#include "fesa/math/sparse_matrix.h"

#include <gtest/gtest.h>

#include <algorithm>
#include <cmath>
#include <future>
#include <limits>
#include <type_traits>
#include <utility>
#include <vector>

#include "fesa/fem/dof_manager.h"
#include "fesa/math/matrix.h"

namespace {

using fesa::CooContribution;
using fesa::SparseMatrix;
using fesa::SparsePattern;
using fesa::Vector;

static_assert(
    !std::is_base_of_v<fesa::Matrix, SparseMatrix>,
    "SparseMatrix must own CSR storage independently of dense Matrix.");

TEST(SparseAssembly, ValidatesKnownCsrAndMultiply) {
  const SparsePattern pattern{{0U, 2U, 2U, 4U, 5U}, {0U, 2U, 1U, 3U, 3U}};
  std::vector<CooContribution> contributions{{2U, 3U, 4.0, 2U, 0U},
                                             {0U, 2U, 2.0, 0U, 1U},
                                             {3U, 3U, 5.0, 3U, 0U},
                                             {0U, 0U, 1.0, 0U, 0U},
                                             {2U, 1U, 3.0, 1U, 0U}};

  auto result =
      SparseMatrix::FromCoo(4U, 4U, std::move(contributions), pattern);
  ASSERT_TRUE(result.HasValue());
  const SparseMatrix& matrix = result.Value();

  EXPECT_EQ(matrix.Rows(), 4U);
  EXPECT_EQ(matrix.Columns(), 4U);
  EXPECT_EQ(matrix.RowOffsets(), pattern.row_offsets);
  EXPECT_EQ(matrix.ColumnIndices(), pattern.column_indices);
  EXPECT_EQ(matrix.Values(), (std::vector<double>{1.0, 2.0, 3.0, 4.0, 5.0}));
  EXPECT_TRUE(matrix.Validate().IsOk());

  Vector rhs{4U};
  rhs[0U] = 1.0;
  rhs[1U] = 2.0;
  rhs[2U] = 3.0;
  rhs[3U] = 4.0;
  const Vector product = matrix.Multiply(rhs);
  ASSERT_EQ(product.Size(), 4U);
  EXPECT_DOUBLE_EQ(product[0U], 7.0);
  EXPECT_DOUBLE_EQ(product[1U], 0.0);
  EXPECT_DOUBLE_EQ(product[2U], 22.0);
  EXPECT_DOUBLE_EQ(product[3U], 20.0);
  EXPECT_THROW(static_cast<void>(matrix.Multiply(Vector{3U})),
               std::invalid_argument);
}

TEST(SparseAssembly, ReducesDuplicatesInFixedTupleOrder) {
  const SparsePattern pattern{{0U, 1U}, {0U}};
  const std::vector<CooContribution> contributions{{0U, 0U, 1.0, 2U, 0U},
                                                   {0U, 0U, -1.0e16, 1U, 0U},
                                                   {0U, 0U, 1.0e16, 0U, 0U}};

  auto first = SparseMatrix::FromCoo(1U, 1U, contributions, pattern);
  ASSERT_TRUE(first.HasValue());
  ASSERT_EQ(first.Value().Values().size(), 1U);
  EXPECT_DOUBLE_EQ(first.Value().Values()[0U], 1.0);

  auto reversed_contributions = contributions;
  std::reverse(reversed_contributions.begin(), reversed_contributions.end());
  auto second =
      SparseMatrix::FromCoo(1U, 1U, std::move(reversed_contributions), pattern);
  ASSERT_TRUE(second.HasValue());
  EXPECT_EQ(second.Value().RowOffsets(), first.Value().RowOffsets());
  EXPECT_EQ(second.Value().ColumnIndices(), first.Value().ColumnIndices());
  EXPECT_EQ(second.Value().Values(), first.Value().Values());
}

