensions = dense<1> : tensor<1xi64>
      },
      precision_config = ["DEFAULT", "DEFAULT"]
  } : (tensor<?x?x3xi16>, tensor<?x3x?xi16>) -> tensor<?x?x?xi32>
  return %0 : tensor<?x?x?xi32>
}
// CHECK-LABEL: func @dot_general_batch_matmul_i16_i16_i32(
// CHECK-SAME: %[[ARG0:.*]]: tensor<?x?x3xi16>, %[[ARG1:.*]]: tensor<?x3x?xi16>)
// CHECK: %[[C0:.*]] = constant 0 : index
// CHECK: %[[D0:.*]] = memref.dim %[[ARG0]], %[[C0]]
// CHECK: %[[C1:.*]] = constant 1 : index
// CHECK: %[[D1:.*]] = memref.dim %[[ARG0]], %[[C1]]
// CHECK: %[[C2:.*]] = constant 2 : index
// CHECK: %[[D2:.*]] = memref.dim %[[ARG1]], %[[C2]]
// CHECK: %[[INIT:.*]] = linalg.init_tensor [%[[D0]], %[[D1]], %[[D2]]]
// CHECK: %[[FILL:.*]] = linalg.fill(%[[INIT]]
// CHECK: linalg.batch_matmul
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] : tensor<?x?x3xi16>, tensor<?x3x?xi16>)
// CHECK-SAME: outs(%[[FILL]] : tensor<?x?x?xi32>)

// -----

func @dot_general_batch_matmul_large
  (%arg0: tensor<2x16x32xf32>, %arg1: tensor<2x32x32xf32>) -> tensor<2x16x32xf32> {
  %0 = "mhlo.dot_general"(%arg0, %arg1) {
    dot_dimension_numbers = {
      lhs_batching_dimensions = dense<0> : tensor<1xi64>,
      lhs_contracting_dimensions = dense<2> : tensor<1xi64>,
      rhs_batching_dimensions = dense<0> : tensor<1xi64>,
      rhs_contracting_dimensions = dense<1> : tensor<1xi64>},
    precision_config = ["DEFAULT", "DEFAULT"]}
    : (tensor<2x16x32xf32>, tensor<2x32x32xf32>) -> tensor<2x16x32xf32>
  return %0 : tensor<2x16x32xf32>
}
// CHECK-LABEL: func @dot_general_batch_matmul_large(
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]*]]: tensor<2x16x32xf32>,
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]*]]: tensor<2x32x32xf32>)
// CHECK: %[[INIT:.*]] = linalg.init_tensor [2, 16, 32]
// CHECK: %[[FILL:.*]] = linalg.fill(%[[INIT]]
// CHECK: %[[DOT:.*]] = linalg.batch_matmul
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] : tensor<2x16x32xf32>, tensor<2x32x32xf32>)
// CHECK-SAME: outs(%[[FILL]] : tensor<2x16x32xf32>)

// -----

// CHECK-LABEL: @clamp
// CHECK-SAME: %[[LB:.*]]: tensor<4xf32>, %[[X:.*]]: tensor<4xf32>, %[[UB:.*]]: tensor<4xf32>
func @clamp(%lb : tensor<4xf32>, %x : tensor<4xf32>, %ub : tensor<4xf32>)
    -> tensor<4xf32> {
  // CHECK: %[[INIT:.*]] = linalg.init_tensor
  // CHECK: %[[RESULT:.*]] = linalg.generic {{.*}} ins(%[[LB]], %[[X]], %[[UB]] : tensor<4xf32>, tensor<4xf32>, tensor<4xf32>) outs(%[[INIT]] : tensor<4xf32>)
  // CHECK: ^bb0(%[[SCALAR_LB:.*]]: f32, %[[SCALAR_X:.*]]: f32, %[[SCALAR_UB:.*]]: f32, %{{.*}}: f32):
  // CHECK:   cmpf olt
  // CHECK:   select
  // CHECK:   cmpf uno
  // CHECK:   select
  // CHECK:   cmpf ogt
  // CHECK:   select
  // CHECK:   cmpf uno
  // CHECK:   %[[MAX_X2_LB:.*]] = select
  // CHECK:   linalg.yield %[[MAX_X2_LB]]
  // CHECK: } -> tensor<4xf32>
  // CHECK: return %[[RESULT]] : tensor<4xf32>
  %0 = "mhlo.clamp"(%lb, %x, %ub) : (tensor<4xf32>, tensor<4xf32>,
      tensor<4xf32>) -> tensor<4xf32>
  return %0 : tensor<4xf32>
}

// -----

func @reduce_add(%arg0: tensor<5x4xi32>, %arg1: tensor<i32>) -> tensor<5xi32> {
  %0 = "mhlo.reduce"(%arg0, %arg1) ({
  ^bb0(%arg3: tensor<i32>, %arg4 : tensor<i32>):
    %1 = mhlo.add %arg3, %arg4 : tensor<i32>
    "mhlo.return"(%1) : (tensor<i32>) -> ()
  }) {dimensions = dense<1> : tensor<1xi64>} : (tensor<5x4xi32>, tensor<i32>) -> tensor<5xi32>
  return %0 : tensor<5xi32>
}
// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1) -> (d0)>
// CHECK-LABEL: @reduce_add
// CHECK-DAG: %[[INIT:.*]] = tensor.extract %{{.*}} : tensor<i32>
// CHECK-DAG: %[[INIT_TENSOR:.*]] = linalg.init_tensor [5]
// CHECK-DAG: %[[FILL_TENSOR:.*]] = linalg.fill(%[[INIT_TENSOR]], %[[INIT]])
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]]]
// CHECK-SAME: iterator_types = ["parallel", "reduction"]
// CHECK-SAME: ins(%{{.*}}tensor<5x4xi32>)
// CHECK-SAME: outs(%[[FILL_TENSOR]] : tensor<5xi32>)
// CHECK-NEXT: ^bb0(%[[LHS_IN:.*]]: i32, %[[RHS_IN:.*]]: i32):
// CHECK-NEXT:   %[[RESULT:.*]] = addi %[[LHS_IN]], %[[RHS_IN]] : i32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i32

// -----

func @reduce_minimum(%arg0: tensor<5x4xi32>, %arg1: tensor<i32>) -> tensor<5xi32> {
  %0 = "mhlo.reduce"(%arg0, %arg1) ({
  ^bb0(%arg3: tensor<i32>, %arg4 : tensor<i32>):
    %1 = mhlo.minimum %arg3, %arg4 : tensor<i32>
    "mhlo.return"(%1) : (tensor<i32>) -> ()
  }) {dimensions = dense<1> : tensor<1xi64>} : (tensor<5x4xi32>, tensor<i32>) -> tensor<5xi32>
  return %0 : tensor<5xi32>
}
// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1) -> (d0)>
// CHECK-LABEL: @reduce_minimum
// CHECK-DAG: %[[INIT:.*]] = tensor.extract %{{.*}} : tensor<i32>
// CHECK-DAG: %[[INIT_TENSOR:.*]] = linalg.init_tensor [5]
// CHECK-DAG: %[[FILL_TENSOR:.*]] = linalg.fill(%[[INIT_TENSOR]], %[[INIT]])
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]]]
// CHECK-SAME: iterator_types = ["parallel", "reduction"]
// CHECK-SAME: ins(%{{.*}}tensor<5x4xi32>)
// CHECK-SAME: outs(%[[FILL_TENSOR]] : tensor<5xi32>)
// CHECK-NEXT: ^bb0(%[[LHS_IN:.*]]: i32, %[[RHS_IN:.*]]: i32):
// CHECK-NEXT:   %[[CMP:.*]] = cmpi slt, %[[LHS_IN]], %[[RHS_IN]] : i32
// CHECK-NEXT:   %[[RESULT:.*]] = select %[[CMP]], %[[LHS_IN]], %[[RHS_IN]] : i32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i32

