#include <catch.hpp>

#include <torch/torch.h>

using namespace torch;
using namespace torch::nn;

class TestModel : public Module {
 public:
  TestModel() {
    register_module("l1", &TestModel::l1, Linear(10, 3).build());
    register_module("l2", &TestModel::l2, Linear(3, 5).build());
    register_module("l3", &TestModel::l3, Linear(5, 100).build());
  }

  variable_list forward(variable_list input) override {
    return input;
  }

  std::shared_ptr<Linear> l1, l2, l3;
};

class NestedModel : public Module {
 public:
  NestedModel() {
    register_module("l1", &NestedModel::l1, Linear(5, 20).build());
    register_module("test", &NestedModel::t, std::make_shared<TestModel>());
    register_parameter(
        "param", &NestedModel::param_, at::CPU(at::kFloat).tensor({3, 2, 21}));
  }

  variable_list forward(variable_list input) override {
    return input;
  };

  Variable param_;
  std::shared_ptr<Linear> l1;
  std::shared_ptr<TestModel> t;
};

TEST_CASE("containers") {
  SECTION("conv") {
    SECTION("1d") {
      auto model = Conv1d(3, 2, 3).stride(2).build();
      auto x = Var(at::CPU(at::kFloat).randn({2, 3, 5}), true);
      auto y = model->forward({x})[0];
      Variable s = y.sum();

      backward(s);
      REQUIRE(y.ndimension() == 3);
      REQUIRE(s.ndimension() == 0);
      for (auto i = 0; i < 3; i++) {
        REQUIRE(y.size(i) == 2);
      }

      REQUIRE(model->parameters().at("weight").grad().numel() == 3 * 2 * 3);
    }
    SECTION("2d") {
      SECTION("even") {
        auto model = Conv2d(3, 2, 3).stride(2).build();
        auto x = Var(at::CPU(at::kFloat).randn({2, 3, 5, 5}), true);
        auto y = model->forward({x})[0];
        Variable s = y.sum();

        backward(s);
        REQUIRE(y.ndimension() == 4);
        REQUIRE(s.ndimension() == 0);
        for (auto i = 0; i < 4; i++) {
          REQUIRE(y.size(i) == 2);
        }

        REQUIRE(
            model->parameters().at("weight").grad().numel() == 3 * 2 * 3 * 3);
      }

      SECTION("uneven") {
        auto model = Conv2d(3, 2, {3, 2}).stride({2, 2}).build();
        auto x = Var(at::CPU(at::kFloat).randn({2, 3, 5, 4}), true);
        auto y = model->forward({x})[0];
        Variable s = y.sum();

        backward(s);
        REQUIRE(y.ndimension() == 4);
        REQUIRE(s.ndimension() == 0);
        for (auto i = 0; i < 4; i++) {
          REQUIRE(y.size(i) == 2);
        }

        REQUIRE(
            model->parameters().at("weight").grad().numel() == 3 * 2 * 3 * 2);
      }
    }
    SECTION("3d") {
      auto model = Conv3d(3, 2, 3).stride(2).build();
      auto x = Var(at::CPU(at::kFloat).randn({2, 3, 5, 5, 5}), true);
      auto y = model->forward({x})[0];
      Variable s = y.sum();

      backward(s);
      REQUIRE(y.ndimension() == 5);
      REQUIRE(s.ndimension() == 0);
      for (auto i = 0; i < 5; i++) {
        REQUIRE(y.size(i) == 2);
      }

      REQUIRE(
          model->parameters().at("weight").grad().numel() == 3 * 2 * 3 * 3 * 3);
    }
  }
  SECTION("linear") {
    SECTION("basic1") {
      auto model = Linear(5, 2).build();
      auto x = Var(at::CPU(at::kFloat).randn({10, 5}), true);
      auto y = model->forward({x})[0];
      Variable s = y.sum();

      backward(s);
      REQUIRE(y.ndimension() == 2);
      REQUIRE(s.ndimension() == 0);
      REQUIRE(y.size(0) == 10);
      REQUIRE(y.size(1) == 2);

      REQUIRE(model->parameters().at("weight").grad().numel() == 2 * 5);
    }

    SECTION("sequential") {
      auto model = std::make_shared<ContainerList>();
      model->append(Linear(10, 3).build());
      model->append(Linear(3, 5).build());
      model->append(Linear(5, 100).build());

      auto x = Var(at::CPU(at::kFloat).randn({1000, 10}));
      for (auto layer : *model) {
        x = layer->forward({x})[0];
        x = x.clamp_min(0); // relu
      }

      backward(x);
      REQUIRE(x.ndimension() == 2);
      REQUIRE(x.size(0) == 1000);
      REQUIRE(x.size(1) == 100);
      REQUIRE(x.data().min().toCFloat() == 0);
    }

