text: #include <gtest/gtest.h>
  
  #include <c10/util/irange.h>
  #include <torch/torch.h>
  
  #include <test/cpp/api/support.h>
  
  #include <algorithm>
  #include <random>
  #include <string>
  #include <sstream>
  
  using namespace torch::nn;
  
  namespace rnn_utils = torch::nn::utils::rnn;
  
  struct NNUtilsTest : torch::test::SeedingFixture {};
  struct PackedSequenceTest : torch::test::SeedingFixture {};
  
  TEST_F(NNUtilsTest, ClipGradNorm) {
    auto l = Linear(10, 10);
    float max_norm = 2;
    auto compute_norm = [&](float norm_type) -> float {
      float total_norm = 0.0;
      if (norm_type != std::numeric_limits<float>::infinity()) {
        for (const auto& p : l->parameters()) {
          total_norm +=
              p.grad().data().abs().pow(norm_type).sum().item().toFloat();
        }
        return std::pow(total_norm, 1.0 / norm_type);
      } else {
        for (const auto& p : l->parameters()) {
          auto param_max = p.grad().data().abs().max().item().toFloat();
          if (param_max > total_norm) {
            total_norm = param_max;
          }
        }
        return total_norm;
      }
    };
    auto compare_scaling =
        [&](const std::vector<torch::Tensor>& grads) -> torch::Tensor {
      std::vector<torch::Tensor> p_scale;
      for (const auto i : c10::irange(grads.size())) {
        auto param = l->parameters()[i];
        auto grad = grads[i];
        p_scale.push_back(param.grad().data().div(grad).view(-1));
      }
      auto scale = torch::cat(p_scale);
      return scale; // need to assert std is 0.
    };
  
    std::vector<torch::Tensor> grads = {
        torch::arange(1.0, 101).view({10, 10}),
        torch::ones({10}).div(1000),
    };
    std::vector<float> norm_types = {
        0.5,
        1.5,
        2.0,
        4.0,
        std::numeric_limits<float>::infinity(),
    };
    for (auto norm_type : norm_types) {
      for (const auto i : c10::irange(grads.size())) {
        l->parameters()[i].mutable_grad() =
            grads[i].clone().view_as(l->parameters()[i].data());
      }
      auto norm_before = compute_norm(norm_type);
      auto norm = utils::clip_grad_norm_(l->parameters(), max_norm, norm_type);
      auto norm_after = compute_norm(norm_type);
      ASSERT_FLOAT_EQ(norm, norm_before);
      ASSERT_NEAR(norm_after, max_norm, 1e-6);
      ASSERT_LE(norm_after, max_norm);
      auto scaled = compare_scaling(grads);
      ASSERT_NEAR(0, scaled.std().item().toFloat(), 1e-7);
    }
    // Small gradients should be left unchanged
    grads = {
        torch::rand({10, 10}).div(10000),
        torch::ones(10).div(500),
    };
    for (auto norm_type : norm_types) {
      for (const auto i : c10::irange(grads.size())) {
        l->parameters()[i].grad().data().copy_(grads[i]);
      }
      auto norm_before = compute_norm(norm_type);
      auto norm = utils::clip_grad_norm_(l->parameters(), max_norm, norm_type);
      auto norm_after = compute_norm(norm_type);
      ASSERT_FLOAT_EQ(norm, norm_before);
      ASSERT_FLOAT_EQ(norm_before, norm_after);
      ASSERT_LE(norm_after, max_norm);
      auto scaled = compare_scaling(grads);
      ASSERT_NEAR(0, scaled.std().item().toFloat(), 1e-7);
      ASSERT_EQ(scaled[0].item().toFloat(), 1);
    }
    // should accept a single tensor as input
    auto p1 = torch::randn({10, 10});
    auto p2 = torch::randn({10, 10});
    auto g = torch::arange(1., 101).view({10, 10});
    p1.mutable_grad() = g.clone();
    p2.mutable_grad() = g.clone();
    for (const auto norm_type : norm_types) {
      utils::clip_grad_norm_(p1, max_norm, norm_type);
      utils::clip_grad_norm_({p2}, max_norm, norm_type);
      ASSERT_TRUE(torch::allclose(p1.grad(), p2.grad()));
    }
  }
  
  // Check that clip_grad_norm_ raises an error if the norm of a gradient
  // is non-finite
  TEST_F(NNUtilsTest, ClipGradNormErrorIfNonfinite) {
    double inf = std::numeric_limits<double>::infinity();
    double nan = std::numeric_limits<double>::quiet_NaN();
  
    using Vector = std::vector<double>;
  