// -----

func @reduce_maximum(%arg0: tensor<5x4xi32>, %arg1: tensor<i32>) -> tensor<5xi32> {
  %0 = "mhlo.reduce"(%arg0, %arg1) ({
  ^bb0(%arg3: tensor<i32>, %arg4 : tensor<i32>):
    %1 = mhlo.maximum %arg3, %arg4 : tensor<i32>
    "mhlo.return"(%1) : (tensor<i32>) -> ()
  }) {dimensions = dense<1> : tensor<1xi64>} : (tensor<5x4xi32>, tensor<i32>) -> tensor<5xi32>
  return %0 : tensor<5xi32>
}
// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1) -> (d0)>
// CHECK-LABEL: @reduce_maximum
// CHECK-DAG: %[[INIT:.*]] = tensor.extract %{{.*}} : tensor<i32>
// CHECK-DAG: %[[INIT_TENSOR:.*]] = linalg.init_tensor [5]
// CHECK-DAG: %[[FILL_TENSOR:.*]] = linalg.fill(%[[INIT_TENSOR]], %[[INIT]])
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]]]
// CHECK-SAME: iterator_types = ["parallel", "reduction"]
// CHECK-SAME: ins(%{{.*}}tensor<5x4xi32>)
// CHECK-SAME: outs(%[[FILL_TENSOR]] : tensor<5xi32>)
// CHECK-NEXT: ^bb0(%[[LHS_IN:.*]]: i32, %[[RHS_IN:.*]]: i32):
// CHECK-NEXT:   %[[CMP:.*]] = cmpi sgt, %[[LHS_IN]], %[[RHS_IN]] : i32
// CHECK-NEXT:   %[[RESULT:.*]] = select %[[CMP]], %[[LHS_IN]], %[[RHS_IN]] : i32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i32

// -----

func @reduce_and(%arg0: tensor<5x4xi1>, %arg1: tensor<i1>) -> tensor<5xi1> {
  %0 = "mhlo.reduce"(%arg0, %arg1) ({
  ^bb0(%arg3: tensor<i1>, %arg4 : tensor<i1>):
    %1 = mhlo.and %arg3, %arg4 : tensor<i1>
    "mhlo.return"(%1) : (tensor<i1>) -> ()
  }) {dimensions = dense<1> : tensor<1xi64>} : (tensor<5x4xi1>, tensor<i1>) -> tensor<5xi1>
  return %0 : tensor<5xi1>
}
// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1) -> (d0)>
// CHECK-LABEL: @reduce_and
// CHECK-DAG: %[[INIT:.*]] = tensor.extract %{{.*}} : tensor<i1>
// CHECK-DAG: %[[INIT_TENSOR:.*]] = linalg.init_tensor [5]
// CHECK-DAG: %[[FILL_TENSOR:.*]] = linalg.fill(%[[INIT_TENSOR]], %[[INIT]])
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]]]
// CHECK-SAME: iterator_types = ["parallel", "reduction"]
// CHECK-SAME: ins(%{{.*}}tensor<5x4xi1>)
// CHECK-SAME: outs(%[[FILL_TENSOR]] : tensor<5xi1>)
// CHECK-NEXT: ^bb0(%[[LHS_IN:.*]]: i1, %[[RHS_IN:.*]]: i1):
// CHECK-NEXT:   %[[RESULT:.*]] = and %[[LHS_IN]], %[[RHS_IN]] : i1
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i1

// -----

func @reduce_or(%arg0: tensor<5x4xi1>, %arg1: tensor<i1>) -> tensor<5xi1> {
  %0 = "mhlo.reduce"(%arg0, %arg1) ({
  ^bb0(%arg3: tensor<i1>, %arg4 : tensor<i1>):
    %1 = mhlo.or %arg3, %arg4 : tensor<i1>
    "mhlo.return"(%1) : (tensor<i1>) -> ()
  }) {dimensions = dense<1> : tensor<1xi64>} : (tensor<5x4xi1>, tensor<i1>) -> tensor<5xi1>
  return %0 : tensor<5xi1>
}
// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1) -> (d0)>
// CHECK-LABEL: @reduce_or
// CHECK-DAG: %[[INIT:.*]] = tensor.extract %{{.*}} : tensor<i1>
// CHECK-DAG: %[[INIT_TENSOR:.*]] = linalg.init_tensor [5]
// CHECK-DAG: %[[FILL_TENSOR:.*]] = linalg.fill(%[[INIT_TENSOR]], %[[INIT]])
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]]]
// CHECK-SAME: iterator_types = ["parallel", "reduction"]
// CHECK-SAME: ins(%{{.*}}tensor<5x4xi1>)
// CHECK-SAME: outs(%[[FILL_TENSOR]] : tensor<5xi1>)
// CHECK-NEXT: ^bb0(%[[LHS_IN:.*]]: i1, %[[RHS_IN:.*]]: i1):
// CHECK-NEXT:   %[[RESULT:.*]] = or %[[LHS_IN]], %[[RHS_IN]] : i1
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i1

// -----

func @reduce_dim0(%arg0: tensor<5x4xi32>, %arg1: tensor<i32>) -> tensor<4xi32> {
  %0 = "mhlo.reduce"(%arg0, %arg1) ({
  ^bb0(%arg3: tensor<i32>, %arg4 : tensor<i32>):
    %1 = mhlo.maximum %arg3, %arg4 : tensor<i32>
    "mhlo.return"(%1) : (tensor<i32>) -> ()
  }) {dimensions = dense<0> : tensor<1xi64>} : (tensor<5x4xi32>, tensor<i32>) -> tensor<4xi32>
  return %0 : tensor<4xi32>
}
// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1) -> (d1, d0)>
// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1) -> (d0)>
// CHECK-LABEL: @reduce_dim0
// CHECK-DAG: %[[INIT:.*]] = tensor.extract %{{.*}} : tensor<i32>
// CHECK-DAG: %[[INIT_TENSOR:.*]] = linalg.init_tensor [4]
// CHECK-DAG: %[[FILL_TENSOR:.*]] = linalg.fill(%[[INIT_TENSOR]], %[[INIT]])
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]]]
// CHECK-SAME: iterator_types = ["parallel", "reduction"]
// CHECK-SAME: ins(%{{.*}}tensor<5x4xi32>)
// CHECK-SAME: outs(%[[FILL_TENSOR]] : tensor<4xi32>)
// CHECK-NEXT: ^bb0(%[[LHS_IN:.*]]: i32, %[[RHS_IN:.*]]: i32):
// CHECK-NEXT:   %[[CMP:.*]] = cmpi sgt, %[[LHS_IN]], %[[RHS_IN]] : i32
// CHECK-NEXT:   %[[RESULT:.*]] = select %[[CMP]], %[[LHS_IN]], %[[RHS_IN]] : i32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i32

// -----

func @reduce_init_const(%arg0: tensor<1x10xf32>) -> tensor<1xf32> {
  %cst = constant dense<0xFF800000> : tensor<f32>
  %0 = "mhlo.reduce"(%arg0, %cst) ({
  ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>): // no predecessors
    %1 = mhlo.add %arg1, %arg2 : tensor<f32>
    "mhlo.return"(%1) : (tensor<f32>) -> ()
  }) {dimensions = dense<1> : tensor<1xi64>} : (tensor<1x10xf32>, tensor<f32>) -> tensor<1xf32>
  return %0 : tensor<1xf32>
}
// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1) -> (d0)>
// CHECK-LABEL: @reduce_init_const
// CHECK-DAG: %[[INIT_TENSOR:.*]] = linalg.init_tensor [1]
// CHECK-DAG: %[[FILL_TENSOR:.*]] = linalg.fill(%[[INIT_TENSOR]], %{{.*}})
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]]]
// CHECK-SAME: iterator_types = ["parallel", "reduction"]
// CHECK-SAME: ins(%{{.*}}tensor<1x10xf32>)
// CHECK-SAME: outs(%[[FILL_TENSOR]] : tensor<1xf32>)
// CHECK-NEXT: ^bb0(%[[LHS_IN:.*]]: f32, %[[RHS_IN:.*]]: f32):
// CHECK-NEXT:   %[[RESULT:.*]] = addf %[[LHS_IN]], %[[RHS_IN]] : f32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : f32

// -----

func @reduce_multi_dimensions(%arg0: tensor<5x4x3xi32>,
                              %arg1: tensor<i32>) -> tensor<4xi32> {
  %0 = "mhlo.reduce"(%arg0, %arg1) ({
  ^bb0(%arg2: tensor<i32>, %arg3: tensor<i32>):
    %1 = mhlo.add %arg2, %arg3 : tensor<i32>
    "mhlo.return"(%1) : (tensor<i32>) -> ()
  }) {dimensions = dense<[0, 2]> : tensor<2xi64>} : (tensor<5x4x3xi32>, tensor<i32>) -> tensor<4xi32>
  return %0 : tensor<4xi32>
}
// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1, d2) -> (d1, d0, d2)>
// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1, d2) -> (d0)>
// CHECK-LABEL: @reduce_multi_dimensions
// CHECK-DAG: %[[INIT:.*]] = tensor.extract %{{.*}} : tensor<i32>
// CHECK-DAG: %[[INIT_TENSOR:.*]] = linalg.init_tensor [4]
// CHECK-DAG: %[[FILL_TENSOR:.*]] = linalg.fill(%[[INIT_TENSOR]], %[[INIT]])
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]]]
// CHECK-SAME: iterator_types = ["parallel", "reduction", "reduction"]
// CHECK-SAME: ins(%{{.*}}tensor<5x4x3xi32>)
// CHECK-SAME: outs(%[[FILL_TENSOR]] : tensor<4xi32>)
// CHECK-NEXT: ^bb0(%[[LHS_IN:.*]]: i32, %[[RHS_IN:.*]]: i32):
// CHECK-NEXT:   %[[RESULT:.*]] = addi %[[LHS_IN]], %[[RHS_IN]] : i32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i32