TEST(SparseAssembly, RejectsInvalidIndexPatternAndShape) {
  const SparsePattern one_entry{{0U, 1U}, {0U}};
  const auto expect_failure = [](std::size_t rows, std::size_t columns,
                                 std::vector<CooContribution> contributions,
                                 const SparsePattern& pattern) {
    auto result =
        SparseMatrix::FromCoo(rows, columns, std::move(contributions), pattern);
    EXPECT_FALSE(result.HasValue());
    if (!result.HasValue()) {
      EXPECT_FALSE(result.GetStatus().IsOk());
      EXPECT_EQ(result.GetStatus().Category(), fesa::FailureCategory::kModel);
      EXPECT_FALSE(result.GetStatus().Diagnostics().empty());
    }
  };

  expect_failure(2U, 2U, {}, {{0U, 0U}, {}});
  expect_failure(1U, 1U, {}, {{1U, 1U}, {0U}});
  expect_failure(2U, 2U, {}, {{0U, 1U, 0U}, {0U}});
  expect_failure(1U, 2U, {}, {{0U, 2U}, {1U, 0U}});
  expect_failure(1U, 1U, {}, {{0U, 2U}, {0U, 0U}});
  expect_failure(1U, 1U, {}, {{0U, 1U}, {1U}});
  expect_failure(1U, 1U, {{1U, 0U, 1.0, 0U, 0U}}, one_entry);
  expect_failure(1U, 1U, {{0U, 1U, 1.0, 0U, 0U}}, one_entry);
  expect_failure(1U, 2U, {{0U, 1U, 1.0, 0U, 0U}}, {{0U, 1U}, {0U}});
  expect_failure(1U, 1U,
                 {{0U, 0U, (std::numeric_limits<double>::infinity)(), 0U, 0U}},
                 one_entry);
  expect_failure(1U, 1U,
                 {{0U, 0U, (std::numeric_limits<double>::quiet_NaN)(), 0U, 0U}},
                 one_entry);
  expect_failure(1U, 1U,
                 {{0U, 0U, (std::numeric_limits<double>::max)(), 0U, 0U},
                  {0U, 0U, (std::numeric_limits<double>::max)(), 1U, 0U}},
                 one_entry);
}

TEST(SparseAssembly, PreservesExpectedStructuralZeros) {
  const SparsePattern pattern{{0U, 2U, 4U, 5U}, {0U, 2U, 1U, 2U, 0U}};
  std::vector<CooContribution> contributions{
      {0U, 0U, 2.0, 0U, 0U}, {1U, 1U, 4.0, 0U, 1U}, {1U, 1U, -4.0, 1U, 0U}};

  auto result =
      SparseMatrix::FromCoo(3U, 3U, std::move(contributions), pattern);
  ASSERT_TRUE(result.HasValue());
  EXPECT_EQ(result.Value().RowOffsets(), pattern.row_offsets);
  EXPECT_EQ(result.Value().ColumnIndices(), pattern.column_indices);
  EXPECT_EQ(result.Value().Values(),
            (std::vector<double>{2.0, 0.0, 0.0, 0.0, 0.0}));
  EXPECT_EQ(std::count(result.Value().Values().begin(),
                       result.Value().Values().end(), 0.0),
            4);
}

TEST(SparseAssembly, MultipliesRectangularCopyMoveAndConcurrentConstCalls) {
  auto assembled = SparseMatrix::FromCoo(
      3U, 5U, {{0U, 4U, 3.0, 0U, 0U}, {2U, 1U, -2.0, 1U, 0U}},
      {{0U, 1U, 1U, 2U}, {4U, 1U}});
  ASSERT_TRUE(assembled.HasValue());
  const auto original = assembled.Value();
  auto copied = original;
  auto moved = std::move(copied);
  Vector rhs{5U, 2.0};
  const auto evaluate = [&rhs](const SparseMatrix& matrix) {
    for (int repeat = 0; repeat < 20; ++repeat) {
      const auto product = matrix.Multiply(rhs);
      if (product.Size() != 3U || product[0U] != 6.0 ||
          product[1U] != 0.0 || product[2U] != -4.0) return false;
    }
    return true;
  };
  auto first = std::async(std::launch::async, [&] { return evaluate(moved); });
  auto second = std::async(std::launch::async, [&] { return evaluate(moved); });
  EXPECT_TRUE(first.get());
  EXPECT_TRUE(second.get());
  EXPECT_TRUE(evaluate(original));
  auto replacement = SparseMatrix::FromCoo(
      1U, 1U, {{0U, 0U, 7.0, 0U, 0U}}, {{0U, 1U}, {0U}});
  ASSERT_TRUE(replacement.HasValue());
  moved = replacement.Value();
  EXPECT_DOUBLE_EQ(moved.Multiply(Vector{1U, 3.0})[0U], 21.0);
  EXPECT_TRUE(evaluate(original));
}

