// RUN: mlir-opt %s -sparsification --canonicalize | FileCheck %s --check-prefix=CHECK-HIR
//
// RUN: mlir-opt %s -sparsification --sparse-tensor-conversion --canonicalize | \
// RUN: FileCheck %s --check-prefix=CHECK-MIR

#X = #sparse_tensor.encoding<{
 dimLevelType = [ "dense", "dense", "dense" ],
 dimOrdering = affine_map<(i,j,k) -> (k,i,j)>
}>

#trait = {
  indexing_maps = [
    affine_map<(i,j,k) -> (k,i,j)>,  // A (in)
    affine_map<(i,j,k) -> ()>        // X (out)
  ],
  iterator_types = ["reduction", "reduction", "reduction"]
}

// CHECK-HIR-LABEL:   func @sparse_dynamic_dims(
// CHECK-HIR-SAME:      %[[VAL_0:.*]]: tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-HIR-SAME:      %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> {
// CHECK-HIR-DAG:       %[[VAL_2:.*]] = arith.constant 1 : index
// CHECK-HIR-DAG:       %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-HIR-DAG:       %[[VAL_4:.*]] = arith.constant 2 : index
// CHECK-HIR:           %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-HIR:           %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-HIR:           %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-HIR:           %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-HIR:           %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32>
// CHECK-HIR:           %[[VAL_10:.*]] = memref.alloc() : memref<f32>
// CHECK-HIR:           memref.copy %[[VAL_9]], %[[VAL_10]] : memref<f32> to memref<f32>
// CHECK-HIR:           %[[VAL_11:.*]] = memref.load %[[VAL_10]][] : memref<f32>
// CHECK-HIR:           %[[VAL_12:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_3]] to %[[VAL_5]] step %[[VAL_2]] iter_args(%[[VAL_14:.*]] = %[[VAL_11]]) -> (f32) {
// CHECK-HIR:             %[[VAL_15:.*]] = scf.for %[[VAL_16:.*]] = %[[VAL_3]] to %[[VAL_6]] step %[[VAL_2]] iter_args(%[[VAL_17:.*]] = %[[VAL_14]]) -> (f32) {
// CHECK-HIR:               %[[VAL_18:.*]] = arith.muli %[[VAL_6]], %[[VAL_13]] : index
// CHECK-HIR:               %[[VAL_19:.*]] = arith.addi %[[VAL_18]], %[[VAL_16]] : index
// CHECK-HIR:               %[[VAL_20:.*]] = scf.for %[[VAL_21:.*]] = %[[VAL_3]] to %[[VAL_7]] step %[[VAL_2]] iter_args(%[[VAL_22:.*]] = %[[VAL_17]]) -> (f32) {
// CHECK-HIR:                 %[[VAL_23:.*]] = arith.muli %[[VAL_7]], %[[VAL_19]] : index
// CHECK-HIR:                 %[[VAL_24:.*]] = arith.addi %[[VAL_23]], %[[VAL_21]] : index
// CHECK-HIR:                 %[[VAL_25:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_24]]] : memref<?xf32>
// CHECK-HIR:                 %[[VAL_26:.*]] = arith.addf %[[VAL_22]], %[[VAL_25]] : f32
// CHECK-HIR:                 scf.yield %[[VAL_26]] : f32
// CHECK-HIR:               }
// CHECK-HIR:               scf.yield %[[VAL_20]] : f32
// CHECK-HIR:             }
// CHECK-HIR:             scf.yield %[[VAL_15]] : f32
// CHECK-HIR:           }
// CHECK-HIR:           memref.store %[[VAL_12]], %[[VAL_10]][] : memref<f32>
// CHECK-HIR:           %[[VAL_30:.*]] = bufferization.to_tensor %[[VAL_10]] : memref<f32>
// CHECK-HIR:           return %[[VAL_30]] : tensor<f32>
// CHECK-HIR:         }