// -----

func @reduce_dynamic(%arg0: tensor<?x?xi32>, %arg1: tensor<i32>) -> tensor<?xi32> {
  %0 = "mhlo.reduce"(%arg0, %arg1) ({
  ^bb0(%arg3: tensor<i32>, %arg4 : tensor<i32>):
    %1 = mhlo.add %arg3, %arg4 : tensor<i32>
    "mhlo.return"(%1) : (tensor<i32>) -> ()
  }) {dimensions = dense<1> : tensor<1xi64>} : (tensor<?x?xi32>, tensor<i32>) -> tensor<?xi32>
  return %0 : tensor<?xi32>
}
// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1) -> (d0)>
// CHECK: func @reduce_dynamic(%[[ARG0:.*]]: tensor<?x?xi32>
// CHECK-DAG: %[[INIT:.*]] = tensor.extract %{{.*}} : tensor<i32>
// CHECK-DAG: %[[C0:.*]] = constant 0 : index
// CHECK-DAG: %[[DIM1:.*]] = memref.dim %[[ARG0]], %[[C0]] : tensor<?x?xi32>
// CHECK-DAG: %[[INIT_TENSOR:.*]] = linalg.init_tensor [%[[DIM1]]]
// CHECK-DAG: %[[FILL_TENSOR:.*]] = linalg.fill(%[[INIT_TENSOR]], %[[INIT]])
// CHECK: linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]]]
// CHECK-SAME: iterator_types = ["parallel", "reduction"]
// CHECK-SAME: ins(%{{.*}}tensor<?x?xi32>)
// CHECK-SAME: outs(%[[FILL_TENSOR]] : tensor<?xi32>)
// CHECK-NEXT: ^bb0(%[[LHS_IN:.*]]: i32, %[[RHS_IN:.*]]: i32):
// CHECK-NEXT:   %[[RESULT:.*]] = addi %[[LHS_IN]], %[[RHS_IN]] : i32
// CHECK-NEXT:   linalg.yield %[[RESULT]] : i32

// -----

func @slice_whole_stride(%arg0: tensor<3x4xi32>) -> tensor<1x4xi32> {
  %0 = "mhlo.slice"(%arg0) {
    start_indices = dense<[1, 0]> : tensor<2xi64>,
    limit_indices = dense<[2, 4]> : tensor<2xi64>,
    strides = dense<1> : tensor<2xi64>
  } : (tensor<3x4xi32>) -> tensor<1x4xi32>
  return %0 : tensor<1x4xi32>
}
// CHECK-LABEL: func @slice_whole_stride
//       CHECK:   subtensor %{{.*}}[1, 0] [1, 4] [1, 1] : tensor<3x4xi32> to tensor<1x4xi32>

// -----

func @slice_stride_part(%arg0: tensor<3x4xi32>) -> tensor<1x2xi32> {
  %0 = "mhlo.slice"(%arg0) {
    start_indices = dense<[1, 1]> : tensor<2xi64>,
    limit_indices = dense<[2, 3]> : tensor<2xi64>,
    strides = dense<1> : tensor<2xi64>
  } : (tensor<3x4xi32>) -> tensor<1x2xi32>
  return %0 : tensor<1x2xi32>
}
// CHECK-LABEL: func @slice_stride_part
//       CHECK:   subtensor %{{.*}}[1, 1] [1, 2] [1, 1]  : tensor<3x4xi32> to tensor<1x2xi32>

// -----

func @dynamic_slice(%arg: tensor<3x4xf32>, %start1: tensor<i64>, %start2: tensor<i64>) -> tensor<1x4xf32> {
  %0 = "mhlo.dynamic-slice"(%arg, %start1, %start2) {
    slice_sizes = dense<[1, 4]> : tensor<2xi64>
  } : (tensor<3x4xf32>, tensor<i64>, tensor<i64>) -> tensor<1x4xf32>
  return %0 : tensor<1x4xf32>
}
// CHECK-LABEL: func @dynamic_slice
// CHECK-SAME:    %[[ARG0:[a-zA-Z0-9_]*]]
// CHECK-SAME:    %[[ARG1:[a-zA-Z0-9_]*]]
// CHECK-SAME:    %[[ARG2:[a-zA-Z0-9_]*]]
// CHECK:         %[[C0:.*]] = constant 0 : i64
// CHECK:         %[[SCALAR1:.*]] = tensor.extract %[[ARG1]][] : tensor<i64>
// CHECK:         %[[UB1:.*]] = constant 2 : i64
// CHECK:         %[[COND1:.*]] = cmpi slt, %[[SCALAR1]], %[[UB1]] : i64
// CHECK:         %[[T1:.*]] = select %[[COND1]], %[[SCALAR1]], %[[UB1]] : i64
// CHECK:         %[[COND2:.*]] = cmpi sgt, %[[T1]], %[[C0]] : i64
// CHECK:         %[[CLAMPED1:.*]] = select %[[COND2]], %[[T1]], %[[C0]] : i64
// CHECK:         %[[START1:.*]] = index_cast %[[CLAMPED1]] : i64 to index
// CHECK:         %[[SCALAR2:.*]] = tensor.extract %[[ARG2]][] : tensor<i64>
// CHECK:         %[[UB2:.*]] = constant 0 : i64
// CHECK:         %[[COND3:.*]] = cmpi slt, %[[SCALAR2]], %[[UB2]] : i64
// CHECK:         %[[T2:.*]] = select %[[COND3]], %[[SCALAR2]], %[[UB2]] : i64
// CHECK:         %[[COND4:.*]] = cmpi sgt, %[[T2]], %[[C0]] : i64
// CHECK:         %[[CLAMPED2:.*]] = select %[[COND4]], %[[T2]], %[[C0]] : i64
// CHECK:         %[[START2:.*]] = index_cast %[[CLAMPED2]] : i64 to index
// CHECK:           subtensor %[[ARG0]][%[[START1]], %[[START2]]] [1, 4] [1, 1]

// -----

func @pad_cst(%arg0: tensor<12x4xf32>) -> tensor<18x12xf32> {
  %0 = constant dense<0.0> : tensor<f32>
  %1 = "mhlo.pad"(%arg0, %0) {
    edge_padding_high = dense<[2, 3]> : tensor<2xi64>,
    edge_padding_low = dense<[4, 5]> : tensor<2xi64>,
    interior_padding = dense<0> : tensor<2xi64>
  } : (tensor<12x4xf32>, tensor<f32>) -> tensor<18x12xf32>
  return %1 : tensor<18x12xf32>
}
// CHECK-LABEL: func @pad_cst
// CHECK-SAME:    %[[ARG0:[a-zA-Z0-9_]*]]
//   CHECK-DAG: %[[CST:.+]] = constant dense<0.000000e+00> : tensor<f32>
//   CHECK-DAG: %[[PAD:.+]] = tensor.extract %[[CST]][] : tensor<f32>
//   CHECK-DAG: %[[C4:.+]] = constant 4 : index
//   CHECK-DAG: %[[C2:.+]] = constant 2 : index
//   CHECK-DAG: %[[C5:.+]] = constant 5 : index
//   CHECK-DAG: %[[C3:.+]] = constant 3 : index
//       CHECK: linalg.pad_tensor %[[ARG0]] low[%[[C4]], %[[C5]]] high[%[[C2]], %[[C3]]]
//       CHECK:  linalg.yield %[[PAD]] : f32
//       CHECK: } : tensor<12x4xf32> to tensor<18x12xf32>

// -----

func @pad_tensor(%arg0: tensor<12x4xf32>, %arg1: tensor<f32>) -> tensor<18x12xf32> {
  %0 = "mhlo.pad"(%arg0, %arg1) {
    edge_padding_high = dense<[2, 3]> : tensor<2xi64>,
    edge_padding_low = dense<[4, 5]> : tensor<2xi64>,
    interior_padding = dense<0> : tensor<2xi64>
  } : (tensor<12x4xf32>, tensor<f32>) -> tensor<18x12xf32>
  return %0 : tensor<18x12xf32>
}
// CHECK-LABEL: func @pad_tensor
//  CHECK-SAME:   %[[ARG0:[a-zA-Z0-9_]*]]
//  CHECK-SAME:   %[[ARG1:[a-zA-Z0-9_]*]]
//   CHECK-DAG:   %[[C4:.+]] = constant 4 : index
//   CHECK-DAG:   %[[C2:.+]] = constant 2 : index
//   CHECK-DAG:   %[[C5:.+]] = constant 5 : index
//   CHECK-DAG:   %[[C3:.+]] = constant 3 : index
//   CHECK-DAG:   %[[PAD:.+]] = tensor.extract %[[ARG1]][] : tensor<f32>
//       CHECK:   linalg.pad_tensor %[[ARG0]] low[%[[C4]], %[[C5]]] high[%[[C2]], %[[C3]]]
//       CHECK:     linalg.yield %[[PAD]] : f32
//       CHECK:   } : tensor<12x4xf32> to tensor<18x12xf32>

