#include <gtest/gtest.h>

#include <ATen/ATen.h>
#include <ATen/BatchedTensorImpl.h>
#include <ATen/VmapTransforms.h>

using namespace at;

namespace {

TEST(VmapTest, TestBatchedTensor) {
  {
    Tensor x = addBatchDim(ones({2, 3, 4}), /*lvl=*/1, /*dim=*/1);
    std::vector<int64_t> expected_size = {2, 4};
    ASSERT_EQ(x.sizes(), expected_size);
    ASSERT_EQ(x.dim(), 2);
    ASSERT_EQ(x.numel(), 8);
    ASSERT_EQ(x.is_contiguous(), false);
    ASSERT_THROW(x.storage(), c10::Error);
    ASSERT_EQ(x.storage_offset(), 0);
  }
  {
    // Test multiple batch dims
    Tensor x = addBatchDim(ones({2, 3, 4}), /*lvl=*/1, /*dim=*/1);
    x = addBatchDim(x, /*lvl=*/2, /*dim=*/1);
    std::vector<int64_t> expected_size = {2};
    ASSERT_EQ(x.sizes(), expected_size);
    ASSERT_EQ(x.dim(), 1);
    ASSERT_EQ(x.numel(), 2);
  }
  {
    // Test vmap tensor dimensionality limit

    // Should not throw
    std::vector<int64_t> sizes(kVmapMaxTensorDims, 1);
    Tensor x = addBatchDim(ones(sizes), /*lvl=*/1, /*dim=*/1);

    // Should throw
    std::vector<int64_t> too_many_sizes(kVmapMaxTensorDims + 1, 1);
    auto big_dim_tensor = ones(too_many_sizes);
    ASSERT_THROW(addBatchDim(big_dim_tensor, /*lvl=*/1, /*dim=*/1), c10::Error);
  }
  {
    // Create a "scalar" BatchedTensor. Should not crash.
    Tensor tensor = addBatchDim(ones({3}), /*lvl*/1, /*dim*/0);
  }
}

// returns {{lvl=0,dim=0}, {lvl=1,dim=1}, ..., {lvl=kVmapNumLevels-1,dim=kVmapNumLevels-1}};
static BatchDims maxBatchDimsAtFront() {
  BatchDims result;
  for (int64_t lvl = 0; lvl < kVmapNumLevels; lvl++) {
    result.emplace_back(lvl, /*dim=*/lvl);
  }
  return result;
}

TEST(VmapTest, TestBatchedTensorMaxLevel) {
  {
    // Should not throw
    auto tensor = ones({2, 3, 4});
    makeBatched(ones({2, 3, 4}), {{/*lvl*/kVmapNumLevels - 1, /*dim*/0}});
  }
  {
    auto tensor = ones({2, 3, 4});
    ASSERT_THROW(
        makeBatched(ones({2, 3, 4}), {{/*lvl*/kVmapNumLevels, /*dim*/0}}),
        c10::Error);
  }
  {
    auto tensor = ones({2, 3, 4});
    ASSERT_THROW(
        makeBatched(ones({2, 3, 4}), {{/*lvl*/kVmapNumLevels + 5, /*dim*/0}}),
        c10::Error);
  }
  {
    // create a BatchedTensor with kVmapNumLevels levels.
    // Should not throw
    auto tensor = ones(std::vector<int64_t>(kVmapNumLevels, 1));
    makeBatched(tensor, maxBatchDimsAtFront());
  }
  {
    // create a BatchedTensor with kVmapNumLevels+1 levels.
    auto tensor = ones(std::vector<int64_t>(kVmapNumLevels + 1, 1));
    auto batch_dims = maxBatchDimsAtFront();
    batch_dims.emplace_back(/*lvl*/kVmapNumLevels, /*dim*/kVmapNumLevels);
    ASSERT_THROW(makeBatched(tensor, batch_dims), c10::Error);
  }
}

TEST(VmapTest, TestBatchedTensorActualDim) {
  {
    // No batch dims
    Tensor tensor = makeBatched(ones({2, 3, 5, 7}), {});
    auto* batched = maybeGetBatchedImpl(tensor);
    ASSERT_EQ(batched->actualDim(0), 0);
    ASSERT_EQ(batched->actualDim(1), 1);
    ASSERT_EQ(batched->actualDim(3), 3);

    // Test wrap around
    ASSERT_EQ(batched->actualDim(-1), 3);
    ASSERT_EQ(batched->actualDim(-4), 0);
    ASSERT_THROW(batched->actualDim(-5), c10::Error);
    ASSERT_THROW(batched->actualDim(4), c10::Error);

    // test wrap_dim = False
    ASSERT_THROW(batched->actualDim(-1, /*wrap_dim*/false), c10::Error);
    ASSERT_THROW(batched->actualDim(-4, /*wrap_dim*/false), c10::Error);
  }
  {
    // Single batch dim at front
    Tensor tensor = makeBatched(ones({2, 3, 5, 7}), {{/*lvl*/1, /*dim*/0}});
    auto* batched = maybeGetBatchedImpl(tensor);
    ASSERT_EQ(batched->actualDim(0), 1);
    ASSERT_EQ(batched->actualDim(2), 3);
    ASSERT_EQ(batched->actualDim(-1), 3);
    ASSERT_THROW(batched->actualDim(3), c10::Error);
  }
  {
    // Single batch dim in middle
    Tensor tensor = makeBatched(ones({2, 3, 5, 7}), {{/*lvl*/1, /*dim*/1}});
    auto* batched = maybeGetBatchedImpl(tensor);
    ASSERT_EQ(batched->actualDim(0), 0);
    ASSERT_EQ(batched->actualDim(1), 2);
    ASSERT_EQ(batched->actualDim(2), 3);
  }
  {
    // Single batch dim at end
    Tensor tensor = makeBatched(ones({2, 3, 5, 7}), {{/*lvl*/1, /*dim*/1}});
    auto* batched = maybeGetBatchedImpl(tensor);
    ASSERT_EQ(batched->actualDim(0), 0);
    ASSERT_EQ(batched->actualDim(2), 3);
    ASSERT_EQ(batched->actualDim(-1), 3);
  }
  {
    // Multiple (2) batch dims at front
    Tensor tensor = makeBatched(
        ones({2, 3, 5, 7}),
        {{/*lvl*/1, /*dim*/0}, {/*lvl*/2, /*dim*/1}});
    auto* batched = maybeGetBatchedImpl(tensor);
    ASSERT_EQ(batched->actualDim(0), 2);
    ASSERT_EQ(batched->actualDim(1), 3);
  }
  {
    // Multiple (2) batch dims, misc places
    Tensor tensor = makeBatched(
        ones({2, 3, 5, 7}),
        {{/*lvl*/1, /*dim*/1}, {/*lvl*/2, /*dim*/3}});
    auto* batched = maybeGetBatchedImpl(tensor);
    ASSERT_EQ(batched->actualDim(0), 0);
    ASSERT_EQ(batched->actualDim(1), 2);
    ASSERT_EQ(batched->actualDim(-1), 2);
    ASSERT_EQ(batched->actualDim(-2), 0);
  }
  {
    // ActualDim on kVmapMaxTensorDims sized underlying tensor
    auto tensor = ones({});
    for (int64_t i = 0; i < kVmapMaxTensorDims; i++) {
      tensor = tensor.unsqueeze(0);
    }
    ASSERT_EQ(tensor.dim(), kVmapMaxTensorDims);

