/*
 * Copyright (c) Facebook, Inc. and its affiliates.
 * All rights reserved.
 *
 * This source code is licensed under the BSD-style license found in the
 * LICENSE file in the root directory of this source tree.
 */

#pragma once

#include <algorithm>
#include <cassert>
#include <cmath>
#include <cstddef>
#include <cstdlib>
#include <functional>
#include <memory>
#include <random>
#include <vector>

#include <pytorch_qnnpack.h>
#include <qnnpack_func.h>

#include "test_utils.h"

class DeconvolutionOperatorTester {
 public:
  inline DeconvolutionOperatorTester& padding(uint32_t padding) {
    this->paddingHeight_ = padding;
    this->paddingWidth_ = padding;
    return *this;
  }

  inline DeconvolutionOperatorTester& padding(
      uint32_t paddingHeight,
      uint32_t paddingWidth) {
    this->paddingHeight_ = paddingHeight;
    this->paddingWidth_ = paddingWidth;
    return *this;
  }

  inline DeconvolutionOperatorTester& paddingHeight(uint32_t paddingHeight) {
    this->paddingHeight_ = paddingHeight;
    return *this;
  }

  inline uint32_t paddingHeight() const {
    return this->paddingHeight_;
  }

  inline DeconvolutionOperatorTester& paddingWidth(uint32_t paddingWidth) {
    this->paddingWidth_ = paddingWidth;
    return *this;
  }

  inline uint32_t paddingWidth() const {
    return this->paddingWidth_;
  }

  inline DeconvolutionOperatorTester& adjustmentHeight(
      uint32_t adjustmentHeight) {
    this->adjustmentHeight_ = adjustmentHeight;
    return *this;
  }

  inline uint32_t adjustmentHeight() const {
    return this->adjustmentHeight_;
  }

  inline DeconvolutionOperatorTester& adjustmentWidth(
      uint32_t adjustmentWidth) {
    this->adjustmentWidth_ = adjustmentWidth;
    return *this;
  }

  inline uint32_t adjustmentWidth() const {
    return this->adjustmentWidth_;
  }

  inline DeconvolutionOperatorTester& inputSize(
      uint32_t inputHeight,
      uint32_t inputWidth) {
    assert(inputHeight >= 1);
    assert(inputWidth >= 1);
    this->inputHeight_ = inputHeight;
    this->inputWidth_ = inputWidth;
    return *this;
  }

  inline DeconvolutionOperatorTester& inputHeight(uint32_t inputHeight) {
    assert(inputHeight >= 1);
    this->inputHeight_ = inputHeight;
    return *this;
  }

  inline uint32_t inputHeight() const {
    return this->inputHeight_;
  }

  inline DeconvolutionOperatorTester& inputWidth(uint32_t inputWidth) {
    assert(inputWidth >= 1);
    this->inputWidth_ = inputWidth;
    return *this;
  }

  inline uint32_t inputWidth() const {
    return this->inputWidth_;
  }

  inline DeconvolutionOperatorTester& groups(uint32_t groups) {
    assert(groups >= 1);
    this->groups_ = groups;
    return *this;
  }

  inline uint32_t groups() const {
    return this->groups_;
  }

  inline DeconvolutionOperatorTester& groupInputChannels(
      size_t groupInputChannels) {
    assert(groupInputChannels >= 1);
    this->groupInputChannels_ = groupInputChannels;
    return *this;
  }

  inline size_t groupInputChannels() const {
    return this->groupInputChannels_;
  }

  inline DeconvolutionOperatorTester& per_channel(bool per_channel) {
    this->per_channel_ = per_channel;
    return *this;
  }

  inline bool per_channel() const {
    return this->per_channel_;
  }

  inline DeconvolutionOperatorTester& groupOutputChannels(
      size_t groupOutputChannels) {
    assert(groupOutputChannels >= 1);
    this->groupOutputChannels_ = groupOutputChannels;
    return *this;
  }

  inline size_t groupOutputChannels() const {
    return this->groupOutputChannels_;
  }

  inline DeconvolutionOperatorTester& batchSize(size_t batchSize) {
    this->batchSize_ = batchSize;
    return *this;
  }

  inline size_t batchSize() const {
    return this->batchSize_;
  }

  inline DeconvolutionOperatorTester& kernelSize(uint32_t kernelSize) {
    assert(kernelSize >= 1);
    this->kernelHeight_ = kernelSize;
    this->kernelWidth_ = kernelSize;
    return *this;
  }

  inline DeconvolutionOperatorTester& kernelSize(
      uint32_t kernelHeight,
      uint32_t kernelWidth) {
    assert(kernelHeight >= 1);
    assert(kernelWidth >= 1);
    this->kernelHeight_ = kernelHeight;
    this->kernelWidth_ = kernelWidth;
    return *this;
  }

  inline DeconvolutionOperatorTester& kernelHeight(uint32_t kernelHeight) {
    assert(kernelHeight >= 1);
    this->kernelHeight_ = kernelHeight;
    return *this;
  }