    Vector norms_pos = {0.1, 1, 2, 3.5, inf};
    Vector norms_neg = {-0.1, -1, -2, -3.5};
    Vector norms_neg_plus_0 = {0, -0.1, -1, -2, -3.5};
    Vector norms_except_0 = {0.1, 1, 2, 3.5, inf, -0.1, -1, -2, -3.5};
    Vector norms_all = {0, 0.1, 1, 2, 3.5, inf, -0.1, -1, -2, -3.5};
  
    // Each entry in test_cases has the following values, in this order:
    //
    // grad_only_one_elem    If True, only one element of the parameter's
    //                       gradient is set to the scalar grad, and the
    //                       rest of the elements are 0. If False, all grad
    //                       elements are equal to the scalar.
    //
    // prefix_finite_grad_param  If True, prefix a parameter that has a grad
    //                           of 1.
    //
    // scalars           Scalars to use as the parameter's grad, through
    //                   multiplication
    //
    // norms_nonfinite   Norm types that should produce nonfinite total norm
    //
    // norms_finite      Norm types that should produce finite total norm
    std::vector<std::tuple<bool, bool, Vector, Vector, Vector>> test_cases({
      // Test errors from an infinite grad
      std::make_tuple(false, false, Vector({inf, -inf}), norms_except_0, Vector({0})),
      std::make_tuple(false, true, Vector({inf, -inf}), norms_pos, norms_neg_plus_0),
      std::make_tuple(true, false, Vector({inf, -inf}), norms_pos, norms_neg_plus_0),
      std::make_tuple(false, true, Vector({inf, -inf}), norms_pos, norms_neg_plus_0),
  
      // Test errors from a NaN grad
      std::make_tuple(false, false, Vector({nan}), norms_except_0, Vector({0})),
      std::make_tuple(false, true, Vector({nan}), norms_except_0, Vector({0})),
      std::make_tuple(true, false, Vector({nan}), norms_except_0, Vector({0})),
      std::make_tuple(true, true, Vector({nan}), norms_except_0, Vector({0})),
  
      // Test a grad that should never error
      std::make_tuple(false, false, Vector({2e22, -2e22}), Vector(), norms_all),
      std::make_tuple(false, true, Vector({2e22, -2e22}), Vector(), norms_all),
      std::make_tuple(true, false, Vector({2e22, -2e22}), Vector(), norms_all),
      std::make_tuple(true, true, Vector({2e22, -2e22}), Vector(), norms_all),
  
      // Test a grad that will overflow to inf for only some norm orders
      std::make_tuple(
        false, false, Vector({2e200, -2e200}), Vector({3.5, 2, -2, -3.5}),
        Vector({inf, 1, 0.1, 0, -1, -0.1})),
      std::make_tuple(
        false, true, Vector({2e200, -2e200}), Vector({3.5, 2}),
        Vector({inf, 1, 0.1, 0, -1, -0.1, -2, -3.5})),
      std::make_tuple(
        true, false, Vector({2e200, -2e200}), Vector({3.5, 2}),
        Vector({inf, 1, 0.1, 0, -1, -0.1, -2, -3.5})),
      std::make_tuple(
        false, true, Vector({2e200, -2e200}), Vector({3.5, 2}),
        Vector({inf, 1, 0.1, 0, -1, -0.1, -2, -3.5})),
    });
  
    auto gen_parameters = [](
        double scalar,
        bool grad_only_one_elem,
        bool prefix_finite_grad_param,
        torch::DeviceType device_type) {
      auto param = torch::ones(10, torch::TensorOptions().dtype(torch::kDouble).device(device_type).requires_grad(true));
      if (grad_only_one_elem) {
        param[1].mul(scalar).sum().backward();
      } else {
        param.mul(scalar).sum().backward();
      }
  
      std::vector<torch::Tensor> parameters;
      if (prefix_finite_grad_param) {
        auto prefix_param = torch::ones(1, torch::TensorOptions().dtype(torch::kDouble).device(device_type).requires_grad(true));
        prefix_param.mul(1).sum().backward();
        parameters.push_back(prefix_param);
      }
      parameters.push_back(param);
  
      return parameters;
    };
  
    auto run_test_case = [&gen_parameters](
        double norm_type,
        bool error_if_nonfinite,
        double scalar,
        bool grad_only_one_elem,
        bool prefix_finite_grad_param,
        bool is_norm_nonfinite,
        torch::DeviceType device_type) {
      std::stringstream ss;
      ss << "device: " << device_type
         << ", norm_type: " << norm_type
         << ", error_if_nonfinite: " << error_if_nonfinite
         << ", scalar: " << scalar
         << ", grad_only_one_elem: " << grad_only_one_elem
         << ", prefix_finite_grad_param: " << prefix_finite_grad_param
         << ", is_norm_nonfinite: " << is_norm_nonfinite;
      std::string msg = ss.str();
  