    auto batched = addBatchDim(tensor, /*lvl*/1, /*dim*/0);
    auto* batched_impl = maybeGetBatchedImpl(batched);
    ASSERT_EQ(
        batched_impl->actualDim(kVmapMaxTensorDims - 2),
        kVmapMaxTensorDims - 1);
    ASSERT_EQ(
        batched_impl->actualDim(-1),
        kVmapMaxTensorDims - 1);
  }
}
TEST(VmapTest, TestMultiBatchVmapTransform) {
  {
    // Input is regular Tensor
    auto tensor = ones({2, 3, 5});
    ASSERT_THROW(MultiBatchVmapTransform::logicalToPhysical(tensor), c10::Error);
  }
  {
    // Input is BatchedTensor, Batch dims are already at the front
    auto tensor = ones({2, 3, 5});
    BatchDims bdims = {{/*lvl*/1, /*dim*/0}, {/*lvl*/3, /*dim*/1}};
    auto batched = makeBatched(tensor, bdims);

    auto result = MultiBatchVmapTransform::logicalToPhysical(batched);
    ASSERT_TRUE(result.tensor().is_same(tensor));
  }
  {
    // Single batch dim, not at front
    auto tensor = ones({2, 3, 5});
    BatchDims bdims = {{/*lvl*/1, /*dim*/1}};
    auto batched = makeBatched(tensor, bdims);

    auto result = MultiBatchVmapTransform::logicalToPhysical(batched);
    ASSERT_EQ(result.tensor().data_ptr(), tensor.data_ptr());
    ASSERT_TRUE(at::allclose(result.tensor(), tensor.permute({1, 0, 2})));
  }
  {
    // Multiple batch dims, not at front.
    auto tensor = ones({2, 3, 5});
    BatchDims bdims = {{/*lvl*/1, /*dim*/1}, {/*lvl*/2,/*dim*/2}, {/*lvl*/3,/*dim*/0}};
    auto batched = makeBatched(tensor, bdims);

    auto result = MultiBatchVmapTransform::logicalToPhysical(batched);
    ASSERT_EQ(result.tensor().data_ptr(), tensor.data_ptr());
    ASSERT_TRUE(at::allclose(result.tensor(), tensor.permute({1, 2, 0})));
  }
  {
    // Edge case: kVmapNumLevels levels; batch dims are already at front.

    // sizes=[2, 1, 3, 1, 1, 7, 1, 1, 1, 1, ...]
    auto sizes = std::vector<int64_t>(kVmapNumLevels, 1);
    sizes[0] = 2;
    sizes[2] = 3;
    sizes[5] = 7;

    // bdims = {{lvl=0,dim=0,lvl=1,dim=1,...,{lvl=63,dim=63}}
    auto batch_dims = maxBatchDimsAtFront();
    auto tensor = ones(sizes);

    auto batched = makeBatched(tensor, batch_dims);
    auto result = MultiBatchVmapTransform::logicalToPhysical(batched);
    ASSERT_TRUE(result.tensor().is_same(tensor));
  }
  {
    // Edge case: kVmapNumLevels levels; batch dims are not at front

    // sizes=[1, 3, 2, 1, 1, 7, 1, 1, 1, 1, ..., 1, 1, 5]
    auto sizes = std::vector<int64_t>(kVmapNumLevels, 1);
    sizes[1] = 3;
    sizes[2] = 2;
    sizes[5] = 7;
    sizes[kVmapNumLevels - 1] = 5;

    // The goal is to permute sizes such that the final sizes are:
    // [2, 3, 5, 7, 1, 1, 1, 1, 1, ...]
    auto expected_result_sizes = std::vector<int64_t>(kVmapNumLevels, 1);
    expected_result_sizes[0] = 2;
    expected_result_sizes[1] = 3;
    expected_result_sizes[2] = 5;
    expected_result_sizes[3] = 7;

    // bdims = {{0, 2}, {1, 1}, {2, 63}, {3, 5}, {4, 0}, {5, 3}, {6, 4},
    //          {7, 6}, {8, 7}, {9, 8}, ..., {63, 62}}
    BatchDims batch_dims = {
      {0, 2}, {1, 1}, {2, kVmapNumLevels - 1}, {3, 5}, {4, 0}, {5, 3}, {6, 4}
    };
    for (int64_t level = 7; level < kVmapNumLevels; level++ ) {
      batch_dims.emplace_back(level, /*dim=*/level - 1);
    }
    auto tensor = ones(sizes);

    auto batched = makeBatched(tensor, batch_dims);
    auto result = MultiBatchVmapTransform::logicalToPhysical(batched);
    ASSERT_EQ(result.tensor().data_ptr(), tensor.data_ptr());
    ASSERT_EQ(result.tensor().sizes(), expected_result_sizes);
  }
}
TEST(VmapTest, TestVmapPhysicalViewGetPhysicalDim) {
  VmapPhysicalView physical_view(ones({2, 3, 4, 5, 6}), 1 | 4);

  // Positive dims
  ASSERT_EQ(physical_view.getPhysicalDim(0), 2);
  ASSERT_EQ(physical_view.getPhysicalDim(1), 3);
  ASSERT_EQ(physical_view.getPhysicalDim(2), 4);
  ASSERT_THROW(physical_view.getPhysicalDim(3), c10::Error);