  inline uint32_t kernelHeight() const {
    return this->kernelHeight_;
  }

  inline DeconvolutionOperatorTester& kernelWidth(uint32_t kernelWidth) {
    assert(kernelWidth >= 1);
    this->kernelWidth_ = kernelWidth;
    return *this;
  }

  inline uint32_t kernelWidth() const {
    return this->kernelWidth_;
  }

  inline DeconvolutionOperatorTester& dilation(uint32_t dilation) {
    assert(dilation >= 1);
    this->dilationHeight_ = dilation;
    this->dilationWidth_ = dilation;
    return *this;
  }

  inline DeconvolutionOperatorTester& dilation(
      uint32_t dilationHeight,
      uint32_t dilationWidth) {
    assert(dilationHeight >= 1);
    assert(dilationWidth >= 1);
    this->dilationHeight_ = dilationHeight;
    this->dilationWidth_ = dilationWidth;
    return *this;
  }

  inline DeconvolutionOperatorTester& dilationHeight(uint32_t dilationHeight) {
    assert(dilationHeight >= 1);
    this->dilationHeight_ = dilationHeight;
    return *this;
  }

  inline uint32_t dilationHeight() const {
    return this->dilationHeight_;
  }

  inline DeconvolutionOperatorTester& dilationWidth(uint32_t dilationWidth) {
    assert(dilationWidth >= 1);
    this->dilationWidth_ = dilationWidth;
    return *this;
  }

  inline uint32_t dilationWidth() const {
    return this->dilationWidth_;
  }

  inline DeconvolutionOperatorTester& stride(uint32_t stride) {
    assert(stride >= 1);
    this->strideHeight_ = stride;
    this->strideWidth_ = stride;
    return *this;
  }

  inline DeconvolutionOperatorTester& stride(
      uint32_t strideHeight,
      uint32_t strideWidth) {
    assert(strideHeight >= 1);
    assert(strideWidth >= 1);
    this->strideHeight_ = strideHeight;
    this->strideWidth_ = strideWidth;
    return *this;
  }

  inline DeconvolutionOperatorTester& strideHeight(uint32_t strideHeight) {
    assert(strideHeight >= 1);
    this->strideHeight_ = strideHeight;
    return *this;
  }

  inline uint32_t strideHeight() const {
    return this->strideHeight_;
  }

  inline DeconvolutionOperatorTester& strideWidth(uint32_t strideWidth) {
    assert(strideWidth >= 1);
    this->strideWidth_ = strideWidth;
    return *this;
  }

  inline uint32_t strideWidth() const {
    return this->strideWidth_;
  }

  inline DeconvolutionOperatorTester& inputPixelStride(
      size_t inputPixelStride) {
    assert(inputPixelStride >= 1);
    this->inputPixelStride_ = inputPixelStride;
    return *this;
  }

  inline size_t inputPixelStride() const {
    if (this->inputPixelStride_ == 0) {
      return groupInputChannels() * groups();
    } else {
      assert(this->inputPixelStride_ >= groupInputChannels() * groups());
      return this->inputPixelStride_;
    }
  }

  inline DeconvolutionOperatorTester& outputPixelStride(
      size_t outputPixelStride) {
    assert(outputPixelStride >= 1);
    this->outputPixelStride_ = outputPixelStride;
    return *this;
  }

  inline size_t outputPixelStride() const {
    if (this->outputPixelStride_ == 0) {
      return groupOutputChannels() * groups();
    } else {
      assert(this->outputPixelStride_ >= groupOutputChannels() * groups());
      return this->outputPixelStride_;
    }
  }

  inline uint32_t dilatedKernelHeight() const {
    return (kernelHeight() - 1) * dilationHeight() + 1;
  }

  inline uint32_t dilatedKernelWidth() const {
    return (kernelWidth() - 1) * dilationWidth() + 1;
  }

  inline size_t outputHeight() const {
    return strideHeight() * (inputHeight() - 1) + adjustmentHeight() +
        dilatedKernelHeight() - paddingHeight() * 2;
  }

  inline size_t outputWidth() const {
    return strideWidth() * (inputWidth() - 1) + adjustmentWidth() +
        dilatedKernelWidth() - paddingWidth() * 2;
  }

  inline DeconvolutionOperatorTester& qmin(uint8_t qmin) {
    this->qmin_ = qmin;
    return *this;
  }

  inline uint8_t qmin() const {
    return this->qmin_;
  }

  inline DeconvolutionOperatorTester& qmax(uint8_t qmax) {
    this->qmax_ = qmax;
    return *this;
  }

  inline uint8_t qmax() const {
    return this->qmax_;
  }

  inline DeconvolutionOperatorTester& iterations(size_t iterations) {
    this->iterations_ = iterations;
    return *this;
  }

