// RUN: torch-mlir-opt <%s -convert-torch-to-linalg -split-input-file -verify-diagnostics | FileCheck %s

// CHECK-LABEL: func.func @test_resize_sizes_linear
func.func @test_resize_sizes_linear(%arg0: !torch.vtensor<[1,1,2,4],f32>, %arg1: !torch.vtensor<[4]
,si64>) -> !torch.vtensor<[?,?,?,?],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 19 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
     // CHECK: %[[generic:.*]] = linalg.generic
     // CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x1:.*]], %[[x2:.*]], %[[x3:.*]], %[[x4:.*]]] : tensor<1x1x2x4xf32>
     // CHECK: %[[extracted_7:.*]] = tensor.extract %[[x0]][%[[x1]], %[[x2]]
     // CHECK: %[[extracted_8:.*]] = tensor.extract %[[x0]][%[[x1]], %[[x2]]
     // CHECK: %[[extracted_9:.*]] = tensor.extract %[[x0]][%[[x1]], %[[x2]]
     // CHECK: %[[dx0p00:.*]] = arith.mulf %[[dx0:.*]], %[[extracted]]
     // CHECK: %[[dx1p01:.*]] = arith.mulf %[[dx1:.*]], %[[extracted_7]]
     // CHECK: %[[sum:.*]] = arith.addf %[[dx0p00]], %[[dx1p01]]
     // CHECK: %[[left:.*]] = arith.mulf %[[dy0:.*]], %[[sum]]
     // CHECK: %[[dx0p10:.*]] = arith.mulf %[[dx0]], %[[extracted_8]]
     // CHECK: %[[dx1p11:.*]] = arith.mulf %[[dx1]], %[[extracted_9]]
     // CHECK: %[[sum2:.*]] = arith.addf %[[dx0p10]], %[[dx1p11]]
     // CHECK: %[[right:.*]] = arith.mulf %[[dy1:.*]], %[[sum2]]
     // CHECK: %[[retval:.*]] = arith.addf %[[left]], %[[right]]
    %none = torch.constant.none
    %none_0 = torch.constant.none
    %int0 = torch.constant.int 0
    %false = torch.constant.bool false
    %true = torch.constant.bool true
    %str = torch.constant.str "bilinear"
    %int2 = torch.constant.int 2
    %0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
    %1 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
    %int3 = torch.constant.int 3
    %2 = torch.aten.select.int %arg1, %int0, %int3 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
    %3 = torch.aten.item %2 : !torch.vtensor<[1],si64> -> !torch.int
    %4 = torch.prim.ListConstruct %1, %3 : (!torch.int, !torch.int) -> !torch.list<int>
    %5 = torch.aten.__interpolate.size_list_scale_list %arg0, %4, %none_0, %str, %false, %none_0, %false : !torch.vtensor<[1,1,2,4],f32>, !torch.list<int>, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
    return %5 : !torch.vtensor<[?,?,?,?],f32>
  }