  // Negative dims (testing wrap dim behavior)
  ASSERT_EQ(physical_view.getPhysicalDim(-1), 4);
  ASSERT_EQ(physical_view.getPhysicalDim(-2), 3);
  ASSERT_EQ(physical_view.getPhysicalDim(-3), 2);
  ASSERT_THROW(physical_view.getPhysicalDim(-4), c10::Error);
}
TEST(VmapTest, TestVmapPhysicalViewGetPhysicalDims) {
  VmapPhysicalView physical_view(ones({2, 3, 4, 5, 6}), 2 | 8 | 16);

  ASSERT_EQ(
      physical_view.getPhysicalDims({0, 1, -1, -2}),
      VmapDimVector({3, 4, 4, 3}));

  ASSERT_THROW(physical_view.getPhysicalDims({2, 0}), c10::Error);
  ASSERT_THROW(physical_view.getPhysicalDims({0, -3}), c10::Error);
}

static void checkBatchDimsEqual(BatchDimsRef bdims, BatchDimsRef expected_bdims) {
  ASSERT_EQ(bdims.size(), expected_bdims.size());
  for (int64_t idx = 0; idx < bdims.size(); idx++) {
    ASSERT_EQ(bdims[idx].dim(), expected_bdims[idx].dim());
    ASSERT_EQ(bdims[idx].level(), expected_bdims[idx].level());
  }
}

TEST(VmapTest, TestVmapPhysicalViewNewLogicalFromPhysical) {
  {
    // Simple case: single level
    VmapPhysicalView physical_view(ones({2, 3, 4}), /*levels = {2}*/4);
    Tensor physical = ones({2, 6, 7});

    auto result = physical_view.getPhysicalToLogicalMap().apply(physical);
    auto* batched = maybeGetBatchedImpl(result);
    ASSERT_TRUE(batched != nullptr);
    ASSERT_TRUE(batched->value().is_same(physical));
    checkBatchDimsEqual(batched->bdims(), {{2, 0}});
  }
  {
    // Multiple levels
    VmapPhysicalView physical_view(ones({2, 3, 4, 5, 6}), /*levels = {1, 3, 4}*/2 | 8 | 16);
    Tensor physical = ones({2, 3, 4, 7});

    auto result = physical_view.getPhysicalToLogicalMap().apply(physical);
    auto* batched = maybeGetBatchedImpl(result);
    ASSERT_TRUE(batched != nullptr);
    ASSERT_TRUE(batched->value().is_same(physical));
    checkBatchDimsEqual(batched->bdims(), {{1, 0}, {3, 1}, {4, 2}});
  }
  {
    // Logical dimensions is [].
    VmapPhysicalView physical_view(ones({2}), /*levels = {2}*/4);
    Tensor physical = ones({2});

    auto result = physical_view.getPhysicalToLogicalMap().apply(physical);
    auto* batched = maybeGetBatchedImpl(result);
    ASSERT_TRUE(batched != nullptr);
    ASSERT_TRUE(batched->value().is_same(physical));
    checkBatchDimsEqual(batched->bdims(), {{2, 0}});
  }
}

// Basic test for BatchedTensor::sum.
// NB: We don't need to write tests in C++ for batching rules if we can test them
// in Python via the vmap API. These are here to bootstrap that process.
TEST(VmapTest, TestBatchedTensorSum) {
  {
    // Simple: single batch dim, single reduce dim
    Tensor x = at::randn({2, 3, 5, 7});

    Tensor batched_x = makeBatched(x, {{/*lvl*/1, /*dim*/0}});
    Tensor batched_out = batched_x.sum(0);
    const auto& out = maybeGetBatchedImpl(batched_out)->value();

    ASSERT_TRUE(at::allclose(out, x.sum(1)));
  }
  {
    // single batch dim, -1 reduce dim handling
    Tensor x = at::randn({2, 3});

    Tensor batched_x = makeBatched(x, {{/*lvl*/1, /*dim*/1}});
    Tensor batched_out = batched_x.sum(-1);
    const auto& out = maybeGetBatchedImpl(batched_out)->value();

    ASSERT_TRUE(at::allclose(out, x.sum(0)));
  }
  {
    // single batch dim, multiple reduce dim
    Tensor x = at::randn({2, 3, 5, 7});

    Tensor batched_x = makeBatched(x, {{/*lvl*/1, /*dim*/1}});
    Tensor batched_out = batched_x.sum(std::vector<int64_t>{0, 1});
    const auto& out = maybeGetBatchedImpl(batched_out)->value();

    ASSERT_TRUE(at::allclose(out, x.sum(std::vector<int64_t>{0, 2})));
  }
  {
    // multiple batch dim, multiple reduce dim
    Tensor x = at::randn({2, 3, 5, 7});

    Tensor batched_x = makeBatched(x, {{/*lvl*/1, /*dim*/0}, {/*lvl*/2, /*dim*/1}});
    Tensor batched_out = batched_x.sum(std::vector<int64_t>{0, 1});
    const auto& out = maybeGetBatchedImpl(batched_out)->value();

    ASSERT_TRUE(at::allclose(out, x.sum(std::vector<int64_t>{2, 3})));
  }
}

static void checkBroadcastingVmapTransform(TensorList inputs, TensorList expected_outputs) {
  auto outputs = BroadcastingVmapTransform::logicalToPhysical(inputs);
  ASSERT_EQ(outputs.size(), expected_outputs.size());
  for (int64_t idx = 0; idx < outputs.size(); idx++) {
    const auto& output = outputs[idx].tensor();
    ASSERT_EQ(output.data_ptr(), expected_outputs[idx].data_ptr());
    ASSERT_TRUE(at::allclose(output, expected_outputs[idx]));
  }
}