// -----

func @linalg.conv_1d_input_nwc_filter_wcf(%arg0: tensor<?x8x?xf32>, %arg1: tensor<2x?x?xf32>)
  -> tensor<?x7x?xf32> {
  %0 = "mhlo.convolution"(%arg0, %arg1) {
    batch_group_count = 1 : i64,
    dimension_numbers = {
      input_batch_dimension = 0 : i64,
      input_feature_dimension = 2 : i64,
      input_spatial_dimensions = dense<[1]> : tensor<1xi64>,
      kernel_input_feature_dimension = 1 : i64,
      kernel_output_feature_dimension = 2 : i64,
      kernel_spatial_dimensions = dense<[0]> : tensor<1xi64>,
      output_batch_dimension = 0 : i64,
      output_feature_dimension = 2 : i64,
      output_spatial_dimensions = dense<[1]> : tensor<1xi64>
    },
    feature_group_count = 1 : i64,
    padding = dense<[[0], [0]]> : tensor<2x1xi64>,
    rhs_dilation = dense<1> : tensor<1xi64>,
    window_strides = dense<1> : tensor<1xi64>
  } : (tensor<?x8x?xf32>, tensor<2x?x?xf32>) -> tensor<?x7x?xf32>
  return %0 : tensor<?x7x?xf32>
}
// CHECK-LABEL: func @linalg.conv_1d_input_nwc_filter_wcf
// CHECK-SAME:    %[[ARG0:[a-zA-Z0-9_]*]]
// CHECK-SAME:    %[[ARG1:[a-zA-Z0-9_]*]]
// CHECK:         %[[C0:.+]] = constant 0 : index
// CHECK:         %[[DIM0:.+]] = memref.dim %[[ARG0]], %[[C0]] : tensor<?x8x?xf32>
// CHECK:         %[[C2:.+]] = constant 2 : index
// CHECK:         %[[DIM2:.+]] = memref.dim %[[ARG1]], %[[C2]] : tensor<2x?x?xf32>
// CHECK:         %[[INIT:.+]] = linalg.init_tensor [%[[DIM0]], 7, %[[DIM2]]]
// CHECK:         %[[ZERO:.+]] = constant 0.000000e+00 : f32
// CHECK:         %[[FILL:.+]] = linalg.fill(%[[INIT]], %[[ZERO]])
// CHECK:         linalg.conv_1d_input_nwc_filter_wcf
// CHECK-SAME:      {dilations = dense<1> : tensor<1xi64>
// CHECK-SAME:       strides = dense<1> : tensor<1xi64>}
// CHECK-SAME:     ins(%[[ARG0]], %[[ARG1]] : tensor<?x8x?xf32>, tensor<2x?x?xf32>)
// CHECK-SAME:    outs(%[[FILL]] : tensor<?x7x?xf32>) -> tensor<?x7x?xf32>

// -----

func @conv_2d_input_nhwc_filter_hwcf(%arg0: tensor<?x4x5x?xf32>, %arg1: tensor<3x2x?x?xf32>)
  -> tensor<?x2x3x?xf32> {
  %0 = "mhlo.convolution"(%arg0, %arg1) {
    batch_group_count = 1 : i64,
    dimension_numbers = {
      input_batch_dimension = 0 : i64,
      input_feature_dimension = 3 : i64,
      input_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>,
      kernel_input_feature_dimension = 2 : i64,
      kernel_output_feature_dimension = 3 : i64,
      kernel_spatial_dimensions = dense<[0, 1]> : tensor<2xi64>,
      output_batch_dimension = 0 : i64,
      output_feature_dimension = 3 : i64,
      output_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>
    },
    feature_group_count = 1 : i64,
    padding = dense<[[0, 0], [0, 0]]> : tensor<2x2xi64>,
    rhs_dilation = dense<1> : tensor<2xi64>,
    window_strides = dense<1> : tensor<2xi64>
  } : (tensor<?x4x5x?xf32>, tensor<3x2x?x?xf32>) -> tensor<?x2x3x?xf32>
  return %0 : tensor<?x2x3x?xf32>
}
// CHECK-LABEL: func @conv_2d_input_nhwc_filter_hwcf
// CHECK-SAME:    %[[ARG0:[a-zA-Z0-9_]*]]
// CHECK-SAME:    %[[ARG1:[a-zA-Z0-9_]*]]
// CHECK:         %[[C0:.+]] = constant 0 : index
// CHECK:         %[[DIM0:.+]] = memref.dim %[[ARG0]], %[[C0]] : tensor<?x4x5x?xf32>
// CHECK:         %[[C3:.+]] = constant 3 : index
// CHECK:         %[[DIM3:.+]] = memref.dim %[[ARG1]], %[[C3]] : tensor<3x2x?x?xf32>
// CHECK:         %[[INIT:.+]] = linalg.init_tensor [%[[DIM0]], 2, 3, %[[DIM3]]]
// CHECK:         %[[ZERO:.+]] = constant 0.000000e+00 : f32
// CHECK:         %[[FILL:.+]] = linalg.fill(%[[INIT]], %[[ZERO]])
// CHECK:         linalg.conv_2d_input_nhwc_filter_hwcf
// CHECK-SAME:      {dilations = dense<1> : tensor<2xi64>
// CHECK-SAME:       strides = dense<1> : tensor<2xi64>}
// CHECK-SAME:     ins(%[[ARG0]], %[[ARG1]] : tensor<?x4x5x?xf32>, tensor<3x2x?x?xf32>)
// CHECK-SAME:    outs(%[[FILL]] : tensor<?x2x3x?xf32>) -> tensor<?x2x3x?xf32>

// -----

func @conv_3d_input_ndhwc_filter_dhwcf(%arg0: tensor<?x8x8x8x?xf32>, %arg1: tensor<2x2x2x?x?xf32>)
  -> tensor<?x7x7x7x?xf32> {
  %0 = "mhlo.convolution"(%arg0, %arg1) {
    batch_group_count = 1 : i64,
    dimension_numbers = {
      input_batch_dimension = 0 : i64,
      input_feature_dimension = 4 : i64,
      input_spatial_dimensions = dense<[1, 2, 3]> : tensor<3xi64>,
      kernel_input_feature_dimension = 3 : i64,
      kernel_output_feature_dimension = 4 : i64,
      kernel_spatial_dimensions = dense<[0, 1, 2]> : tensor<3xi64>,
      output_batch_dimension = 0 : i64,
      output_feature_dimension = 4 : i64,
      output_spatial_dimensions = dense<[1, 2, 3]> : tensor<3xi64>
    },
    feature_group_count = 1 : i64,
    padding = dense<[[0, 0, 0], [0, 0, 0]]> : tensor<2x3xi64>,
    rhs_dilation = dense<1> : tensor<3xi64>,
    window_strides = dense<1> : tensor<3xi64>
  } : (tensor<?x8x8x8x?xf32>, tensor<2x2x2x?x?xf32>) -> tensor<?x7x7x7x?xf32>
  return %0 : tensor<?x7x7x7x?xf32>
}
// CHECK-LABEL: func @conv_3d_input_ndhwc_filter_dhwcf
// CHECK-SAME:    %[[ARG0:[a-zA-Z0-9_]*]]
// CHECK-SAME:    %[[ARG1:[a-zA-Z0-9_]*]]
// CHECK:         %[[C0:.+]] = constant 0 : index
// CHECK:         %[[DIM0:.+]] = memref.dim %[[ARG0]], %[[C0]] : tensor<?x8x8x8x?xf32>
// CHECK:         %[[C4:.+]] = constant 4 : index
// CHECK:         %[[DIM4:.+]] = memref.dim %[[ARG1]], %[[C4]] : tensor<2x2x2x?x?xf32>
// CHECK:         %[[INIT:.+]] = linalg.init_tensor [%[[DIM0]], 7, 7, 7, %[[DIM4]]]
// CHECK:         %[[ZERO:.+]] = constant 0.000000e+00 : f32
// CHECK:         %[[FILL:.+]] = linalg.fill(%[[INIT]], %[[ZERO]])
// CHECK:         linalg.conv_3d_input_ndhwc_filter_dhwcf
// CHECK-SAME:      {dilations = dense<1> : tensor<3xi64>
// CHECK-SAME:       strides = dense<1> : tensor<3xi64>}
// CHECK-SAME:     ins(%[[ARG0]], %[[ARG1]] : tensor<?x8x8x8x?xf32>, tensor<2x2x2x?x?xf32>)
// CHECK-SAME:    outs(%[[FILL]] : tensor<?x7x7x7x?xf32>) -> tensor<?x7x7x7x?xf32>