      auto parameters = gen_parameters(
        scalar,
        grad_only_one_elem,
        prefix_finite_grad_param,
        device_type);
  
      if (is_norm_nonfinite && error_if_nonfinite) {
        std::vector<torch::Tensor> grads_before;
        // NOLINTNEXTLINE(performance-for-range-copy)
        for (auto p : parameters) {
          // NOLINTNEXTLINE(performance-inefficient-vector-operation)
          grads_before.push_back(p.grad().clone());
        }
        // NOLINTNEXTLINE(cppcoreguidelines-avoid-goto,hicpp-avoid-goto)
        EXPECT_THROW(utils::clip_grad_norm_(parameters, 1., norm_type, true), std::exception) << msg;
        // Grads should not change if error is thrown
        for (const auto p_idx : c10::irange(parameters.size())) {
          ASSERT_TRUE(torch::allclose(parameters[p_idx].grad(), grads_before[p_idx], 1.0, 0.0, /*equal_nan*/ true)) << msg;
        }
      } else {
        // NOLINTNEXTLINE(cppcoreguidelines-avoid-goto,hicpp-avoid-goto)
        EXPECT_NO_THROW(utils::clip_grad_norm_(parameters, 1., norm_type, error_if_nonfinite)) << msg;
      }
    };
  
    for (auto device_type : {torch::kCPU, torch::kCUDA}) {
      if (device_type == torch::kCUDA && !torch::cuda::is_available()) {
        continue;
      }
      for (auto test_case : test_cases) {
        auto grad_only_one_elem = std::get<0>(test_case);
        auto prefix_finite_grad_param = std::get<1>(test_case);
        auto scalars = std::get<2>(test_case);
        auto norms_nonfinite = std::get<3>(test_case);
        auto norms_finite = std::get<4>(test_case);
  
        for (auto error_if_nonfinite : {false, true}) {
          for (auto scalar : scalars) {
            for (auto norm_type : norms_nonfinite) {
              run_test_case(
                norm_type,
                error_if_nonfinite,
                scalar,
                grad_only_one_elem,
                prefix_finite_grad_param,
                true,
                device_type);
            }
  
            for (auto norm_type : norms_finite) {
              run_test_case(
                norm_type,
                error_if_nonfinite,
                scalar,
                grad_only_one_elem,
                prefix_finite_grad_param,
                false,
                device_type);
            }
          }
        }
      }
    }
  }
  
  TEST_F(NNUtilsTest, ClipGradValue) {
    auto l = Linear(10, 10);
    float clip_value = 2.5;
  
    torch::Tensor grad_w = torch::arange(-50., 50).view({10, 10}).div_(5);
    torch::Tensor grad_b = torch::ones({10}).mul_(2);
    std::vector<std::vector<torch::Tensor>> grad_lists = {
        {grad_w, grad_b}, {grad_w, torch::Tensor()}};
    for (auto grad_list : grad_lists) {
      for (const auto i : c10::irange(grad_list.size())) {
        auto p = l->parameters()[i];
        auto g = grad_list[i];
        p.mutable_grad() = g.defined() ? g.clone().view_as(p.data()) : g;
      }
  
      utils::clip_grad_value_(l->parameters(), clip_value);
      for (const auto& p : l->parameters()) {
        if (p.grad().defined()) {
          ASSERT_LE(
              p.grad().data().max().item().toFloat(), clip_value);
          ASSERT_GE(
              p.grad().data().min().item().toFloat(), -clip_value);
        }
      }
    }
  
    // Should accept a single Tensor as input
    auto p1 = torch::randn({10, 10});
    auto p2 = torch::randn({10, 10});
    auto g = torch::arange(-50., 50).view({10, 10}).div_(5);
    p1.mutable_grad() = g.clone();
    p2.mutable_grad() = g.clone();
    utils::clip_grad_value_(p1, clip_value);
    utils::clip_grad_value_({p2}, clip_value);
    ASSERT_TRUE(torch::allclose(p1.grad(), p2.grad()));
  }
  
