// Copyright 2023 Google LLC
// SPDX-License-Identifier: Apache-2.0
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

#ifndef THIRD_PARTY_GEMMA_CPP_UTIL_TEST_UTIL_H_
#define THIRD_PARTY_GEMMA_CPP_UTIL_TEST_UTIL_H_

#include <stddef.h>
#include <stdint.h>

#include <algorithm>  // std::sort
#include <cmath>
#include <iostream>

#include "util/basics.h"  // RngStream
#include "util/mat.h"
#include "hwy/base.h"

// IWYU pragma: begin_exports
#include "hwy/nanobenchmark.h"
#include "hwy/stats.h"
#include "hwy/tests/test_util.h"  // RandomState
// IWYU pragma: end_exports

namespace gcpp {

// Excludes outliers; we might not have enough samples for a reliable mode.
HWY_INLINE double TrimmedMean(double* seconds, size_t num) {
  std::sort(seconds, seconds + num);
  double sum = 0;
  int count = 0;
  for (size_t i = num / 4; i < num / 2; ++i) {
    sum += seconds[i];
    count += 1;
  }
  HWY_DASSERT(num != 0);
  return sum / count;
}

// Returns normalized value in [-1, 1).
HWY_INLINE float RandomFloat(RngStream& rng) {
  const uint32_t exp = hwy::BitCastScalar<uint32_t>(1.0f);
  const uint32_t mantissa_mask = hwy::MantissaMask<float>();
  const uint32_t representation = exp | (rng() & mantissa_mask);
  const float f12 = hwy::BitCastScalar<float>(representation);
  HWY_DASSERT(1.0f <= f12 && f12 < 2.0f);  // exponent is 2^0, only mantissa
  const float f = (2.0f * (f12 - 1.0f)) - 1.0f;
  HWY_DASSERT(-1.0f <= f && f < 1.0f);
  return f;
}

// Returns random Gaussian (mean=0, stddev=1/3 similar to expected weights)
// using the central limit theorem. Avoid std::normal_distribution for
// consistent cross-platform output.
// TODO: use RngStream instead of RandomState.
HWY_INLINE double RandomGaussian(hwy::RandomState& rng) {
  uint64_t sum = 0;
  constexpr int kReps = 40;
  for (int rep = 0; rep < kReps; ++rep) {
    sum += hwy::Random32(&rng) & 0xFFFFF;
  }
  const double sum_f =
      static_cast<double>(sum) / static_cast<double>(0xFFFFF * kReps);
  HWY_ASSERT(0.0 <= sum_f && sum_f <= 1.0);
  const double plus_minus_1 = 2.0 * sum_f - 1.0;
  HWY_ASSERT(-1.0 <= plus_minus_1 && plus_minus_1 <= 1.0);
  // Normalize by stddev of sum of uniform random scaled to [-1, 1].
  return plus_minus_1 * std::sqrt(kReps / 3.0);
};

// Returns true if val is inside [min, max].
template <typename T>
static inline bool IsInside(T expected_min, T expected_max, T val) {
  HWY_DASSERT(expected_min <= expected_max);
  return expected_min <= val && val <= expected_max;
}

template <typename T>
static inline bool IsNear(T expected, T val, T epsilon = T{1E-6}) {
  return IsInside(expected - epsilon, expected + epsilon, val);
}

HWY_INLINE void VerifyGaussian(hwy::Stats& stats) {
  // Inputs are roughly [-1, 1] and symmetric about zero.
  HWY_ASSERT(IsNear(-1.0f, stats.Min(), 0.10f));
  HWY_ASSERT(IsNear(+1.0f, stats.Max(), 0.10f));
  HWY_ASSERT(IsInside(-2E-3, 2E-3, stats.Mean()));
  HWY_ASSERT(IsInside(-0.15, 0.15, stats.Skewness()));
  // Near-Gaussian.
  HWY_ASSERT(IsInside(0.30, 0.35, stats.StandardDeviation()));
  HWY_ASSERT(IsNear(3.0, stats.Kurtosis(), 0.3));
}

template <typename T>
void FillMatPtrT(MatPtrT<T>& mat) {
  for (int i = 0; i < mat.Rows(); ++i) {
    for (int j = 0; j < mat.Cols(); ++j) {
      mat.Row(i)[j] = hwy::Unpredictable1() * 0.01f * (i + j + 1);
    }
  }
}

template <typename T>
void PrintMatPtr(MatPtrT<T> mat) {
  for (int i = 0; i < mat.Rows(); ++i) {
    for (int j = 0; j < mat.Cols(); ++j) {
      std::cerr << mat.Row(i)[j] << " ,";
    }
    std::cerr << std::endl;
  }
};

}  // namespace gcpp

#endif  // THIRD_PARTY_GEMMA_CPP_UTIL_TEST_UTIL_H_