// -----

func @conv2d_1452x2223_dilated_valid(%arg0: tensor<1x4x5x2xf32>, %arg1: tensor<2x2x2x3xf32>)
  -> tensor<1x2x4x3xf32> {
  %0 = "mhlo.convolution"(%arg0, %arg1) {
    batch_group_count = 1 : i64,
    dimension_numbers = {
      input_batch_dimension = 0 : i64,
      input_feature_dimension = 3 : i64,
      input_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>,
      kernel_input_feature_dimension = 2 : i64,
      kernel_output_feature_dimension = 3 : i64,
      kernel_spatial_dimensions = dense<[0, 1]> : tensor<2xi64>,
      output_batch_dimension = 0 : i64,
      output_feature_dimension = 3 : i64,
      output_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>
    },
    feature_group_count = 1 : i64,
    padding = dense<0> : tensor<2x2xi64>,
    rhs_dilation = dense<[2, 1]> : tensor<2xi64>,
    window_strides = dense<1> : tensor<2xi64>
  } : (tensor<1x4x5x2xf32>, tensor<2x2x2x3xf32>) -> tensor<1x2x4x3xf32>
  return %0 : tensor<1x2x4x3xf32>
}
// CHECK-LABEL: func @conv2d_1452x2223_dilated_valid
// CHECK-SAME:    %[[ARG0:[a-zA-Z0-9_]*]]
// CHECK-SAME:    %[[ARG1:[a-zA-Z0-9_]*]]
// CHECK:         %[[INIT:.+]] = linalg.init_tensor [1, 2, 4, 3] : tensor<1x2x4x3xf32>
// CHECK:         %[[ZERO:.+]] = constant 0.000000e+00 : f32
// CHECK:         %[[FILL:.+]] = linalg.fill(%[[INIT]], %[[ZERO]]) : tensor<1x2x4x3xf32>, f32 -> tensor<1x2x4x3xf32>
// CHECK:         linalg.conv_2d_input_nhwc_filter_hwcf
// CHECK-SAME:      {dilations = dense<[2, 1]> : tensor<2xi64>
// CHECK-SAME:       strides = dense<1> : tensor<2xi64>}
// CHECK-SAME:     ins(%[[ARG0]], %[[ARG1]] : tensor<1x4x5x2xf32>, tensor<2x2x2x3xf32>)
// CHECK-SAME:    outs(%[[FILL]] : tensor<1x2x4x3xf32>) -> tensor<1x2x4x3xf32>

// -----

func @depthwise_conv(%arg0: tensor<2x4x5x2xf32>,
                     %arg1: tensor<2x2x2x3xf32>) -> tensor<2x3x4x6xf32> {
  %0 = "mhlo.convolution"(%arg0, %arg1) {
    batch_group_count = 1 : i64,
    dimension_numbers = {
      input_batch_dimension = 0 : i64,
      input_feature_dimension = 3 : i64,
      input_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>,
      kernel_input_feature_dimension = 2 : i64,
      kernel_output_feature_dimension = 3 : i64,
      kernel_spatial_dimensions = dense<[0, 1]> : tensor<2xi64>,
      output_batch_dimension = 0 : i64,
      output_feature_dimension = 3 : i64,
      output_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>
    },
    feature_group_count = 2 : i64,
    padding = dense<0> : tensor<2x2xi64>,
    rhs_dilation = dense<1> : tensor<2xi64>,
    window_strides = dense<1> : tensor<2xi64>} : (tensor<2x4x5x2xf32>, tensor<2x2x2x3xf32>) -> tensor<2x3x4x6xf32>
  return %0 : tensor<2x3x4x6xf32>
}
// CHECK-DAG:  #[[MAP0:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d0)>
// CHECK-DAG:  #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d1)>
// CHECK-DAG:  #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d2)>
// CHECK-DAG:  #[[MAP3:.+]] = affine_map<(d0, d1, d2, d3, d4) -> (d3, d4)>
// CHECK:      func @depthwise_conv
// CHECK-SAME:   %[[IN:[a-zA-Z0-9_]*]]
// CHECK-SAME:   %[[FILTER:[a-zA-Z0-9_]*]]
// CHECK:        %[[INIT:.+]] = linalg.init_tensor [2, 3, 4, 2, 3] : tensor<2x3x4x2x3xf32>
// CHECK:        %[[CST:.+]] = constant 0.000000e+00 : f32
// CHECK:        %[[FILL:.+]] = linalg.fill(%[[INIT]], %[[CST]]) : tensor<2x3x4x2x3xf32>, f32 -> tensor<2x3x4x2x3xf32>
// CHECK:        %[[OUT:.+]] = linalg.depthwise_conv_2d_input_nhwc_filter_hwcf
// CHECK-SAME:     {strides = dense<1> : tensor<2xi64>}
// CHECK-SAME:      ins(%[[IN]], %[[FILTER]] : tensor<2x4x5x2xf32>, tensor<2x2x2x3xf32>)
// CHECK-SAME:     outs(%[[FILL]] : tensor<2x3x4x2x3xf32>) -> tensor<2x3x4x2x3xf32>
// CHECK:        %{{.+}} = linalg.tensor_reshape %[[OUT]]
// CHECK-SAME:     [#[[MAP0]], #[[MAP1]], #[[MAP2]], #[[MAP3]]]
// CHECK-SAME:     : tensor<2x3x4x2x3xf32> into tensor<2x3x4x6xf32>

// -----

func @depthwise_conv_multiplier_1(%arg0: tensor<1x113x113x96xf32>,
                                  %arg1: tensor<3x3x1x96xf32>) -> tensor<1x56x56x96xf32> {
  %0 = "mhlo.convolution"(%arg0, %arg1) {
    batch_group_count = 1 : i64,
    dimension_numbers = {
      input_batch_dimension = 0 : i64,
      input_feature_dimension = 3 : i64,
      input_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>,
      kernel_input_feature_dimension = 2 : i64,
      kernel_output_feature_dimension = 3 : i64,
      kernel_spatial_dimensions = dense<[0, 1]> : tensor<2xi64>,
      output_batch_dimension = 0 : i64,
      output_feature_dimension = 3 : i64,
      output_spatial_dimensions = dense<[1, 2]> : tensor<2xi64>
    },
    feature_group_count = 96 : i64,
    padding = dense<0> : tensor<2x2xi64>,
    rhs_dilation = dense<1> : tensor<2xi64>,
    window_strides = dense<2> : tensor<2xi64>} : (tensor<1x113x113x96xf32>, tensor<3x3x1x96xf32>) -> tensor<1x56x56x96xf32>
  return %0 : tensor<1x56x56x96xf32>
}
// CHECK-DAG:   #[[MAP0:.+]] = affine_map<(d0, d1, d2, d3) -> (d0)>
// CHECK-DAG:   #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3) -> (d1)>
// CHECK-DAG:   #[[MAP2:.+]] = affine_map<(d0, d1, d2, d3) -> (d2, d3)>
// CHECK:       func @depthwise_conv_multiplier_1
// CHECK-SAME:    %[[IN:[a-zA-Z0-9_]*]]
// CHECK-SAME:    %[[FILTER:[a-zA-Z0-9_]*]]
// CHECK:         %[[INIT:.+]] = linalg.init_tensor [1, 56, 56, 96] : tensor<1x56x56x96xf32>
// CHECK:         %[[CST:.+]] = constant 0.000000e+00 : f32
// CHECK:         %[[FILL:.+]] = linalg.fill(%[[INIT]], %[[CST]]) : tensor<1x56x56x96xf32>, f32 -> tensor<1x56x56x96xf32>
// CHECK:         %[[RESHAPED_FILTER:.+]] = linalg.tensor_reshape %[[FILTER]] [#[[MAP0]], #[[MAP1]], #[[MAP2]]] : tensor<3x3x1x96xf32> into tensor<3x3x96xf32>
// CHECK:         %{{.+}} = linalg.depthwise_conv_2d_input_nhwc_filter_hwc
// CHECK-SAME:      {strides = dense<2> : tensor<2xi64>}
// CHECK-SAME:       ins(%[[IN]], %[[RESHAPED_FILTER]] : tensor<1x113x113x96xf32>, tensor<3x3x96xf32>)
// CHECK-SAME:      outs(%[[FILL]] : tensor<1x56x56x96xf32>) -> tensor<1x56x56x96xf32>