  TEST_F(NNUtilsTest, ConvertParameters) {
    std::vector<torch::Tensor> parameters{
      torch::arange(9, torch::kFloat32),
      torch::arange(9, torch::kFloat32).view({3, 3}),
      torch::arange(8, torch::kFloat32).view({2, 2, 2})
    };
  
    auto expected = torch::cat({
      torch::arange(9, torch::kFloat32),
      torch::arange(9, torch::kFloat32).view(-1),
      torch::arange(8, torch::kFloat32).view(-1)
    });
    auto vector = utils::parameters_to_vector(parameters);
    ASSERT_TRUE(vector.allclose(expected));
  
    std::vector<torch::Tensor> zero_parameters{
      torch::zeros({9}, torch::kFloat32),
      torch::zeros({9}, torch::kFloat32).view({3, 3}),
      torch::zeros({8}, torch::kFloat32).view({2, 2, 2})
    };
  
    utils::vector_to_parameters(vector, zero_parameters);
    for (const auto i : c10::irange(zero_parameters.size())) {
      ASSERT_TRUE(zero_parameters[i].allclose(parameters[i]));
    }
  
    {
      auto conv1 = Conv2d(3, 10, 5);
      auto fc1 = Linear(10, 20);
      auto model = Sequential(conv1, fc1);
  
      auto vec = utils::parameters_to_vector(model->parameters());
      ASSERT_EQ(vec.size(0), 980);
    }
    {
      auto conv1 = Conv2d(3, 10, 5);
      auto fc1 = Linear(10, 20);
      auto model = Sequential(conv1, fc1);
  
      auto vec = torch::arange(0., 980);
      utils::vector_to_parameters(vec, model->parameters());
  
      auto sample = model->parameters()[0][0][0][0];
      ASSERT_TRUE(torch::equal(sample.data(), vec.data().slice(0, 0, 5)));
    }
  }
  
  // NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers,cppcoreguidelines-avoid-non-const-global-variables)
  int64_t PackedSequenceTest_batch_size = 5;
  // NOLINTNEXTLINE(cppcoreguidelines-avoid-magic-numbers,cppcoreguidelines-avoid-non-const-global-variables)
  int64_t PackedSequenceTest_max_length = 6;
  
  std::vector<torch::Tensor> PackedSequenceTest_ordered_sequence(torch::ScalarType tensor_type) {
    std::vector<torch::Tensor> seqs;
    seqs.reserve(PackedSequenceTest_batch_size);
    for (const auto i : c10::irange(PackedSequenceTest_batch_size)) {
      (void)i; // Suppress unused variable warning
      seqs.emplace_back(torch::empty({
        torch::randint(1, PackedSequenceTest_max_length, {1}).item<int64_t>()
      }, tensor_type));
    }
    for (auto& s : seqs) {
      s.random_(-128, 128);
    }
    sort(
      seqs.begin(),
      seqs.end(),
      [&](const torch::Tensor& t1, const torch::Tensor& t2) {
        return t1.size(0) > t2.size(0);
      }
    );
    return seqs;
  }
  
  std::tuple<torch::Tensor, torch::Tensor> PackedSequenceTest_padded_sequence(torch::ScalarType tensor_type) {
    // Create Tensor of random padded sequences
    auto ordered = PackedSequenceTest_ordered_sequence(tensor_type);
    auto lengths = torch::empty({(int64_t)ordered.size()}, torch::kInt64);
    for (const auto i : c10::irange(ordered.size())) {
      lengths[i] = ordered[i].size(0);
    }
    auto padded_tensor = rnn_utils::pad_sequence(ordered);
    return std::make_tuple(padded_tensor, lengths);
  }
  
  void assert_is_equal_packed_sequence(const rnn_utils::PackedSequence& a, const rnn_utils::PackedSequence& b) {
    ASSERT_TRUE(torch::allclose(a.data(), b.data()));
    ASSERT_TRUE(torch::allclose(a.batch_sizes(), b.batch_sizes()));
    ASSERT_TRUE(
      (!a.sorted_indices().defined() && !b.sorted_indices().defined()) ||
      torch::allclose(a.sorted_indices(), b.sorted_indices()));
    ASSERT_TRUE(
      (!a.unsorted_indices().defined() && !b.unsorted_indices().defined()) ||
      torch::allclose(a.unsorted_indices(), b.unsorted_indices()));
  }
  
  void assert_is_same_packed_sequence(const rnn_utils::PackedSequence& a, const rnn_utils::PackedSequence& b) {
    ASSERT_TRUE(a.data().is_same(b.data()));
    ASSERT_TRUE(a.batch_sizes().is_same(b.batch_sizes()));
    ASSERT_TRUE(a.sorted_indices().is_same(b.sorted_indices()));
    ASSERT_TRUE(a.unsorted_indices().is_same(b.unsorted_indices()));
  }
  