    SECTION("simple") {
      auto model = std::make_shared<Sequential>();
      auto l1 = model->add(Linear(10, 3).build(), "l1");
      auto l2 = model->add(Linear(3, 5).build(), "l2");
      auto l3 = model->add(Linear(5, 100).build(), "l3");

      auto x = Var(at::CPU(at::kFloat).randn({1000, 10}));
      x = l1->forward({x})[0].clamp_min(0);
      x = l2->forward({x})[0].clamp_min(0);
      x = l3->forward({x})[0].clamp_min(0);

      backward(x);
      REQUIRE(x.ndimension() == 2);
      REQUIRE(x.size(0) == 1000);
      REQUIRE(x.size(1) == 100);
      REQUIRE(x.data().min().toCFloat() == 0);
    }
  }

  SECTION("embedding") {
    SECTION("basic") {
      int dict_size = 10;
      auto model = Embedding(dict_size, 2).build();
      // Cannot get gradients to change indices (input) - only for embedding
      // params
      auto x = Var(at::CPU(at::kLong).tensor({10}).fill_(dict_size - 1), false);
      auto y = model->forward({x})[0];
      Variable s = y.sum();

      backward(s);
      REQUIRE(y.ndimension() == 2);
      REQUIRE(s.ndimension() == 0);
      REQUIRE(y.size(0) == 10);
      REQUIRE(y.size(1) == 2);

      REQUIRE(model->parameters().at("table").grad().numel() == 2 * dict_size);
    }

    SECTION("list") {
      auto model = Embedding(6, 4).build();
      auto x = Var(at::CPU(at::kLong).tensor({2, 3}).fill_(5), false);
      auto y = model->forward({x})[0];
      Variable s = y.sum();

      backward(s);
      REQUIRE(y.ndimension() == 3);
      REQUIRE(y.size(0) == 2);
      REQUIRE(y.size(1) == 3);
      REQUIRE(y.size(2) == 4);
    }
  }

  SECTION("dropout") {
    auto dropout = Dropout(0.5).build();
    Variable x = Var(at::CPU(at::kFloat).ones(100));
    Variable y = dropout->forward({x})[0];

    backward(y);
    REQUIRE(y.ndimension() == 1);
    REQUIRE(y.size(0) == 100);
    // TODO: These two tests are flaky
    // https://github.com/pytorch/pytorch/issues/7286
    // REQUIRE(y.sum().toCFloat() < 130); // Probably
    // REQUIRE(y.sum().toCFloat() > 70); // Probably

    dropout->eval();
    y = dropout->forward({x})[0];
    REQUIRE(y.data().sum().toCFloat() == 100);
  }

  SECTION("param") {
    auto model = std::make_shared<NestedModel>();
    REQUIRE(model->param("param").size(0) == 3);
    REQUIRE(model->param("param").size(1) == 2);
    REQUIRE(model->param("param").size(2) == 21);
    REQUIRE(model->param("l1.bias").size(0) == 20);
    REQUIRE(model->param("l1.weight").size(0) == 20);
    REQUIRE(model->param("l1.weight").size(1) == 5);
    REQUIRE(model->param("test.l1.bias").size(0) == 3);
    REQUIRE(model->param("test.l1.weight").size(0) == 3);
    REQUIRE(model->param("test.l1.weight").size(1) == 10);
    REQUIRE(model->param("test.l2.bias").size(0) == 5);
    REQUIRE(model->param("test.l2.weight").size(0) == 5);
    REQUIRE(model->param("test.l2.weight").size(1) == 3);
    REQUIRE(model->param("test.l3.bias").size(0) == 100);
    REQUIRE(model->param("test.l3.weight").size(0) == 100);
    REQUIRE(model->param("test.l3.weight").size(1) == 5);
  }

  SECTION("functional") {
    bool was_called = false;
    // clang-format off
    auto functional = Functional([&was_called](variable_list input) {
      was_called = true;
      return input;
    }).build();
    // clang-format on
    auto output = functional->forward({Var(at::CPU(at::kFloat).ones(5))});
    REQUIRE(was_called);
    REQUIRE(output.size() == 1);
    REQUIRE(output.front().equal(Var(at::CPU(at::kFloat).ones(5))));
  }
}

TEST_CASE("containers_cuda", "[cuda]") {
  SECTION("1") {
    auto model = Linear(5, 2).build();
    model->cuda();
    auto x = Var(at::CUDA(at::kFloat).randn({10, 5}), true);
    auto y = model->forward({x})[0];
    Variable s = y.sum();

    backward(s);
    REQUIRE(y.ndimension() == 2);
    REQUIRE(s.ndimension() == 0);
    REQUIRE(y.size(0) == 10);
    REQUIRE(y.size(1) == 2);

    REQUIRE(model->parameters().at("weight").grad().numel() == 2 * 5);
  }

  SECTION("2") {
    auto model = Linear(5, 2).build();
    model->cuda();
    model->cpu();
    auto x = Var(at::CPU(at::kFloat).randn({10, 5}), true);
    auto y = model->forward({x})[0];
    Variable s = y.sum();

    backward(s);
    REQUIRE(y.ndimension() == 2);
    REQUIRE(s.ndimension() == 0);
    REQUIRE(y.size(0) == 10);
    REQUIRE(y.size(1) == 2);

    REQUIRE(model->parameters().at("weight").grad().numel() == 2 * 5);
  }
}