// -----

func.func @test_resize_sizes_nearest(%arg0: !torch.vtensor<[1,1,2,4],f32>, %arg1: !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,?,?,?],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 19 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
    // CHECK: %[[GENERIC:.*]] = linalg.generic
    // CHECK: %[[x11:.*]] = linalg.index 0 : index
    // CHECK: %[[x12:.*]] = linalg.index 1 : index
    // CHECK: %[[x13:.*]] = linalg.index 2 : index
    // CHECK: %[[x14:.*]] = linalg.index 3 : index
    // CHECK: %[[x15:.*]] = arith.sitofp %[[c2_i64:.*]] : i64 to f32
    // CHECK: %[[x19:.*]] = arith.sitofp %[[x6:.*]] : i64 to f32
    // CHECK: %[[x21:.*]] = arith.divf %[[x19]], %[[x15]] : f32
    // CHECK: %[[x23:.*]] = arith.index_cast %[[x13]] : index to i64
    // CHECK: %[[x24:.*]] = arith.sitofp %[[x23]] : i64 to f32
    // CHECK: %[[x25:.*]] = arith.divf %[[x24]], %[[x21]] : f32
    // CHECK: %[[x29:.*]] = math.floor %[[x25]] : f32
    // CHECK: %[[x31:.*]] = arith.fptosi %[[x29]] : f32 to i64
    // CHECK: %[[x32:.*]] = arith.index_cast %[[x31]] : i64 to index
    // CHECK: %[[x16:.*]] = arith.sitofp %[[c4_i64:.*]] : i64 to f32
    // CHECK: %[[x20:.*]] = arith.sitofp %[[x7:.*]] : i64 to f32
    // CHECK: %[[x22:.*]] = arith.divf %[[x20]], %[[x16]] : f32
    // CHECK: %[[x26:.*]] = arith.index_cast %[[x14]] : index to i64
    // CHECK: %[[x27:.*]] = arith.sitofp %[[x26]] : i64 to f32
    // CHECK: %[[x28:.*]] = arith.divf %[[x27]], %[[x22]] : f32
    // CHECK: %[[x30:.*]] = math.floor %[[x28]] : f32
    // CHECK: %[[x33:.*]] = arith.fptosi %[[x30]] : f32 to i64
    // CHECK: %[[x34:.*]] = arith.index_cast %[[x33]] : i64 to index
    // CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x11]], %[[x12]], %[[x32]], %[[x34]]] : tensor<1x1x2x4xf32>
    // CHECK: linalg.yield %[[extracted]] : f32
    %none = torch.constant.none
    %none_0 = torch.constant.none
    %int0 = torch.constant.int 0
    %false = torch.constant.bool false
    %true = torch.constant.bool true
    %str = torch.constant.str "nearest"
    %int2 = torch.constant.int 2
    %0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
    %1 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
    %int3 = torch.constant.int 3
    %2 = torch.aten.select.int %arg1, %int0, %int3 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
    %3 = torch.aten.item %2 : !torch.vtensor<[1],si64> -> !torch.int
    %4 = torch.prim.ListConstruct %1, %3 : (!torch.int, !torch.int) -> !torch.list<int>
    %5 = torch.aten.__interpolate.size_list_scale_list %arg0, %4, %none_0, %str, %false, %none_0, %false : !torch.vtensor<[1,1,2,4],f32>, !torch.list<int>, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
    return %5 : !torch.vtensor<[?,?,?,?],f32>
  }

// -----

func.func @test_resize_nearest_1d(%arg0: !torch.vtensor<[?,?,?],f32>, %arg1: !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,?,?],f32> {
    // CHECK: %[[GENERIC:.*]] = linalg.generic
    // CHECK: %[[x11:.*]] = linalg.index 0 : index
    // CHECK: %[[x12:.*]] = linalg.index 1 : index
    // CHECK: %[[x13:.*]] = linalg.index 2 : index
    // CHECK: %[[x15:.*]] = arith.sitofp %[[c2_i64:.*]] : i64 to f32
    // CHECK: %[[x19:.*]] = arith.sitofp %[[x6:.*]] : i64 to f32
    // CHECK: %[[x21:.*]] = arith.divf %[[x19]], %[[x15]] : f32
    // CHECK: %[[x23:.*]] = arith.index_cast %[[x13]] : index to i64
    // CHECK: %[[x24:.*]] = arith.sitofp %[[x23]] : i64 to f32
    // CHECK: %[[x25:.*]] = arith.divf %[[x24]], %[[x21]] : f32
    // CHECK: %[[x29:.*]] = math.floor %[[x25]] : f32
    // CHECK: %[[x31:.*]] = arith.fptosi %[[x29]] : f32 to i64
    // CHECK: %[[x32:.*]] = arith.index_cast %[[x31]] : i64 to index
    // CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x11]], %[[x12]], %[[x32]]] : tensor<?x?x?xf32>
    // CHECK: linalg.yield %[[extracted]] : f32
    %none = torch.constant.none
    %none_0 = torch.constant.none
    %int0 = torch.constant.int 0
    %false = torch.constant.bool false
    %true = torch.constant.bool true
    %str = torch.constant.str "nearest"
    %int2 = torch.constant.int 2
    %0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[3],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
    %1 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
    %4 = torch.prim.ListConstruct %1 : (!torch.int) -> !torch.list<int>
    %5 = torch.aten.__interpolate.size_list_scale_list %arg0, %4, %none_0, %str, %false, %none_0, %false : !torch.vtensor<[?,?,?],f32>, !torch.list<int>, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?],f32>
    return %5 : !torch.vtensor<[?,?,?],f32>
}