  TEST_F(PackedSequenceTest, WrongOrder) {
    auto a = torch::ones({25, 300});
    auto b = torch::ones({22, 300});
    auto b_a = rnn_utils::pad_sequence({b, a});
    // NOLINTNEXTLINE(cppcoreguidelines-avoid-goto,hicpp-avoid-goto)
    ASSERT_THROW(
      rnn_utils::pack_padded_sequence(
        b_a, torch::tensor({22, 25}), /*batch_first=*/false, /*enforce_sorted=*/true),
      c10::Error);
  }
  
  TEST_F(PackedSequenceTest, TotalLength) {
    torch::Tensor padded, lengths;
    std::tie(padded, lengths) = PackedSequenceTest_padded_sequence(torch::kFloat);
    int64_t max_length = torch::max(lengths).item<int64_t>();
    rnn_utils::PackedSequence packed = rnn_utils::pack_padded_sequence(padded, lengths);
  
    // test ValueError if total_length < max_length
    for (int64_t total_length : std::vector<int64_t>{-1, 0, max_length - 1}) {
      for (bool batch_first : std::vector<bool>{true, false}) {
        auto err_fn = [&]() {
          rnn_utils::pad_packed_sequence(
            packed,
            /*batch_first=*/batch_first,
            /*padding_value=*/0.0,
            /*total_length=*/total_length);
        };
        ASSERT_THROWS_WITH(err_fn(),
          "Expected total_length to be at least the length of the longest sequence in input");
      }
    }
  
    // test that pad_packed_sequence returns results of correct length
    for (bool batch_first : std::vector<bool>{true, false}) {
      torch::Tensor no_extra_pad, ignored;
      std::tie(no_extra_pad, ignored) = rnn_utils::pad_packed_sequence(
        packed, /*batch_first=*/batch_first);
      for (int64_t total_length_delta : std::vector<int64_t>{0, 1, 8}) {
        int64_t total_length = max_length + total_length_delta;
        torch::Tensor unpacked, lengths_out;
        std::tie(unpacked, lengths_out) = rnn_utils::pad_packed_sequence(
          packed, /*batch_first=*/batch_first, /*padding_value=*/0.0, /*total_length=*/total_length);
        ASSERT_TRUE(torch::allclose(lengths, lengths_out));
        ASSERT_EQ(unpacked.size(batch_first ? 1 : 0), total_length);
        torch::Tensor ref_output, extra_pad;
        if (total_length_delta == 0) {
          ref_output = no_extra_pad;
        } else if (batch_first) {
          extra_pad = torch::zeros({PackedSequenceTest_batch_size, total_length_delta}, no_extra_pad.options());
          ref_output = torch::cat({no_extra_pad, extra_pad}, 1);
        } else {
          extra_pad = torch::zeros({total_length_delta, PackedSequenceTest_batch_size}, no_extra_pad.options());
          ref_output = torch::cat({no_extra_pad, extra_pad}, 0);
        }
        ASSERT_TRUE(torch::allclose(unpacked, ref_output));
      }
    }
  }
  
  TEST_F(PackedSequenceTest, To) {
    for (bool enforce_sorted : std::vector<bool>{true, false}) {
      torch::Tensor padded, lengths;
      std::tie(padded, lengths) = PackedSequenceTest_padded_sequence(torch::kInt);
      rnn_utils::PackedSequence a = rnn_utils::pack_padded_sequence(
        padded, lengths, /*batch_first=*/false, /*enforce_sorted=*/enforce_sorted).cpu();
  
      assert_is_same_packed_sequence(a, a.to(torch::kCPU));
      assert_is_same_packed_sequence(a, a.cpu());
      assert_is_same_packed_sequence(a, a.to(torch::device(torch::kCPU).dtype(torch::kInt32)));
  
      if (torch::cuda::is_available()) {
        auto b = a.cuda();
        assert_is_same_packed_sequence(b, b.to(torch::kCUDA));
        assert_is_same_packed_sequence(b, b.cuda());
        assert_is_equal_packed_sequence(a, b.to(torch::kCPU));
        assert_is_equal_packed_sequence(b, a.to(torch::kCUDA));
        assert_is_equal_packed_sequence(a, b.to(torch::device(torch::kCPU).dtype(torch::kInt32)));
        assert_is_same_packed_sequence(b, b.to(torch::kInt32));
      }
    }
  }
  