TEST(VmapTest, TestBroadcastingVmapTransformBatchedBatched) {
  {
    // Check that batch dims get moved to the front
    int64_t B0 = 5, B1 = 7;
    Tensor x = at::randn({2, B0, 3, B1});
    Tensor y = at::randn({B1, 2, 3, B0});
    Tensor batched_x = makeBatched(x, {{0, 1}, {1, 3}});
    Tensor batched_y = makeBatched(y, {{0, 3}, {1, 0}});

    checkBroadcastingVmapTransform(
        {batched_x, batched_y},
        {x.permute({1, 3, 0, 2}), y.permute({3, 0, 1, 2})});
  }
  {
    // Check that batch dims become aligned (i.e. extra 1 dims get added)
    int64_t B0 = 5, B1 = 7, B2 = 9;
    Tensor x = at::randn({B0, B2, 2, 3});
    Tensor y = at::randn({B0, B1, 2, 3});
    Tensor batched_x = makeBatched(x, {{0, 0}, {2, 1}});
    Tensor batched_y = makeBatched(y, {{0, 0}, {1, 1}});

    checkBroadcastingVmapTransform(
        {batched_x, batched_y},
        {x.unsqueeze(1), y.unsqueeze(2)});
  }
  {
    // Check that the "example" gets padded with extra dims of size 1.
    int64_t B0 = 5;
    Tensor x = at::randn({B0, 3});
    Tensor y = at::randn({B0, 2, 3});
    Tensor batched_x = makeBatched(x, {{0, 0}});
    Tensor batched_y = makeBatched(y, {{0, 0}});

    checkBroadcastingVmapTransform(
        {batched_x, batched_y},
        {x.unsqueeze(1), y});
  }
  {
    // Check batch dims get moved to front, batch dims get aligned,
    // and the example gets padded correctly.
    int64_t B0 = 5, B1 = 7, B2 = 11, B3 = 13;
    Tensor x = at::randn({2, B0, 3, B2});
    Tensor y = at::randn({B3, 3, B1});
    Tensor batched_x = makeBatched(x, {{0, 1}, {2, 3}});
    Tensor batched_y = makeBatched(y, {{1, 2}, {3, 0}});

    checkBroadcastingVmapTransform(
        {batched_x, batched_y},
        {
          x.permute({1, 3, 0, 2}).view({B0, 1, B2, 1, 2, 3}),
          y.permute({2, 0, 1}).view({1, B1, 1, B3, 1, 3}),
        });
  }
  {
    // Edge case: BatchedTensor "scalar" handling
    int64_t B0 = 5, B2 = 11;
    Tensor x = at::randn({B0});
    Tensor y = at::randn({B0, B2});
    Tensor batched_x = makeBatched(x, {{0, 0}});
    Tensor batched_y = makeBatched(y, {{0, 0}, {1, 1}});

    checkBroadcastingVmapTransform({batched_x, batched_y}, {x.view({B0, 1}), y});
    checkBroadcastingVmapTransform({batched_y, batched_x}, {y, x.view({B0, 1})});
  }
  {
    // Edge case: Only one tensor is a "batchedtensor scalar"
    int64_t B0 = 5, B2 = 11;
    Tensor x = at::randn({B0});
    Tensor y = at::randn({B0, B2, 2});
    Tensor batched_x = makeBatched(x, {{0, 0}});
    Tensor batched_y = makeBatched(y, {{0, 0}, {1, 1}});

    checkBroadcastingVmapTransform({batched_x, batched_y}, {x.view({B0, 1, 1}), y});
    checkBroadcastingVmapTransform({batched_y, batched_x}, {y, x.view({B0, 1, 1})});
  }
}

TEST(VmapTest, TestBroadcastingVmapTransformBatchedUnbatched) {
  {
    // Check same example size
    int64_t B0 = 5, B1 = 7;
    Tensor x = at::randn({2, B0, 3, B1});
    Tensor y = at::randn({2, 3});
    Tensor batched_x = makeBatched(x, {{0, 1}, {1, 3}});

    checkBroadcastingVmapTransform(
        {batched_x, y},
        {x.permute({1, 3, 0, 2}), y.view({1, 1, 2, 3})});
    checkBroadcastingVmapTransform(
        {y, batched_x},
        {y.view({1, 1, 2, 3}), x.permute({1, 3, 0, 2})});
  }
  {
    // BatchedTensor has higher example dim than non-batched-tensor
    int64_t B0 = 5, B1 = 7;
    Tensor x = at::randn({B0, B1, 2, 3});
    Tensor y = at::randn({3});
    Tensor batched_x = makeBatched(x, {{0, 0}, {1, 1}});

    checkBroadcastingVmapTransform(
        {batched_x, y}, {x, y.view({1, 1, 1, 3})});
    checkBroadcastingVmapTransform(
        {y, batched_x}, {y.view({1, 1, 1, 3}), x});
  }
  {
    // BatchedTensor has lower example dim than non-batched-tensor
    int64_t B0 = 5, B1 = 7;
    Tensor x = at::randn({B0, B1, 3});
    Tensor y = at::randn({2, 3});
    Tensor batched_x = makeBatched(x, {{0, 0}, {1, 1}});

    checkBroadcastingVmapTransform(
        {batched_x, y}, {x.view({B0, B1, 1, 3}), y.view({1, 1, 2, 3})});
    checkBroadcastingVmapTransform(
        {y, batched_x}, {y.view({1, 1, 2, 3}), x.view({B0, B1, 1, 3})});
  }
  {
    // Scalar handling
    int64_t B0 = 5, B1 = 7;
    Tensor x = at::randn({B0, B1});
    Tensor y = at::randn({});
    Tensor batched_x = makeBatched(x, {{0, 0}, {1, 1}});

    checkBroadcastingVmapTransform({batched_x, y}, {x, y.view({1, 1})});
    checkBroadcastingVmapTransform({y, batched_x}, {y.view({1, 1}), x});
  }
}

TEST(VmapTest, TestBroadcastingVmapTransformMaxLevels) {
  {
    // inputs have all 64 levels
    auto x = randn(std::vector<int64_t>(kVmapNumLevels, 1));
    auto y = randn(std::vector<int64_t>(kVmapNumLevels, 1));
    auto batched_x = makeBatched(x, maxBatchDimsAtFront());
    auto batched_y = makeBatched(y, maxBatchDimsAtFront());

    checkBroadcastingVmapTransform({batched_x, batched_y}, {x, y});
  }
  {
    // inputs don't have all 64 levels, but results do.
    int64_t split = 19;
    auto x = randn(std::vector<int64_t>(split, 1));
    auto y = randn(std::vector<int64_t>(kVmapNumLevels - split, 1));

    auto tmp = maxBatchDimsAtFront();
    BatchDims x_bdims(tmp.begin(), tmp.begin() + split);