// -----

func @reduce_window_min_nhwc(%arg0: tensor<1x18x18x64xf32>,
                             %arg1: tensor<f32>) -> tensor<1x8x8x64xf32>{
  %0 = "mhlo.reduce_window"(%arg0, %arg1) ( {
  ^bb0(%arg2: tensor<f32>, %arg3 : tensor<f32>):
    %1 = mhlo.minimum %arg2, %arg3 : tensor<f32>
    "mhlo.return"(%1) : (tensor<f32>) -> ()
  }) {window_dimensions = dense<[1, 3, 3, 1]> : tensor<4xi64>,
      window_strides = dense<[1, 2, 2, 1]> : tensor<4xi64>} : (tensor<1x18x18x64xf32>, tensor<f32>) -> tensor<1x8x8x64xf32>
  return %0 : tensor<1x8x8x64xf32>
}
// CHECK-LABEL: func @reduce_window_min_nhwc
// CHECK-SAME:    %[[ARG0:[a-zA-Z0-9_]*]]
// CHECK-SAME:    %[[ARG1:[a-zA-Z0-9_]*]]
// CHECK:         %[[WINDOW:.+]] = linalg.init_tensor [3, 3] : tensor<3x3xf32>
// CHECK:         %[[INIT:.+]] = linalg.init_tensor [1, 8, 8, 64] : tensor<1x8x8x64xf32>
// CHECK:         %[[INIT_VAL:.+]] = tensor.extract %[[ARG1]][] : tensor<f32>
// CHECK:         %[[FILL:.+]] = linalg.fill(%[[INIT]], %[[INIT_VAL]]) : tensor<1x8x8x64xf32>, f32 -> tensor<1x8x8x64xf32>
// CHECK:         %[[RES:.+]] = linalg.pooling_nhwc_min
// CHECK-SAME:      {dilations = dense<1> : vector<2xi64>
// CHECK-SAME:       strides = dense<2> : vector<2xi64>}
// CHECK-SAME:      ins(%[[ARG0]], %[[WINDOW]] : tensor<1x18x18x64xf32>, tensor<3x3xf32>)
// CHECK-SAME:      outs(%[[FILL]] : tensor<1x8x8x64xf32>) -> tensor<1x8x8x64xf32>

// -----

func @reduce_window_max_nhwc(%arg0: tensor<1x18x18x64xf32>,
                             %arg1: tensor<f32>) -> tensor<1x8x8x64xf32>{
  %0 = "mhlo.reduce_window"(%arg0, %arg1) ( {
  ^bb0(%arg2: tensor<f32>, %arg3 : tensor<f32>):
    %1 = mhlo.maximum %arg2, %arg3 : tensor<f32>
    "mhlo.return"(%1) : (tensor<f32>) -> ()
  }) {window_dimensions = dense<[1, 3, 3, 1]> : tensor<4xi64>,
      window_strides = dense<[1, 2, 2, 1]> : tensor<4xi64>} : (tensor<1x18x18x64xf32>, tensor<f32>) -> tensor<1x8x8x64xf32>
  return %0 : tensor<1x8x8x64xf32>
}
// CHECK-LABEL: func @reduce_window_max_nhwc
// CHECK-SAME:    %[[ARG0:[a-zA-Z0-9_]*]]
// CHECK-SAME:    %[[ARG1:[a-zA-Z0-9_]*]]
// CHECK:         %[[WINDOW:.+]] = linalg.init_tensor [3, 3] : tensor<3x3xf32>
// CHECK:         %[[INIT:.+]] = linalg.init_tensor [1, 8, 8, 64] : tensor<1x8x8x64xf32>
// CHECK:         %[[INIT_VAL:.+]] = tensor.extract %[[ARG1]][] : tensor<f32>
// CHECK:         %[[FILL:.+]] = linalg.fill(%[[INIT]], %[[INIT_VAL]]) : tensor<1x8x8x64xf32>, f32 -> tensor<1x8x8x64xf32>
// CHECK:         %[[RES:.+]] = linalg.pooling_nhwc_max
// CHECK-SAME:      {dilations = dense<1> : vector<2xi64>
// CHECK-SAME:       strides = dense<2> : vector<2xi64>}
// CHECK-SAME:      ins(%[[ARG0]], %[[WINDOW]] : tensor<1x18x18x64xf32>, tensor<3x3xf32>)
// CHECK-SAME:      outs(%[[FILL]] : tensor<1x8x8x64xf32>) -> tensor<1x8x8x64xf32>

// -----

func @reduce_window_sum_nhwc(%arg0: tensor<1x18x18x64xf32>,
                             %arg1: tensor<f32>) -> tensor<1x8x8x64xf32>{
  %0 = "mhlo.reduce_window"(%arg0, %arg1) ( {
  ^bb0(%arg2: tensor<f32>, %arg3 : tensor<f32>):
    %1 = mhlo.add %arg2, %arg3 : tensor<f32>
    "mhlo.return"(%1) : (tensor<f32>) -> ()
  }) {window_dimensions = dense<[1, 3, 3, 1]> : tensor<4xi64>,
      window_strides = dense<[1, 2, 2, 1]> : tensor<4xi64>} : (tensor<1x18x18x64xf32>, tensor<f32>) -> tensor<1x8x8x64xf32>
  return %0 : tensor<1x8x8x64xf32>
}
// CHECK-LABEL: func @reduce_window_sum_nhwc
// CHECK-SAME:    %[[ARG0:[a-zA-Z0-9_]*]]
// CHECK-SAME:    %[[ARG1:[a-zA-Z0-9_]*]]
// CHECK:         %[[WINDOW:.+]] = linalg.init_tensor [3, 3] : tensor<3x3xf32>
// CHECK:         %[[INIT:.+]] = linalg.init_tensor [1, 8, 8, 64] : tensor<1x8x8x64xf32>
// CHECK:         %[[INIT_VAL:.+]] = tensor.extract %[[ARG1]][] : tensor<f32>
// CHECK:         %[[FILL:.+]] = linalg.fill(%[[INIT]], %[[INIT_VAL]]) : tensor<1x8x8x64xf32>, f32 -> tensor<1x8x8x64xf32>
// CHECK:         %[[RES:.+]] = linalg.pooling_nhwc_sum
// CHECK-SAME:      {dilations = dense<1> : vector<2xi64>
// CHECK-SAME:       strides = dense<2> : vector<2xi64>}
// CHECK-SAME:      ins(%[[ARG0]], %[[WINDOW]] : tensor<1x18x18x64xf32>, tensor<3x3xf32>)
// CHECK-SAME:      outs(%[[FILL]] : tensor<1x8x8x64xf32>) -> tensor<1x8x8x64xf32>

// -----

func @reduce_window_max_nhwc_with_cst(%arg0: tensor<1x18x18x64xf32>) -> tensor<1x8x8x64xf32> {
  %0 = constant dense<0xFF800000> : tensor<f32>
  %1 = "mhlo.reduce_window"(%arg0, %0) ( {
  ^bb0(%arg1: tensor<f32>, %arg2 : tensor<f32>):
    %2 = mhlo.maximum %arg1, %arg2 : tensor<f32>
    "mhlo.return"(%2) : (tensor<f32>) -> ()
  }) {window_dimensions = dense<[1, 3, 3, 1]> : tensor<4xi64>,
      window_strides = dense<[1, 2, 2, 1]> : tensor<4xi64>} : (tensor<1x18x18x64xf32>, tensor<f32>) -> tensor<1x8x8x64xf32>
  return %1 : tensor<1x8x8x64xf32>
}

// CHECK-LABEL: func @reduce_window_max_nhwc
// CHECK-SAME:    %[[ARG0:[a-zA-Z0-9_]*]]
// CHECK-DAG:     %[[CST:.+]] = constant dense<0xFF800000> : tensor<f32>
// CHECK:         %[[WINDOW:.+]] = linalg.init_tensor [3, 3] : tensor<3x3xf32>
// CHECK:         %[[INIT:.+]] = linalg.init_tensor [1, 8, 8, 64] : tensor<1x8x8x64xf32
// CHECK:         %[[INIT_VAL:.+]] = tensor.extract %[[CST]][] : tensor<f32>
// CHECK:         %[[FILL:.+]] = linalg.fill(%[[INIT]], %[[INIT_VAL]]) : tensor<1x8x8x64xf32>, f32 -> tensor<1x8x8x64xf32>
// CHECK:         %[[RES:.+]] = linalg.pooling_nhwc_max
// CHECK-SAME:      {dilations = dense<1> : vector<2xi64>
// CHECK-SAME:       strides = dense<2> : vector<2xi64>}
// CHECK-SAME:      ins(%[[ARG0]], %[[WINDOW]] : tensor<1x18x18x64xf32>, tensor<3x3xf32>)
// CHECK-SAME:      outs(%[[FILL]] : tensor<1x8x8x64xf32>) -> tensor<1x8x8x64xf32>