  TEST_F(NNUtilsTest, PackSequence) {
    auto _compatibility_test = [&](
        torch::ArrayRef<torch::Tensor> sequences,
        torch::Tensor lengths,
        bool batch_first,
        bool enforce_sorted = false) {
      torch::Tensor padded = rnn_utils::pad_sequence(sequences, batch_first);
      rnn_utils::PackedSequence packed = rnn_utils::pack_sequence(sequences, enforce_sorted);
      std::tuple<torch::Tensor, torch::Tensor> unpacked = rnn_utils::pad_packed_sequence(packed, batch_first);
      ASSERT_TRUE(torch::allclose(padded, std::get<0>(unpacked)));
      rnn_utils::PackedSequence pack_padded = rnn_utils::pack_padded_sequence(
          padded, lengths, batch_first, enforce_sorted);
      assert_is_equal_packed_sequence(packed, pack_padded);
    };
  
    // single dimensional
    auto a = torch::tensor({1, 2, 3});
    auto b = torch::tensor({4, 5});
    auto c = torch::tensor({6});
    rnn_utils::PackedSequence packed = rnn_utils::pack_sequence({a, b, c}, /*enforce_sorted=*/false);
    auto expected = torch::tensor({1, 4, 6, 2, 5, 3});
    ASSERT_TRUE(torch::allclose(packed.batch_sizes(), torch::tensor({3, 2, 1})));
    ASSERT_TRUE(torch::allclose(packed.data(), expected));
    ASSERT_TRUE(torch::allclose(packed.sorted_indices(), torch::tensor({0, 1, 2})));
    ASSERT_TRUE(torch::allclose(packed.unsorted_indices(), torch::tensor({0, 1, 2})));
  
    rnn_utils::PackedSequence packed_unsorted = rnn_utils::pack_sequence({b, c, a}, /*enforce_sorted=*/false);
    ASSERT_TRUE(torch::allclose(packed_unsorted.batch_sizes(), torch::tensor({3, 2, 1})));
    ASSERT_TRUE(torch::allclose(packed_unsorted.data(), expected));
    ASSERT_TRUE(torch::allclose(packed_unsorted.sorted_indices(), torch::tensor({2, 0, 1})));
    ASSERT_TRUE(torch::allclose(packed_unsorted.unsorted_indices(), torch::tensor({1, 2, 0})));
  
    // single dimensional, enforce_sorted = True
    rnn_utils::PackedSequence packed_enforce_sorted = rnn_utils::pack_sequence({a, b, c}, /*enforce_sorted=*/true);
    ASSERT_TRUE(torch::allclose(packed_enforce_sorted.batch_sizes(), torch::tensor({3, 2, 1})));
    ASSERT_TRUE(torch::allclose(packed_enforce_sorted.data(), expected));
    ASSERT_FALSE(packed_enforce_sorted.sorted_indices().defined());
    ASSERT_FALSE(packed_enforce_sorted.unsorted_indices().defined());
  
    ASSERT_THROWS_WITH(
      rnn_utils::pack_sequence({b, c, a}, /*enforce_sorted=*/true),
      "must be sorted in decreasing order");
  
    ASSERT_THROWS_WITH(
      rnn_utils::pack_sequence({b, c, a}, /*enforce_sorted=*/true),
      "You can pass `enforce_sorted=False`");
  
    // more dimensions
    int64_t maxlen = 9;
    for (int64_t num_dim : std::vector<int64_t>{0, 1, 2, 3}) {
      std::vector<torch::Tensor> sequences;
      std::vector<int64_t> lengths_vec;
      std::vector<int64_t> trailing_dims(num_dim, 4);
      for (int64_t i = maxlen; i > 0; i--) {
        int64_t seq_len = i * i;
        lengths_vec.emplace_back(seq_len);
        std::vector<int64_t> tensor_sizes{seq_len, 5};
        tensor_sizes.insert(
          tensor_sizes.end(),
          trailing_dims.begin(),
          trailing_dims.end());
        sequences.emplace_back(torch::rand(tensor_sizes));
      }
      std::vector<torch::Tensor> unsorted_sequences;
      for (const auto& s : sequences) {
        // NOLINTNEXTLINE(performance-inefficient-vector-operation)
        unsorted_sequences.emplace_back(s.clone());
      }
      std::shuffle(
        std::begin(unsorted_sequences),
        std::end(unsorted_sequences),
        std::default_random_engine{});
  
      std::vector<int64_t> unsorted_sequences_lengths_vec;
      for (const auto& t : unsorted_sequences) {
        // NOLINTNEXTLINE(performance-inefficient-vector-operation)
        unsorted_sequences_lengths_vec.emplace_back(t.size(0));
      }
  
      // compatibility with other utilities
      for (bool batch_first : std::vector<bool>{true, false}) {
        for (bool enforce_sorted : std::vector<bool>{true, false}) {
          _compatibility_test(
            sequences, torch::tensor(lengths_vec), batch_first, enforce_sorted);
        }
        _compatibility_test(
          unsorted_sequences, torch::tensor(unsorted_sequences_lengths_vec), batch_first);
      }
    }
  }
  