// -----

func.func @test_resize_nearest_3d(%arg0: !torch.vtensor<[?,?,?,?,?],f32>, %arg1: !torch.vtensor<[5],si64>) -> !torch.vtensor<[?,?,?,?,?],f32> {
    // CHECK: %[[GENERIC:.*]] = linalg.generic
    // CHECK: %[[x11:.*]] = linalg.index 0 : index
    // CHECK: %[[x12:.*]] = linalg.index 1 : index
    // CHECK: %[[x13:.*]] = linalg.index 2 : index
    // CHECK: %[[x14:.*]] = linalg.index 3 : index
    // CHECK: %[[index4:.*]] = linalg.index 4 : index
    // CHECK: %[[x15:.*]] = arith.sitofp %[[c2_i64:.*]] : i64 to f32
    // CHECK: %[[x19:.*]] = arith.sitofp %[[x6:.*]] : i64 to f32
    // CHECK: %[[x21:.*]] = arith.divf %[[x19]], %[[x15]] : f32
    // CHECK: %[[x23:.*]] = arith.index_cast %[[x13]] : index to i64
    // CHECK: %[[x24:.*]] = arith.sitofp %[[x23]] : i64 to f32
    // CHECK: %[[x25:.*]] = arith.divf %[[x24]], %[[x21]] : f32
    // CHECK: %[[x29:.*]] = math.floor %[[x25]] : f32
    // CHECK: %[[x31:.*]] = arith.fptosi %[[x29]] : f32 to i64
    // CHECK: %[[x32:.*]] = arith.index_cast %[[x31]] : i64 to index
    // CHECK: %[[x34:.*]] = arith.index_cast %[[Wfptosi:.*]] : i64 to index
    // CHECK: %[[x35:.*]] = arith.index_cast %[[Dfptosi:.*]] : i64 to index
    // CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x11]], %[[x12]], %[[x32]], %[[x34]], %[[x35]]] : tensor<?x?x?x?x?xf32>
    // CHECK: linalg.yield %[[extracted]] : f32
    %none = torch.constant.none
    %none_0 = torch.constant.none
    %int0 = torch.constant.int 0
    %false = torch.constant.bool false
    %true = torch.constant.bool true
    %str = torch.constant.str "nearest"
    %int2 = torch.constant.int 2
    %0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[5],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
    %1 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
    %int3 = torch.constant.int 3
    %2 = torch.aten.select.int %arg1, %int0, %int3 : !torch.vtensor<[5],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
    %3 = torch.aten.item %2 : !torch.vtensor<[1],si64> -> !torch.int
    %int4 = torch.constant.int 4
    %4 = torch.aten.select.int %arg1, %int0, %int4 : !torch.vtensor<[5],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
    %5 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
    %6 = torch.prim.ListConstruct %1, %3, %5: (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
    %7 = torch.aten.__interpolate.size_list_scale_list %arg0, %6, %none_0, %str, %false, %none_0, %false : !torch.vtensor<[?,?,?,?,?],f32>, !torch.list<int>, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?,?,?],f32>
    return %7 : !torch.vtensor<[?,?,?,?,?],f32>
  }