TEST(SparseAssembly, PreservesZeroShapesAndEmptyRows) {
  auto no_rows = SparseMatrix::FromCoo(0U, 3U, {}, {{0U}, {}});
  auto no_columns = SparseMatrix::FromCoo(2U, 0U, {}, {{0U, 0U, 0U}, {}});
  auto no_entries = SparseMatrix::FromCoo(2U, 3U, {}, {{0U, 0U, 0U}, {}});
  ASSERT_TRUE(no_rows.HasValue());
  ASSERT_TRUE(no_columns.HasValue());
  ASSERT_TRUE(no_entries.HasValue());
  EXPECT_EQ(no_rows.Value().Multiply(Vector{3U}).Size(), 0U);
  const auto empty_columns = no_columns.Value().Multiply(Vector{0U});
  const auto empty_entries = no_entries.Value().Multiply(Vector{3U});
  ASSERT_EQ(empty_columns.Size(), 2U);
  ASSERT_EQ(empty_entries.Size(), 2U);
  for (std::size_t row = 0; row < 2U; ++row) {
    EXPECT_DOUBLE_EQ(empty_columns[row], 0.0);
    EXPECT_DOUBLE_EQ(empty_entries[row], 0.0);
  }
}

TEST(SparseAssembly, RejectsNonfiniteRhsAndRecoversAfterFiniteOverflow) {
  auto matrix = SparseMatrix::FromCoo(
      2U, 2U, {{0U, 0U, 2.0, 0U, 0U}}, {{0U, 1U, 2U}, {0U, 1U}});
  ASSERT_TRUE(matrix.HasValue());
  Vector rhs{2U, (std::numeric_limits<double>::infinity)()};
  EXPECT_THROW(static_cast<void>(matrix.Value().Multiply(rhs)),
               std::invalid_argument);
  rhs[1U] = 4.0;
  rhs[0U] = (std::numeric_limits<double>::max)();
  EXPECT_TRUE(std::isinf(matrix.Value().Multiply(rhs)[0U]));
  rhs[0U] = (std::numeric_limits<double>::quiet_NaN)();
  EXPECT_THROW(static_cast<void>(matrix.Value().Multiply(rhs)),
               std::invalid_argument);
  EXPECT_THROW(static_cast<void>(matrix.Value().Multiply(Vector{1U})),
               std::invalid_argument);
  rhs[0U] = 3.0;
  rhs[1U] = 4.0;
  const auto finite = matrix.Value().Multiply(rhs);
  EXPECT_DOUBLE_EQ(finite[0U], 6.0);
  EXPECT_DOUBLE_EQ(finite[1U], 0.0);
}

TEST(SparseAssembly, MatchesIndependentLongDoubleResidualOnUnevenRows) {
  const std::vector<double> entries{1.25, -0.375, 4.5, -3.0, 0.125, 2.0};
  const SparsePattern pattern{{0U, 3U, 3U, 5U, 6U}, {0U, 2U, 4U, 1U, 3U, 2U}};
  std::vector<CooContribution> contributions;
  for (std::size_t row = 0; row < 4U; ++row) {
    for (auto position = pattern.row_offsets[row];
         position < pattern.row_offsets[row + 1U]; ++position) {
      contributions.push_back(
          {row, pattern.column_indices[position], entries[position], row,
           position});
    }
  }
  auto matrix = SparseMatrix::FromCoo(4U, 5U, contributions, pattern);
  ASSERT_TRUE(matrix.HasValue());
  Vector rhs{5U};
  for (std::size_t column = 0; column < 5U; ++column) {
    rhs[column] = 0.3 + static_cast<double>(column) * 0.7;
  }
  const auto product = matrix.Value().Multiply(rhs);
  for (std::size_t row = 0; row < 4U; ++row) {
    long double expected = 0;
    for (auto position = pattern.row_offsets[row];
         position < pattern.row_offsets[row + 1U]; ++position) {
      expected += static_cast<long double>(entries[position]) *
                  rhs[pattern.column_indices[position]];
    }
    EXPECT_NEAR(product[row], static_cast<double>(expected), 2.0e-14);
  }
}

}  // namespace