  TEST_F(NNUtilsTest, PackPaddedSequence) {
    auto generate_test_case = [&](
        torch::ArrayRef<int64_t> sorted_lengths,
        bool should_shuffle) {
      auto pad = [&](torch::Tensor tensor, int64_t length) {
        std::vector<int64_t> tensor_sizes{length - tensor.size(0)};
        tensor_sizes.insert(
          tensor_sizes.end(),
          tensor.sizes().slice(1).begin(),
          tensor.sizes().slice(1).end());
        return torch::cat({tensor, torch::zeros(tensor_sizes, tensor.options())});
      };
      int64_t max_length = sorted_lengths[0];
      torch::Tensor batch_sizes = torch::empty({max_length}, torch::kInt64);
      for (int64_t i = 1; i < max_length + 1; i++) {
        int64_t total = 0;
        for (const auto& x : sorted_lengths) {
          if (x >= i) {
            total++;
          }
        }
        batch_sizes[i-1] = total;
      }
      int64_t offset = 0;
      std::vector<torch::Tensor> tensors_to_be_cat;
      for (int64_t i = 1; i < static_cast<int64_t>(sorted_lengths.size() + 1); i++) {
        int64_t l = sorted_lengths.at(i-1);
        tensors_to_be_cat.emplace_back(pad(i * 100 + torch::arange(1., 5 * l + 1).view({l, 1, 5}), max_length));
      }
      auto padded = torch::cat(tensors_to_be_cat, 1);
      std::vector<torch::Tensor> expected_data_vec;
      for (const auto n : c10::irange(batch_sizes.size(0))) {
        int64_t batch_size = batch_sizes[n].item<int64_t>();
        for (const auto i : c10::irange(batch_size)) {
          expected_data_vec.emplace_back(torch::arange(1., 6) + (i + 1) * 100 + 5 * n);
        }
      }
      auto expected_data = torch::stack(expected_data_vec, /*dim=*/0);
  
      torch::Tensor unsorted_indices, lengths;
      if (should_shuffle) {
        // Shuffle the padded sequence to create an unsorted sequence
        std::vector<int64_t> permutation;
        for (const auto i : c10::irange(sorted_lengths.size())) {
          permutation.emplace_back(i);
        }
        std::shuffle(
          std::begin(permutation),
          std::end(permutation),
          std::default_random_engine{});
  
        unsorted_indices = torch::tensor(permutation);
        padded = padded.index_select(1, unsorted_indices);
        lengths = torch::tensor(sorted_lengths).index_select(0, unsorted_indices);
      } else {
        unsorted_indices = torch::Tensor();
        lengths = torch::tensor(sorted_lengths);
      }
  
      return std::make_tuple(
        padded.requires_grad_(), lengths, expected_data, batch_sizes, unsorted_indices);
    };
  
    std::vector<std::pair<std::vector<int64_t>, bool>> test_cases = {
      // sorted_lengths, should_shuffle
      {{10, 8, 4, 2, 2, 2, 1}, false},
      {{11, 10, 8, 6, 4, 3, 1}, false},
      {{11, 10, 8, 6, 4, 3, 1}, true}
    };
  
    for (const auto& test_case : test_cases) {
      for (bool batch_first : std::vector<bool>{true, false}) {
        // NOLINTNEXTLINE(performance-unnecessary-copy-initialization)
        std::vector<int64_t> sorted_lengths = std::get<0>(test_case);
        bool should_shuffle = std::get<1>(test_case);
  
        torch::Tensor padded, lengths, expected_data, batch_sizes, unsorted_indices;
        std::tie(padded, lengths, expected_data, batch_sizes, unsorted_indices) = generate_test_case(
          sorted_lengths, should_shuffle);
  
        auto src = padded;
        if (batch_first) {
          src = src.transpose(0, 1);
        }
  
        // check output
        rnn_utils::PackedSequence packed = rnn_utils::pack_padded_sequence(
          src, lengths, /*batch_first=*/batch_first, /*enforce_sorted=*/!should_shuffle);
        ASSERT_TRUE(torch::allclose(packed.data(), expected_data));
        ASSERT_TRUE(torch::allclose(packed.batch_sizes(), batch_sizes));
        ASSERT_TRUE(
          (!packed.unsorted_indices().defined() && !unsorted_indices.defined()) ||
          torch::allclose(packed.unsorted_indices(), unsorted_indices));
  