// -----

func @reduce_window_sum_max_nhwc(%arg0: tensor<1x18x18x64xf32>,
                             %arg1: tensor<f32>) -> (tensor<1x8x8x64xf32>, tensor<1x8x8x64xf32>) {
  %0:2 = "mhlo.reduce_window"(%arg0, %arg0, %arg1, %arg1) ( {
  ^bb0(%arg2: tensor<f32>, %arg3 : tensor<f32>, %arg4: tensor<f32>, %arg5 : tensor<f32>):
    %1 = mhlo.add %arg2, %arg4 : tensor<f32>
    %2 = mhlo.maximum %arg3, %arg5 : tensor<f32>
    "mhlo.return"(%1, %2) : (tensor<f32>, tensor<f32>) -> ()
  }) {window_dimensions = dense<[1, 3, 3, 1]> : tensor<4xi64>,
      window_strides = dense<[1, 2, 2, 1]> : tensor<4xi64>} : (tensor<1x18x18x64xf32>, tensor<1x18x18x64xf32>, tensor<f32>, tensor<f32>) -> (tensor<1x8x8x64xf32>, tensor<1x8x8x64xf32>)
  return %0#0, %0#1 : tensor<1x8x8x64xf32>, tensor<1x8x8x64xf32>
}

// CHECK-LABEL: func @reduce_window_sum_max_nhwc
// CHECK-SAME:    %[[ARG0:[a-zA-Z0-9_]*]]
// CHECK-SAME:    %[[ARG1:[a-zA-Z0-9_]*]]
// CHECK:         %[[WINDOW0:.+]] = linalg.init_tensor [3, 3] : tensor<3x3xf32>
// CHECK:         %[[INIT0:.+]] = linalg.init_tensor [1, 8, 8, 64] : tensor<1x8x8x64xf32>
// CHECK:         %[[INIT_VAL0:.+]] = tensor.extract %[[ARG1]][] : tensor<f32>
// CHECK:         %[[FILL0:.+]] = linalg.fill(%[[INIT]], %[[INIT_VAL]]) : tensor<1x8x8x64xf32>, f32 -> tensor<1x8x8x64xf32>
// CHECK:         %[[RES0:.+]] = linalg.pooling_nhwc_sum
// CHECK-SAME:      {dilations = dense<1> : vector<2xi64>
// CHECK-SAME:       strides = dense<2> : vector<2xi64>}
// CHECK-SAME:      ins(%[[ARG0]], %[[WINDOW0]] : tensor<1x18x18x64xf32>, tensor<3x3xf32>)
// CHECK-SAME:      outs(%[[FILL0]] : tensor<1x8x8x64xf32>) -> tensor<1x8x8x64xf32>
// CHECK:         %[[WINDOW1:.+]] = linalg.init_tensor [3, 3] : tensor<3x3xf32>
// CHECK:         %[[INIT1:.+]] = linalg.init_tensor [1, 8, 8, 64] : tensor<1x8x8x64xf32>
// CHECK:         %[[INIT_VAL1:.+]] = tensor.extract %[[ARG1]][] : tensor<f32>
// CHECK:         %[[FILL1:.+]] = linalg.fill(%[[INIT1]], %[[INIT_VAL1]]) : tensor<1x8x8x64xf32>, f32 -> tensor<1x8x8x64xf32>
// CHECK:         %[[RES1:.+]] = linalg.pooling_nhwc_max
// CHECK-SAME:      {dilations = dense<1> : vector<2xi64>
// CHECK-SAME:       strides = dense<2> : vector<2xi64>}
// CHECK-SAME:      ins(%[[ARG0]], %[[WINDOW1]] : tensor<1x18x18x64xf32>, tensor<3x3xf32>)
// CHECK-SAME:      outs(%[[FILL1]] : tensor<1x8x8x64xf32>) -> tensor<1x8x8x64xf32>
// CHECK:         return %[[RES0]], %[[RES1]]

// -----

func @torch_select_index(%arg0: tensor<5x1x5xi32>,
                         %arg1: tensor<2xi32>) ->  tensor<2x1x5xi32> {
  %0 = "mhlo.torch_index_select"(%arg0, %arg1) {
    dim = 0 : i64,
    batch_dims = 0 : i64
  } : (tensor<5x1x5xi32>, tensor<2xi32>) -> tensor<2x1x5xi32>
  return %0 : tensor<2x1x5xi32>
}
//  CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2) -> (d0)>
//  CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
//      CHECK: func @torch_select_index
// CHECK-SAME:   %[[INPUT:[a-zA-Z0-9_]*]]
// CHECK-SAME:   %[[INDEX:[a-zA-Z0-9_]*]]
//      CHECK: linalg.indexed_generic {
// CHECK-SAME:   indexing_maps
// CHECK-SAME:   #[[MAP0]], #[[MAP1]]
// CHECK-SAME:   iterator_types = ["parallel", "parallel", "parallel"]
// CHECK-SAME: ins(%[[INDEX]] : tensor<2xi32>)
//      CHECK: ^{{.+}}(
// CHECK-SAME:   %[[I:.+]]: index, %[[J:.+]]: index, %[[K:.+]]: index
// CHECK-SAME:   %[[VAL:.+]]: i32, %{{.+}}: i32):
//      CHECK:   %[[CAST:.+]] = index_cast %[[VAL]] : i32 to index
//      CHECK:   %[[VAL2:.+]] = tensor.extract %[[INPUT]][%[[CAST]], %[[J]], %[[K]]] : tensor<5x1x5xi32>
//      CHECK:   linalg.yield %[[VAL2]] : i32

// -----

func @torch_select_index_scalar(%arg0: tensor<4x8xf32>,
                                %arg1: tensor<i32>) -> tensor<8xf32> {
  %0 = "mhlo.torch_index_select"(%arg0, %arg1) {
    batch_dims = 0 : i64,
    dim = 0 : i64
  } : (tensor<4x8xf32>, tensor<i32>) -> tensor<8xf32>
  return %0 : tensor<8xf32>
}

//  CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0) -> ()>
//  CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0) -> (d0)>
//      CHECK: func @torch_select_index_scalar
// CHECK-SAME:   %[[INPUT:[a-zA-Z0-9_]*]]
// CHECK-SAME:   %[[INDEX:[a-zA-Z0-9_]*]]
//      CHECK: %[[T0:.+]] = linalg.init_tensor [8] : tensor<8xf32>
//      CHECK: linalg.indexed_generic {
// CHECK-SAME:   indexing_maps
// CHECK-SAME:   #[[MAP0]], #[[MAP1]]
// CHECK-SAME:   iterator_types = ["parallel"]
// CHECK-SAME:   ins(%[[INDEX]] : tensor<i32>) outs(%[[T0]] : tensor<8xf32>)
//      CHECK:   ^{{.+}}(
// CHECK-SAME:     %[[I:[a-zA-Z0-9_]+]]: index, %[[VAL:[a-zA-Z0-9_]+]]: i32, %{{.+}}: f32):
//      CHECK:     %[[CAST:.+]] = index_cast %[[VAL]] : i32 to index
//      CHECK:     %[[VAL2:.+]] = tensor.extract %[[INPUT]][%[[CAST]], %[[I]]] : tensor<4x8xf32>
//      CHECK:     linalg.yield %[[VAL2]] : f32

// -----

func @torch_select_index_batch(%arg0: tensor<4x7x8x2xf32>,
                               %arg1: tensor<4x1xi32>) -> tensor<4x7x1x2xf32> {
  %0 = "mhlo.torch_index_select"(%arg0, %arg1) {
    dim = 2 : i64,
    batch_dims = 1 : i64
  } : (tensor<4x7x8x2xf32>, tensor<4x1xi32>) -> tensor<4x7x1x2xf32>
  return %0 : tensor<4x7x1x2xf32>
}
//  CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d2)>
//  CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
//      CHECK: func @torch_select_index_batch
// CHECK-SAME:   %[[INPUT:[a-zA-Z0-9_]*]]
// CHECK-SAME:   %[[INDEX:[a-zA-Z0-9_]*]]
//      CHECK: linalg.indexed_generic {
// CHECK-SAME:   indexing_maps
// CHECK-SAME:   #[[MAP0]], #[[MAP1]]
// CHECK-SAME:   iterator_types = ["parallel", "parallel", "parallel", "parallel"]
// CHECK-SAME: ins(%[[INDEX]] : tensor<4x1xi32>)
// CHECK-NEXT: ^{{.+}}(
// CHECK-SAME:   %[[I:[a-zA-Z0-9_]+]]: index, %[[J:[a-zA-Z0-9_]+]]: index,
// CHECK-SAME:   %[[K:[a-zA-Z0-9_]+]]: index, %[[L:.+]]: index,
// CHECK-SAME:   %[[VAL:.+]]: i32, %{{.+}}: f32):
//      CHECK:   %[[CAST:.+]] = index_cast %[[VAL]] : i32 to index
//      CHECK:   %[[VAL2:.+]] = tensor.extract %[[INPUT]][%[[I]], %[[J]], %[[CAST]], %[[L]]] : tensor<4x7x8x2xf32>
//      CHECK:   linalg.yield %[[VAL2]] : f32

