// RUN: mlir-opt %s \
// RUN:   --sparsification --sparse-tensor-conversion \
// RUN:   --convert-vector-to-scf --convert-scf-to-std \
// RUN:   --func-bufferize --tensor-constant-bufferize --tensor-bufferize \
// RUN:   --std-bufferize --finalizing-bufferize  \
// RUN:   --convert-vector-to-llvm --convert-memref-to-llvm --convert-std-to-llvm | \
// RUN: TENSOR0="%mlir_integration_test_dir/data/test.tns" \
// RUN: mlir-cpu-runner \
// RUN:  -e entry -entry-point-result=void  \
// RUN:  -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s

!Filename = type !llvm.ptr<i8>

#SparseTensor = #sparse_tensor.encoding<{
  dimLevelType = [ "compressed", "compressed", "compressed", "compressed",
                   "compressed", "compressed", "compressed", "compressed" ],
  // Note that any dimOrdering permutation should give the same results
  // since, even though it impacts the sparse storage scheme layout,
  // it should not change the semantics.
  dimOrdering = affine_map<(i,j,k,l,m,n,o,p) -> (p,o,j,k,i,l,m,n)>
}>

#trait_flatten = {
  indexing_maps = [
    affine_map<(i,j,k,l,m,n,o,p) -> (i,j,k,l,m,n,o,p)>, // A
    affine_map<(i,j,k,l,m,n,o,p) -> (i,j)>              // X (out)
  ],
  iterator_types = [ "parallel",  "parallel",  "reduction", "reduction",
                     "reduction", "reduction", "reduction", "reduction" ],
  doc = "X(i,j) += A(i,j,k,l,m,n,o,p)"
}

//
// Integration test that lowers a kernel annotated as sparse to
// actual sparse code, initializes a matching sparse storage scheme
// from file, and runs the resulting code with the JIT compiler.
//
module {
  //
  // A kernel that flattens a rank 8 tensor into a dense matrix.
  //
  func @kernel_flatten(%arga: tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>,
                       %argx: tensor<7x3xf64>) -> tensor<7x3xf64> {
    %0 = linalg.generic #trait_flatten
      ins(%arga: tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>)
      outs(%argx: tensor<7x3xf64>) {
      ^bb(%a: f64, %x: f64):
        %0 = addf %x, %a : f64
        linalg.yield %0 : f64
    } -> tensor<7x3xf64>
    return %0 : tensor<7x3xf64>
  }

  func private @getTensorFilename(index) -> (!Filename)

  //
  // Main driver that reads tensor from file and calls the sparse kernel.
  //
  func @entry() {
    %d0 = constant 0.0 : f64
    %c0 = constant 0 : index
    %c1 = constant 1 : index
    %c3 = constant 3 : index
    %c7 = constant 7 : index

    // Setup matrix memory that is initialized to zero.
    %xdata = memref.alloc() : memref<7x3xf64>
    scf.for %i = %c0 to %c7 step %c1 {
      scf.for %j = %c0 to %c3 step %c1 {
        memref.store %d0, %xdata[%i, %j] : memref<7x3xf64>
      }
    }
    %x = memref.tensor_load %xdata : memref<7x3xf64>

    // Read the sparse tensor from file, construct sparse storage.
    %fileName = call @getTensorFilename(%c0) : (index) -> (!Filename)
    %a = sparse_tensor.new %fileName : !Filename to tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>

    // Call the kernel.
    %0 = call @kernel_flatten(%a, %x)
      : (tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>, tensor<7x3xf64>) -> tensor<7x3xf64>

    // Print the result for verification.
    //
    // CHECK: ( 6.25, 0, 0 )
    // CHECK: ( 4.224, 6.21, 0 )
    // CHECK: ( 0, 0, 15.455 )
    // CHECK: ( 0, 0, 0 )
    // CHECK: ( 0, 0, 0 )
    // CHECK: ( 0, 0, 0 )
    // CHECK: ( 7, 0, 0 )
    //
    %r = memref.buffer_cast %0 : memref<7x3xf64>
    scf.for %i = %c0 to %c7 step %c1 {
      %v = vector.transfer_read %r[%i, %c0], %d0: memref<7x3xf64>, vector<3xf64>
      vector.print %v : vector<3xf64>
    }

    // Release the resources.
    memref.dealloc %xdata : memref<7x3xf64>

    return
  }
}