    // Construct y_bdims.
    int64_t dim = 0;
    auto y_bdims_vector = fmap(
        ArrayRef<BatchDim>(tmp.begin() + split, tmp.end()),
        [&](const BatchDim& bdim) -> BatchDim {
          return { bdim.level(), dim++ };
        });
    BatchDims y_bdims(y_bdims_vector.begin(), y_bdims_vector.end());

    auto batched_x = makeBatched(x, x_bdims);
    auto batched_y = makeBatched(y, y_bdims);

    auto expected_size = std::vector<int64_t>(kVmapNumLevels, 1);
    checkBroadcastingVmapTransform(
        {batched_x, batched_y},
        {x.view(expected_size), y.view(expected_size)});
  }
}

// Basic test for BatchedTensor::mul.
TEST(VmapTest, TestBatchedTensorMul) {
  {
    // batched * batched
    Tensor x = at::randn({2, 3});
    Tensor y = at::randn({2, 3});

    Tensor Bx = addBatchDim(x, /*lvl*/1, /*dim*/0);
    Tensor By = addBatchDim(y, /*lvl*/1, /*dim*/0);
    Tensor Bout = Bx * By;

    const auto& out = maybeGetBatchedImpl(Bout)->value();
    std::vector<int64_t> expected_size = {2, 3};
    ASSERT_EQ(out.sizes(), expected_size);
    ASSERT_TRUE(at::allclose(out, x * y));
  }
  {
    // batched * unbatched
    Tensor x = at::randn({2, 3});
    Tensor y = at::randn({3});

    Tensor Bx = addBatchDim(x, /*lvl*/1, /*dim*/0);
    Tensor Bout = Bx * y;
    const auto& out = maybeGetBatchedImpl(Bout)->value();
    std::vector<int64_t> expected_size = {2, 3};
    ASSERT_EQ(out.sizes(), expected_size);
    ASSERT_TRUE(at::allclose(out, x * y));
  }
  {
    // batched (level 1) * batched (level 2)
    Tensor x = at::randn({2, 3});
    Tensor y = at::randn({5, 3});

    Tensor Bx = addBatchDim(x, /*lvl*/1, /*dim*/0);
    Tensor By = addBatchDim(y, /*lvl*/2, /*dim*/0);
    Tensor Bout = Bx * By;

    // We get a doubly wrapped BatchTensor...
    const auto& out = maybeGetBatchedImpl(Bout)->value();
    std::vector<int64_t> expected_size = {2, 5, 3};
    ASSERT_EQ(out.sizes(), expected_size);
    ASSERT_TRUE(at::allclose(out, x.unsqueeze(1) * y));
  }
  {
    // batched (level 2, 3, 4) * batched (level 3, 1, 2)
    Tensor x = at::randn({3, 5, 7});
    Tensor y = at::randn({5, 2, 3});

    // Each BatchDim is constructed in {dim, level} format.
    Tensor Bx = makeBatched(x, {{2, 0}, {3, 1}, {4, 2}});
    Tensor By = makeBatched(y, {{1, 1}, {2, 2}, {3, 0}});
    Tensor Bout = Bx * By;

    const auto& out = maybeGetBatchedImpl(Bout)->value();

    // The batching rule aligns dimensions in the order of their `level`.
    // It just happened that we chose sizes to be in the same order as the level.
    std::vector<int64_t> expected_size = {2, 3, 5, 7};
    ASSERT_EQ(out.sizes(), expected_size);
    ASSERT_TRUE(at::allclose(out, x * y.permute({1, 2, 0}).unsqueeze(3)));
  }
}

// test for BatchedTensor::size(int).
TEST(VmapTest, TestBatchedTensorSize) {
  {
    // Single batch dim at front
    Tensor x = at::randn({3, 5, 7});
    Tensor Bx = makeBatched(x, {{0, 0}});

    ASSERT_EQ(Bx.size(0), 5);
    ASSERT_EQ(Bx.size(1), 7);
    ASSERT_EQ(Bx.size(-1), 7);
    ASSERT_EQ(Bx.size(-2), 5);
    ASSERT_THROW(Bx.size(2), c10::Error);
    ASSERT_THROW(Bx.size(-3), c10::Error);
  }
  {
    // multiple batch dims not at front
    Tensor x = at::randn({2, 3, 5, 7, 11});
    Tensor Bx = makeBatched(x, {{0, 3}, {1, 1}});

    ASSERT_EQ(Bx.size(0), 2);
    ASSERT_EQ(Bx.size(1), 5);
    ASSERT_EQ(Bx.size(2), 11);
    ASSERT_EQ(Bx.size(-1), 11);
    ASSERT_EQ(Bx.size(-2), 5);
    ASSERT_EQ(Bx.size(-3), 2);
    ASSERT_THROW(Bx.size(3), c10::Error);
    ASSERT_THROW(Bx.size(-4), c10::Error);
  }
}

TEST(VmapTest, TestVmapPhysicalViewGetPhysicalShape) {
  {
    VmapPhysicalView physical_view(ones({2, 3, 4, 5, 6}), 1 | 4);
    ASSERT_EQ(physical_view.getPhysicalShape({}), VmapDimVector({2, 3}));
    ASSERT_EQ(physical_view.getPhysicalShape({7}), VmapDimVector({2, 3, 7}));
    ASSERT_EQ(physical_view.getPhysicalShape({7, 11, 13}), VmapDimVector({2, 3, 7, 11, 13}));
    ASSERT_EQ(physical_view.getPhysicalShape({7, 11, 13, 17}), VmapDimVector({2, 3, 7, 11, 13, 17}));
  }
  {
    VmapPhysicalView physical_view(ones({2, 3, 4, 5, 6}), 2);
    ASSERT_EQ(physical_view.getPhysicalShape({}), VmapDimVector({2}));
    ASSERT_EQ(physical_view.getPhysicalShape({7}), VmapDimVector({2, 7}));
  }
}

// Basic test for BatchedTensor::expand
TEST(VmapTest, TestBatchedTensorExpand) {
  {
    // Expand size is too small
    auto tensor = at::randn({2, 3, 5});
    auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});
    ASSERT_THROW(batched.expand({5}), c10::Error);
  }
  {
    // Expand size has same dimensionality as the logical dim
    auto tensor = at::randn({2, 1, 5});
    auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});
    auto batched_out = batched.expand({3, 5});
    const auto& out = maybeGetBatchedImpl(batched_out)->value();