//
// CHECK-MIR-LABEL:   func @sparse_dynamic_dims(
// CHECK-MIR-SAME:      %[[VAL_0:.*]]: !llvm.ptr<i8>,
// CHECK-MIR-SAME:      %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> {
// CHECK-MIR-DAG:       %[[VAL_2:.*]] = arith.constant 2 : index
// CHECK-MIR-DAG:       %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-MIR-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-MIR:           %[[VAL_5:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_4]]) : (!llvm.ptr<i8>, index) -> index
// CHECK-MIR:           %[[VAL_6:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_3]]) : (!llvm.ptr<i8>, index) -> index
// CHECK-MIR:           %[[VAL_7:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_2]]) : (!llvm.ptr<i8>, index) -> index
// CHECK-MIR:           %[[VAL_8:.*]] = call @sparseValuesF32(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf32>
// CHECK-MIR:           %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32>
// CHECK-MIR:           %[[VAL_10:.*]] = memref.alloc() : memref<f32>
// CHECK-MIR:           memref.copy %[[VAL_9]], %[[VAL_10]] : memref<f32> to memref<f32>
// CHECK-MIR:           %[[VAL_11:.*]] = memref.load %[[VAL_10]][] : memref<f32>
// CHECK-MIR:           %[[VAL_12:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_4]] to %[[VAL_5]] step %[[VAL_3]] iter_args(%[[VAL_14:.*]] = %[[VAL_11]]) -> (f32) {
// CHECK-MIR:             %[[VAL_15:.*]] = scf.for %[[VAL_16:.*]] = %[[VAL_4]] to %[[VAL_6]] step %[[VAL_3]] iter_args(%[[VAL_17:.*]] = %[[VAL_14]]) -> (f32) {
// CHECK-MIR:               %[[VAL_18:.*]] = arith.muli %[[VAL_6]], %[[VAL_13]] : index
// CHECK-MIR:               %[[VAL_19:.*]] = arith.addi %[[VAL_18]], %[[VAL_16]] : index
// CHECK-MIR:               %[[VAL_20:.*]] = scf.for %[[VAL_21:.*]] = %[[VAL_4]] to %[[VAL_7]] step %[[VAL_3]] iter_args(%[[VAL_22:.*]] = %[[VAL_17]]) -> (f32) {
// CHECK-MIR:                 %[[VAL_23:.*]] = arith.muli %[[VAL_7]], %[[VAL_19]] : index
// CHECK-MIR:                 %[[VAL_24:.*]] = arith.addi %[[VAL_23]], %[[VAL_21]] : index
// CHECK-MIR:                 %[[VAL_25:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_24]]] : memref<?xf32>
// CHECK-MIR:                 %[[VAL_26:.*]] = arith.addf %[[VAL_22]], %[[VAL_25]] : f32
// CHECK-MIR:                 scf.yield %[[VAL_26]] : f32
// CHECK-MIR:               }
// CHECK-MIR:               scf.yield %[[VAL_20]] : f32
// CHECK-MIR:             }
// CHECK-MIR:             scf.yield %[[VAL_15]] : f32
// CHECK-MIR:           }
// CHECK-MIR:           memref.store %[[VAL_12]], %[[VAL_10]][] : memref<f32>
// CHECK-MIR:           %[[VAL_30:.*]] = bufferization.to_tensor %[[VAL_10]] : memref<f32>
// CHECK-MIR:           return %[[VAL_30]] : tensor<f32>
// CHECK-MIR:         }
func @sparse_dynamic_dims(%arga: tensor<?x?x?xf32, #X>,
                          %argx: tensor<f32>) -> tensor<f32> {
  %0 = linalg.generic #trait
    ins(%arga: tensor<?x?x?xf32, #X>)
    outs(%argx: tensor<f32>) {
      ^bb(%a : f32, %x: f32):
        %0 = arith.addf %x, %a : f32
        linalg.yield %0 : f32
  } -> tensor<f32>
  return %0 : tensor<f32>
}