// -----

func @torch_index_select_dynamic(%input: tensor<?x?x?x?xf32>,
                                 %index: tensor<?x?xi32>) -> tensor<?x?x?x?xf32>{
  %0 = "mhlo.torch_index_select"(%input, %index) {
    batch_dims = 1 : i64,
    dim = 2 : i64
  } : (tensor<?x?x?x?xf32>, tensor<?x?xi32>) -> tensor<?x?x?x?xf32>
  return %0 : tensor<?x?x?x?xf32>
}
//      CHECK: #[[MAP0:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d2)>
//      CHECK: #[[MAP1:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
//      CHECK: func @torch_index_select_dynamic
// CHECK-SAME:   %[[INPUT:[a-zA-Z0-9_]*]]
// CHECK-SAME:   %[[INDEX:[a-zA-Z0-9_]*]]
//      CHECK:   %[[C0:.+]] = constant 0 : index
//      CHECK:   %[[D0:.+]] = memref.dim %[[INPUT]], %[[C0]]
//      CHECK:   %[[C1:.+]] = constant 1 : index
//      CHECK:   %[[D1:.+]] = memref.dim %[[INPUT]], %[[C1]]
//      CHECK:   %[[C1:.+]] = constant 1 : index
//      CHECK:   %[[D2:.+]] = memref.dim %[[INDEX]], %[[C1]]
//      CHECK:   %[[C3:.+]] = constant 3 : index
//      CHECK:   %[[D3:.+]] = memref.dim %[[INPUT]], %[[C3]]
//      CHECK:   %[[INIT:.+]] = linalg.init_tensor [%[[D0]], %[[D1]], %[[D2]], %[[D3]]]
//      CHECK:   %[[RESULT:.+]] = linalg.indexed_generic
// CHECK-SAME:     indexing_maps = [#[[MAP0]], #[[MAP1]]]
// CHECK-SAME:     iterator_types = ["parallel", "parallel", "parallel", "parallel"]
// CHECK-SAME:     ins(%[[INDEX]] : tensor<?x?xi32>)
// CHECK-SAME:     outs(%[[INIT]] : tensor<?x?x?x?xf32>)
//      CHECK:     ^{{.+}}(
// CHECK-SAME:       %[[ARG0:[a-zA-Z0-9_]+]]: index,
// CHECK-SAME:       %[[ARG1:[a-zA-Z0-9_]+]]: index,
// CHECK-SAME:       %[[ARG2:[a-zA-Z0-9_]+]]: index
// CHECK-SAME:       %[[ARG3:[a-zA-Z0-9_]+]]: index,
// CHECK-SAME:       %[[ARG4:[a-zA-Z0-9_]+]]: i32, %{{[a-zA-Z0-9_]+}}: f32)
//      CHECK:       %[[POS:.+]] = index_cast %[[ARG4]]
//      CHECK:       %[[YIELD:.+]] = tensor.extract %[[INPUT]][%[[ARG0]], %[[ARG1]], %[[POS]], %[[ARG3]]]
//      CHECK:       linalg.yield %[[YIELD]]

// -----

// CHECK-LABEL:   func @concatenate(
// CHECK-SAME:   %[[VAL_0:[a-zA-Z0-9_]*]]
// CHECK-SAME:   %[[VAL_1:[a-zA-Z0-9_]*]]
// CHECK-SAME:   %[[VAL_2:[a-zA-Z0-9_]*]]
// CHECK:           %[[VAL_3:.*]] = constant 0 : index
// CHECK:           %[[VAL_4:.*]] = constant 0 : index
// CHECK:           %[[VAL_5:.*]] = memref.dim %[[VAL_0]], %[[VAL_4]] : tensor<?x?xi32>
// CHECK:           %[[VAL_6:.*]] = constant 1 : index
// CHECK:           %[[VAL_7:.*]] = memref.dim %[[VAL_0]], %[[VAL_6]] : tensor<?x?xi32>
// CHECK:           %[[VAL_8:.*]] = constant 1 : index
// CHECK:           %[[VAL_9:.*]] = memref.dim %[[VAL_1]], %[[VAL_8]] : tensor<?x?xi32>
// CHECK:           %[[VAL_10:.*]] = addi %[[VAL_7]], %[[VAL_9]] : index
// CHECK:           %[[VAL_11:.*]] = constant 1 : index
// CHECK:           %[[VAL_12:.*]] = memref.dim %[[VAL_2]], %[[VAL_11]] : tensor<?x?xi32>
// CHECK:           %[[VAL_13:.*]] = addi %[[VAL_10]], %[[VAL_12]] : index
// CHECK:           %[[VAL_14:.*]] = linalg.init_tensor [%[[VAL_5]], %[[VAL_13]]] : tensor<?x?xi32>
// CHECK:           %[[VAL_15:.*]] = linalg.indexed_generic {indexing_maps = [#map], iterator_types = ["parallel", "parallel"]} outs(%[[VAL_14]] : tensor<?x?xi32>) {
// CHECK:           ^bb0(%[[VAL_16:.*]]: index, %[[VAL_17:.*]]: index, %[[VAL_18:.*]]: i32):
// CHECK:             %[[VAL_19:.*]] = constant 1 : index
// CHECK:             %[[VAL_20:.*]] = memref.dim %[[VAL_0]], %[[VAL_19]] : tensor<?x?xi32>
// CHECK:             %[[VAL_21:.*]] = addi %[[VAL_3]], %[[VAL_20]] : index
// CHECK:             %[[VAL_22:.*]] = cmpi ult, %[[VAL_17]], %[[VAL_21]] : index
// CHECK:             %[[VAL_23:.*]] = scf.if %[[VAL_22]] -> (i32) {
// CHECK:               %[[VAL_24:.*]] = subi %[[VAL_17]], %[[VAL_3]] : index
// CHECK:               %[[VAL_25:.*]] = tensor.extract %[[VAL_0]][%[[VAL_16]], %[[VAL_24]]] : tensor<?x?xi32>
// CHECK:               scf.yield %[[VAL_25]] : i32
// CHECK:             } else {
// CHECK:               %[[VAL_26:.*]] = constant 1 : index
// CHECK:               %[[VAL_27:.*]] = memref.dim %[[VAL_1]], %[[VAL_26]] : tensor<?x?xi32>
// CHECK:               %[[VAL_28:.*]] = addi %[[VAL_21]], %[[VAL_27]] : index
// CHECK:               %[[VAL_29:.*]] = cmpi ult, %[[VAL_17]], %[[VAL_28]] : index
// CHECK:               %[[VAL_30:.*]] = scf.if %[[VAL_29]] -> (i32) {
// CHECK:                 %[[VAL_31:.*]] = subi %[[VAL_17]], %[[VAL_21]] : index
// CHECK:                 %[[VAL_32:.*]] = tensor.extract %[[VAL_1]][%[[VAL_16]], %[[VAL_31]]] : tensor<?x?xi32>
// CHECK:                 scf.yield %[[VAL_32]] : i32
// CHECK:               } else {
// CHECK:                 %[[VAL_33:.*]] = subi %[[VAL_17]], %[[VAL_28]] : index
// CHECK:                 %[[VAL_34:.*]] = tensor.extract %[[VAL_2]][%[[VAL_16]], %[[VAL_33]]] : tensor<?x?xi32>
// CHECK:                 scf.yield %[[VAL_34]] : i32
// CHECK:               }
// CHECK:               scf.yield %[[VAL_35:.*]] : i32
// CHECK:             }
// CHECK:             linalg.yield %[[VAL_36:.*]] : i32
// CHECK:           } -> tensor<?x?xi32>
// CHECK:           return %[[VAL_37:.*]] : tensor<?x?xi32>
// CHECK:         }
func @concatenate(%a: tensor<?x?xi32>, %b: tensor<?x?xi32>, %c: tensor<?x?xi32>) -> tensor<?x?xi32> {
    %concat = "mhlo.concatenate"(%a, %b, %c) {
      dimension = 1
    } : (tensor<?x?xi32>, tensor<?x?xi32>, tensor<?x?xi32>) -> tensor<?x?xi32>
    return %concat : tensor<?x?xi32>
}