    ASSERT_EQ(out.data_ptr(), tensor.data_ptr());
    ASSERT_TRUE(at::allclose(out, tensor.expand({2, 3, 5})));
  }
  {
    // Expand size has same dimensionality as the logical dim, incorrect expand size
    auto tensor = at::randn({2, 1, 5});
    auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});
    ASSERT_THROW(batched.expand({1, 25}), c10::Error);
  }
  {
    // Expand size has greater dimensionality as the logical dim
    auto tensor = at::randn({2, 3, 5});
    auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});
    auto batched_out = batched.expand({7, 3, 5});
    const auto& out = maybeGetBatchedImpl(batched_out)->value();

    ASSERT_EQ(out.data_ptr(), tensor.data_ptr());
    ASSERT_TRUE(at::allclose(out, tensor.view({2, 1, 3, 5}).expand({2, 7, 3, 5})));
  }
  {
    // Expand size has greater dimensionality as the logical dim, incorrect expand size
    auto tensor = at::randn({2, 3, 5});
    auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});
    ASSERT_THROW(batched.expand({7, 9, 5}), c10::Error);
  }
  {
    // logical dim is 0, expand size has same dimensionality as logical dim
    auto tensor = at::randn({2, 3});
    auto batched = makeBatched(tensor, {{0, 0}, {1, 1}});
    auto batched_out = batched.expand({});
    const auto& out = maybeGetBatchedImpl(batched_out)->value();
    ASSERT_EQ(out.data_ptr(), tensor.data_ptr());
    ASSERT_TRUE(at::allclose(out, tensor));
  }
  {
    // logical dim is 0, expand size has greater dimensionality than logical dim
    auto tensor = at::randn({2, 3});
    auto batched = makeBatched(tensor, {{0, 0}, {1, 1}});
    auto batched_out = batched.expand({5, 7});
    const auto& out = maybeGetBatchedImpl(batched_out)->value();
    ASSERT_EQ(out.data_ptr(), tensor.data_ptr());
    ASSERT_TRUE(at::allclose(out, tensor.view({2, 3, 1, 1}).expand({2, 3, 5, 7})));
  }
}
// Basic test for BatchedTensor::unsqueeze
TEST(VmapTest, TestBatchedTensorUnsqueeze) {
  {
    // Basic test
    auto tensor = at::randn({2, 3, 5});  // NOLINT
    auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});

    auto batched_out = batched.unsqueeze(0);
    const auto& out = maybeGetBatchedImpl(batched_out)->value();
    ASSERT_EQ(out.data_ptr(), tensor.data_ptr());
    ASSERT_TRUE(at::allclose(out, tensor.unsqueeze(1)));
  }
  {
    // Test with multiple levels
    auto tensor = at::randn({2, 3, 5});  // NOLINT
    auto batched = makeBatched(tensor, {{0, 0}, {1, 1}});

    auto batched_out = batched.unsqueeze(0);
    const auto& out = maybeGetBatchedImpl(batched_out)->value();
    ASSERT_EQ(out.data_ptr(), tensor.data_ptr());
    ASSERT_TRUE(at::allclose(out, tensor.unsqueeze(2)));
  }
  {
    // Negative dim
    auto tensor = at::randn({2, 3, 5});  // NOLINT
    auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});

    auto batched_out = batched.unsqueeze(-1);
    const auto& out = maybeGetBatchedImpl(batched_out)->value();
    ASSERT_EQ(out.data_ptr(), tensor.data_ptr());
    ASSERT_TRUE(at::allclose(out, tensor.unsqueeze(-1)));
  }
}
// Basic test for BatchedTensor::squeeze(dim)
TEST(VmapTest, TestBatchedTensorSqueeze) {
  {
    // Basic test
    auto tensor = at::randn({2, 1, 5});  // NOLINT
    auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});

    auto batched_out = batched.squeeze(0);
    const auto& out = maybeGetBatchedImpl(batched_out)->value();
    ASSERT_EQ(out.data_ptr(), tensor.data_ptr());
    ASSERT_TRUE(at::allclose(out, tensor.squeeze(1)));
  }
  {
    // Test with multiple levels
    auto tensor = at::randn({2, 3, 1});  // NOLINT
    auto batched = makeBatched(tensor, {{0, 0}, {1, 1}});

    auto batched_out = batched.squeeze(0);
    const auto& out = maybeGetBatchedImpl(batched_out)->value();
    ASSERT_EQ(out.data_ptr(), tensor.data_ptr());
    ASSERT_TRUE(at::allclose(out, tensor.squeeze(2)));
  }
  {
    // Negative dim
    auto tensor = at::randn({2, 3, 1});  // NOLINT
    auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});

    auto batched_out = batched.squeeze(-1);
    const auto& out = maybeGetBatchedImpl(batched_out)->value();
    ASSERT_EQ(out.data_ptr(), tensor.data_ptr());
    ASSERT_TRUE(at::allclose(out, tensor.squeeze(-1)));
  }
}
// Basic test for BatchedTensor::transpose
TEST(VmapTest, TestBatchedTensorTranspose) {
  {
    // Basic test
    auto tensor = at::randn({2, 3, 5});  // NOLINT
    auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});

    auto batched_out = batched.transpose(0, 1);
    const auto& out = maybeGetBatchedImpl(batched_out)->value();
    ASSERT_EQ(out.data_ptr(), tensor.data_ptr());
    ASSERT_TRUE(at::allclose(out, tensor.transpose(1, 2)));
  }
  {
    // Test with multiple levels
    auto tensor = at::randn({2, 3, 5, 7, 11});  // NOLINT
    auto batched = makeBatched(tensor, {{0, 0}, {1, 1}});

    auto batched_out = batched.transpose(0, 2);
    const auto& out = maybeGetBatchedImpl(batched_out)->value();
    ASSERT_EQ(out.data_ptr(), tensor.data_ptr());
    ASSERT_TRUE(at::allclose(out, tensor.transpose(2, 4)));
  }
  {
    // Negative dims
    auto tensor = at::randn({2, 3, 5, 7});  // NOLINT
    auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});

    auto batched_out = batched.transpose(-2, -1);
    const auto& out = maybeGetBatchedImpl(batched_out)->value();
    ASSERT_EQ(out.data_ptr(), tensor.data_ptr());
    ASSERT_TRUE(at::allclose(out, tensor.transpose(-2, -1)));
  }
}