        // test inverse
        torch::Tensor unpacked, unpacked_len;
        std::tie(unpacked, unpacked_len) = rnn_utils::pad_packed_sequence(packed, /*batch_first=*/batch_first);
        ASSERT_TRUE(torch::allclose(unpacked, src));
        ASSERT_TRUE(torch::allclose(unpacked_len, lengths));
  
        // check grad
        if (padded.grad().defined()) {
          torch::NoGradGuard no_grad;
          padded.grad().zero_();
        }
        torch::Tensor grad_output;
        {
          torch::NoGradGuard no_grad;
          grad_output = unpacked.clone().normal_();
        }
        unpacked.backward(grad_output);
        if (batch_first) {
          grad_output.transpose_(0, 1);
        }
        for (const auto i : c10::irange(lengths.size(0))) {
          int64_t l = lengths[i].item<int64_t>();
          ASSERT_TRUE(torch::allclose(
            padded.grad().narrow(0, 0, l).select(1, i),
            grad_output.narrow(0, 0, l).select(1, i)));
          if (l < 10) {
            ASSERT_EQ(
              padded.grad().narrow(0, l, padded.grad().size(0) - l).select(1, i).abs().sum().item<double>(),
              0);
          }
        }
      }
    }
  
    // test error messages
    ASSERT_THROWS_WITH(rnn_utils::pack_padded_sequence(torch::randn({3, 3}), torch::tensor({1, 3, 2})),
        "You can pass `enforce_sorted=False`");
    ASSERT_THROWS_WITH(rnn_utils::pack_padded_sequence(torch::randn({0, 0}), torch::tensor({})),
        "empty tensor");
  }
  
  TEST_F(NNUtilsTest, PadSequence) {
    auto pad = [&](const torch::Tensor& tensor, int64_t length) {
      torch::NoGradGuard no_grad;
      std::vector<int64_t> tensor_sizes{length - tensor.size(0)};
      tensor_sizes.insert(
        tensor_sizes.end(),
        tensor.sizes().slice(1).begin(),
        tensor.sizes().slice(1).end());
      return torch::cat({tensor, torch::zeros(tensor_sizes, tensor.options())});
    };
  
    // single dimensional
    auto a = torch::tensor({1, 2, 3});
    auto b = torch::tensor({4, 5});
    auto c = torch::tensor({6});
  
    torch::Tensor expected, padded;
  
    // batch_first = true
    expected = torch::tensor({{4, 5, 0}, {1, 2, 3}, {6, 0, 0}});
    padded = rnn_utils::pad_sequence({b, a, c}, true);
    ASSERT_TRUE(padded.allclose(expected));
  
    // batch_first = false
    padded = rnn_utils::pad_sequence({b, a, c});
    ASSERT_TRUE(padded.allclose(expected.transpose(0, 1)));
  
    // pad with non-zero value
    expected = torch::tensor({{4, 5, 1}, {1, 2, 3}, {6, 1, 1}});
    padded = rnn_utils::pad_sequence({b, a, c}, true, 1);
    ASSERT_TRUE(padded.allclose(expected));
  
    // Test pad sorted sequence
    expected = torch::tensor({{1, 2, 3}, {4, 5, 0}, {6, 0, 0}});
    padded = rnn_utils::pad_sequence({a, b, c}, true);
    ASSERT_TRUE(padded.allclose(expected));
  
    // more dimensions
    int64_t maxlen = 9;
    for (int64_t num_dim : std::vector<int64_t>{0, 1, 2, 3}) {
      std::vector<torch::Tensor> sequences;
      std::vector<int64_t> trailing_dims(num_dim, 4);
      for (int64_t i = 1; i < maxlen + 1; i++) {
        int64_t seq_len = i * i;
        std::vector<int64_t> tensor_sizes{seq_len, 5};
        tensor_sizes.insert(
          tensor_sizes.end(),
          trailing_dims.begin(),
          trailing_dims.end());
        sequences.emplace_back(torch::rand(tensor_sizes));
      }
      std::shuffle(
        std::begin(sequences),
        std::end(sequences),
        std::default_random_engine{});
      std::vector<torch::Tensor> expected_tensors;
      for (const torch::Tensor& seq : sequences) {
        // NOLINTNEXTLINE(performance-inefficient-vector-operation)
        expected_tensors.emplace_back(pad(seq, maxlen * maxlen));
      }
  
      // batch first = true
      auto expected = torch::stack(expected_tensors);
      auto padded = rnn_utils::pad_sequence(sequences, true);
      ASSERT_TRUE(padded.allclose(expected));
  
      // batch first = false
      padded = rnn_utils::pad_sequence(sequences);
      ASSERT_TRUE(padded.allclose(expected.transpose(0, 1)));
    }
  }
  