// Basic test for BatchedTensor::permute
TEST(VmapTest, TestBatchedTensorPermute) {
  {
    // Basic test
    auto tensor = at::randn({2, 3, 5});  // NOLINT
    auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});

    auto batched_out = batched.permute({1, 0});
    const auto& out = maybeGetBatchedImpl(batched_out)->value();
    ASSERT_EQ(out.data_ptr(), tensor.data_ptr());
    ASSERT_TRUE(at::allclose(out, tensor.permute({0, 2, 1})));
  }
  {
    // Test with multiple levels
    auto tensor = at::randn({2, 3, 5, 7, 11});  // NOLINT
    auto batched = makeBatched(tensor, {{0, 0}, {1, 1}});

    auto batched_out = batched.permute({2, 1, 0});
    const auto& out = maybeGetBatchedImpl(batched_out)->value();
    ASSERT_EQ(out.data_ptr(), tensor.data_ptr());
    ASSERT_TRUE(at::allclose(out, tensor.permute({0, 1, 4, 3, 2})));
  }
  {
    // Negative dims
    auto tensor = at::randn({2, 3, 5, 7});  // NOLINT
    auto batched = makeBatched(tensor, {{/*lvl*/0, /*dim*/0}});

    auto batched_out = batched.permute({-1, -2, -3});
    const auto& out = maybeGetBatchedImpl(batched_out)->value();
    ASSERT_EQ(out.data_ptr(), tensor.data_ptr());
    ASSERT_TRUE(at::allclose(out, tensor.permute({0, -1, -2, -3})));
  }
}

static void checkMultiBatchVmapTransform(TensorList inputs, TensorList expected_outputs) {
  auto outputs = MultiBatchVmapTransform::logicalToPhysical(inputs);
  ASSERT_EQ(outputs.size(), expected_outputs.size());
  for (int64_t idx = 0; idx < outputs.size(); idx++) {
    const auto& output = outputs[idx].tensor();
    ASSERT_EQ(output.data_ptr(), expected_outputs[idx].data_ptr());
    ASSERT_EQ(output.sizes(), expected_outputs[idx].sizes());
    ASSERT_TRUE(at::allclose(output, expected_outputs[idx]));
  }
}

TEST(VmapTest, TestMultiBatchVmapTransformBatchedBatched) {
  {
    // Check that batch dims get moved to the front
    int64_t B0 = 5, B1 = 7;
    Tensor x = at::randn({2, B0, 3, B1});
    Tensor y = at::randn({B1, 2, 3, B0});
    Tensor batched_x = makeBatched(x, {{/*lvl*/0, /*dim*/1}, {/*lvl*/1, /*dim*/3}});
    Tensor batched_y = makeBatched(y, {{/*lvl*/0, /*dim*/3}, {/*lvl*/1, /*dim*/0}});

    checkMultiBatchVmapTransform(
        {batched_x, batched_y},
        {at::movedim(x, {1, 3}, {0, 1}), at::movedim(y, {0, 3}, {1, 0})});
  }
  {
    // Check that batch dims become broadcasted and are present in all returns
    int64_t B0 = 5, B1 = 7, B2 = 9;
    Tensor x = at::randn({B0, B2, 2, 3});
    Tensor y = at::randn({B0, B1, 2, 3});
    Tensor batched_x = makeBatched(x, {{/*lvl*/0, /*dim*/0}, {/*lvl*/2, /*dim*/1}});
    Tensor batched_y = makeBatched(y, {{/*lvl*/0, /*dim*/0}, {/*lvl*/1, /*dim*/1}});

    checkMultiBatchVmapTransform(
        {batched_x, batched_y},
        {x.unsqueeze(1).expand({B0, B1, B2, 2, 3}), y.unsqueeze(2).expand({B0, B1, B2, 2, 3})});
  }
  {
    // Check operation on tensors of different logical dims
    int64_t B0 = 5;
    Tensor x = at::randn({B0, 3});
    Tensor y = at::randn({B0, 2, 3});
    Tensor batched_x = makeBatched(x, {{/*lvl*/0, /*dim*/0}});
    Tensor batched_y = makeBatched(y, {{/*lvl*/0, /*dim*/0}});

    checkMultiBatchVmapTransform({batched_x, batched_y}, {x, y});
  }
  {
    // More complicated example with two tensors.
    int64_t B0 = 5, B1 = 7, B2 = 11, B3 = 13;
    Tensor x = at::randn({2, B0, 3, B2});
    Tensor y = at::randn({B3, 3, B1});
    Tensor batched_x = makeBatched(x, {{/*lvl*/0, /*dim*/1}, {/*lvl*/2, /*dim*/3}});
    Tensor batched_y = makeBatched(y, {{/*lvl*/1, /*dim*/2}, {/*lvl*/3, /*dim*/0}});

    checkMultiBatchVmapTransform(
        {batched_x, batched_y},
        {
          x.permute({1, 3, 0, 2}).view({B0, 1, B2, 1, 2, 3}).expand({B0, B1, B2, B3, 2, 3}),
          y.permute({2, 0, 1}).view({1, B1, 1, B3, 3}).expand({B0, B1, B2, B3, 3}),
        });
  }
  {
    // Edge case: BatchedTensor "scalar" handling
    int64_t B0 = 5, B2 = 11;
    Tensor x = at::randn({B0});
    Tensor y = at::randn({B0, B2});
    Tensor batched_x = makeBatched(x, {{/*lvl*/0, /*dim*/0}});
    Tensor batched_y = makeBatched(y, {{/*lvl*/0, /*dim*/0}, {/*lvl*/1, /*dim*/1}});

    checkMultiBatchVmapTransform({batched_x, batched_y}, {x.view({B0, 1}).expand({B0, B2}), y});
    checkMultiBatchVmapTransform({batched_y, batched_x}, {y, x.view({B0, 1}).expand({B0, B2})});
  }
  {
    // Edge case: Only one tensor is a "batchedtensor scalar"
    int64_t B0 = 5, B2 = 11;
    Tensor x = at::randn({B0});
    Tensor y = at::randn({B0, B2, 2});
    Tensor batched_x = makeBatched(x, {{/*lvl*/0, /*dim*/0}});
    Tensor batched_y = makeBatched(y, {{/*lvl*/0, /*dim*/0}, {/*lvl*/1, /*dim*/1}});

    checkMultiBatchVmapTransform({batched_x, batched_y}, {x.view({B0, 1}).expand({B0, B2}), y});
    checkMultiBatchVmapTransform({batched_y, batched_x}, {y, x.view({B0, 1}).expand({B0, B2})});
  }
}

TEST(VmapTest, TestMultiBatchVmapTransformBatchedUnbatched) {
  {
    // Check same example size
    int64_t B0 = 5, B1 = 7;
    Tensor x = at::randn({2, B0, 3, B1});
    Tensor y = at::randn({2, 3});
    Tensor batched_x = makeBatched(x, {{/*lvl*/0, /*dim*/1}, {/*lvl*/1, /*dim*/3}});

    checkMultiBatchVmapTransform(
        {batched_x, y},
        {at::movedim(x, {1, 3}, {0, 1}), y.view({1, 1, 2, 3}).expand({B0, B1, 2, 3})});
    checkMultiBatchVmapTransform(
        {y, batched_x},
        {y.view({1, 1, 2, 3}).expand({B0, B1, 2, 3}), at::movedim(x, {1, 3}, {0, 1})});
  }
  {
    // BatchedTensor has higher example dim than non-batched-tensor
    int64_t B0 = 5, B1 = 7;
    Tensor x = at::randn({B0, B1, 2, 3});
    Tensor y = at::randn({3});
    Tensor batched_x = makeBatched(x, {{/*lvl*/0, /*dim*/0}, {/*lvl*/1, /*dim*/1}});

    checkMultiBatchVmapTransform(
        {batched_x, y}, {x, y.view({1, 1, 3}).expand({B0, B1, 3})});
    checkMultiBatchVmapTransform(
        {y, batched_x}, {y.view({1, 1, 3}).expand({B0, B1, 3}), x});
  }
  {
    // BatchedTensor has lower example dim than non-batched-tensor
    int64_t B0 = 5, B1 = 7;
    Tensor x = at::randn({B0, B1, 3});
    Tensor y = at::randn({2, 3});
    Tensor batched_x = makeBatched(x, {{/*lvl*/0, /*dim*/0}, {/*lvl*/1, /*dim*/1}});

    checkMultiBatchVmapTransform(
        {batched_x, y}, {x.view({B0, B1, 3}), y.view({1, 1, 2, 3}).expand({B0, B1, 2, 3})});
    checkMultiBatchVmapTransform(
        {y, batched_x}, {y.view({1, 1, 2, 3}).expand({B0, B1, 2, 3}), x.view({B0, B1, 3})});
  }
  {
    // Scalar handling
    int64_t B0 = 5, B1 = 7;
    Tensor x = at::randn({B0, B1});
    Tensor y = at::randn({});
    Tensor batched_x = makeBatched(x, {{/*lvl*/0, /*dim*/0}, {/*lvl*/1, /*dim*/1}});

    checkMultiBatchVmapTransform({batched_x, y}, {x, y.view({1, 1}).expand({B0, B1})});
    checkMultiBatchVmapTransform({y, batched_x}, {y.view({1, 1}).expand({B0, B1}), x});
  }
}

TEST(VmapTest, TestMultiBatchVmapTransformMaxLevels) {
  {
    // inputs have all 64 levels
    auto x = randn(std::vector<int64_t>(kVmapNumLevels, 1));
    auto y = randn(std::vector<int64_t>(kVmapNumLevels, 1));
    auto batched_x = makeBatched(x, maxBatchDimsAtFront());
    auto batched_y = makeBatched(y, maxBatchDimsAtFront());

    checkMultiBatchVmapTransform({batched_x, batched_y}, {x, y});
  }
  {
    // inputs don't have all 64 levels, but results do.
    int64_t split = 19;
    auto x = randn(std::vector<int64_t>(split, 1));
    auto y = randn(std::vector<int64_t>(kVmapNumLevels - split, 1));

    auto tmp = maxBatchDimsAtFront();
    BatchDims x_bdims(tmp.begin(), tmp.begin() + split);

    // Construct y_bdims.
    int64_t dim = 0;
    auto y_bdims_vector = fmap(
        ArrayRef<BatchDim>(tmp.begin() + split, tmp.end()),
        [&](const BatchDim& bdim) -> BatchDim {
          return { bdim.level(), dim++ };
        });
    BatchDims y_bdims(y_bdims_vector.begin(), y_bdims_vector.end());

    auto batched_x = makeBatched(x, x_bdims);
    auto batched_y = makeBatched(y, y_bdims);

    auto expected_size = std::vector<int64_t>(kVmapNumLevels, 1);
    checkMultiBatchVmapTransform(
        {batched_x, batched_y},
        {x.view(expected_size), y.view(expected_size)});
  }
}

TEST(VmapTest, TestMultiBatchVmapTransformMultipleTensors) {
  // Test with three (all batched) tensors
  {
    int64_t B0 = 5, B1 = 7, B2 = 9;
    Tensor x = at::randn({2, B0, 3, B1});
    Tensor y = at::randn({B1, 4});
    Tensor z = at::randn({2, B2});
    Tensor batched_x = makeBatched(x, {{/*lvl*/0, /*dim*/1}, {/*lvl*/1, /*dim*/3}});
    Tensor batched_y = makeBatched(y, {{/*lvl*/1, /*dim*/0}});
    Tensor batched_z = makeBatched(z, {{/*lvl*/2, /*dim*/1}});

    checkMultiBatchVmapTransform(
        {batched_x, batched_y, batched_z},
        {
          at::movedim(x, {1, 3}, {0, 1}).view({B0, B1, 1, 2, 3}).expand({B0, B1, B2, 2, 3}),
          y.view({1, B1, 1, 4}).expand({B0, B1, B2, 4}),
          z.t().view({1, 1, B2, 2}).expand({B0, B1, B2, 2}),
        });
  }
  // Test with three tensors, some batched, some unbatched
  {
    int64_t B0 = 5, B1 = 7, B2 = 9;
    Tensor x = at::randn({2, 3});
    Tensor y = at::randn({4, B0});
    Tensor z = at::randn({B1, 2, B2});
    Tensor batched_y = makeBatched(y, {{/*lvl*/0, /*dim*/1}});
    Tensor batched_z = makeBatched(z, {{/*lvl*/1, /*dim*/0}, {/*lvl*/2, /*dim*/2}});

    checkMultiBatchVmapTransform(
        {x, batched_y, batched_z},
        {
          x.view({1, 1, 1, 2, 3}).expand({B0, B1, B2, 2, 3}),
          y.t().view({B0, 1, 1, 4}).expand({B0, B1, B2, 4}),
          z.permute({0, 2, 1}).view({1, B1, B2, 2}).expand({B0, B1, B2, 2}),
        });
  }
}


} // namespace