  inline size_t iterations() const {
    return this->iterations_;
  }

  void testQ8(const qnnpack::testing::Mode mode = qnnpack::testing::Mode::Static) const {
    std::random_device randomDevice;
    auto rng = std::mt19937(randomDevice());
    auto s32rng =
        std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), rng);
    auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng);

    std::vector<uint8_t> input(
        batchSize() *
            ((inputHeight() * inputWidth() - 1) * inputPixelStride() +
             groups() * groupInputChannels()) +
        8);
    std::vector<uint8_t> kernel(
        groups() * groupOutputChannels() * kernelHeight() * kernelWidth() *
        groupInputChannels());
    std::vector<int32_t> bias(groups() * groupOutputChannels());
    std::vector<uint8_t> output(
        batchSize() *
        ((outputHeight() * outputWidth() - 1) * outputPixelStride() +
         groups() * groupOutputChannels()));
    std::vector<int32_t> accumulators(
        batchSize() * outputHeight() * outputWidth() * groups() *
        groupOutputChannels());

    const uint8_t* inputPtr = input.data() + 8;
    const uint8_t inputZeroPoint = 127;
    // Make num zero points multiple of 8.
    // This is the least common denominator for SSE/ARM kernels we have.
    size_t num_zero_points_padded =
      groups() * groupOutputChannels() + 8;
    std::vector<uint8_t> kernelZeroPoints(num_zero_points_padded, 127);


    for (size_t iteration = 0; iteration < iterations(); iteration++) {
      std::generate(input.begin(), input.end(), std::ref(u8rng));
      std::generate(kernel.begin(), kernel.end(), std::ref(u8rng));
      std::generate(bias.begin(), bias.end(), std::ref(s32rng));
      if (per_channel()) {
        std::generate(kernelZeroPoints.begin(), kernelZeroPoints.end(), std::ref(u8rng));
      }
      std::fill(output.begin(), output.end(), 0xA5);
      std::fill(accumulators.begin(), accumulators.end(), 0);

      for (size_t i = 0; i < batchSize(); i++) {
        for (size_t oy = 0; oy < outputHeight(); oy++) {
          for (size_t ox = 0; ox < outputWidth(); ox++) {
            for (size_t g = 0; g < groups(); g++) {
              for (size_t oc = 0; oc < groupOutputChannels(); oc++) {
                accumulators
                    [(((i * outputHeight() + oy) * outputWidth() + ox) *
                          groups() +
                      g) *
                         groupOutputChannels() +
                     oc] = bias[g * groupOutputChannels() + oc];
              }
            }
          }
        }
      }
      for (size_t i = 0; i < batchSize(); i++) {
        for (size_t oy = 0; oy < outputHeight(); oy++) {
          for (size_t ox = 0; ox < outputWidth(); ox++) {
            for (size_t ky = 0; ky < kernelHeight(); ky++) {
              const size_t y = oy + paddingHeight() - ky * dilationHeight();
              const size_t iy = y / strideHeight();
              if (iy * strideHeight() == y && iy < inputHeight()) {
                for (size_t kx = 0; kx < kernelWidth(); kx++) {
                  const size_t x = ox + paddingWidth() - kx * dilationWidth();
                  const size_t ix = x / strideWidth();
                  if (ix * strideWidth() == x && ix < inputWidth()) {
                    for (size_t g = 0; g < groups(); g++) {
                      for (size_t oc = 0; oc < groupOutputChannels(); oc++) {
                        for (size_t ic = 0; ic < groupInputChannels(); ic++) {
                          accumulators
                              [(((i * outputHeight() + oy) * outputWidth() +
                                 ox) *
                                    groups() +
                                g) *
                                   groupOutputChannels() +
                               oc] +=
                              (int32_t(inputPtr
                                           [((i * inputHeight() + iy) *
                                                 inputWidth() +
                                             ix) *
                                                inputPixelStride() +
                                            g * groupInputChannels() + ic]) -
                               int32_t(inputZeroPoint)) *
                              (int32_t(kernel
                                           [(((g * groupInputChannels() + ic) *
                                                  kernelHeight() +
                                              ky) *
                                                 kernelWidth() +
                                             kx) *
                                                groupOutputChannels() +
                                            oc]) -
                               int32_t(kernelZeroPoints[g* groupOutputChannels() + oc]));
                        }
                      }
                    }
                  }
                }
              }
            }
          }
        }
      }
      // Create dummy min/max for empty inputs.
      // These are only used to compute scale and zero point,
      // and real callers will just pull those values from the model.
      const int32_t accumulatorsMin = accumulators.empty()
          ? 0
          : *std::min_element(accumulators.cbegin(), accumulators.cend());
      const int32_t accumulatorsMax = accumulators.empty()
          ? 900
          : *std::max_element(accumulators.cbegin(), accumulators.cend());

      const double outputScale =
          double(uint32_t(accumulatorsMax - accumulatorsMin)) / 255.0;
      const uint8_t outputZeroPoint = uint8_t(std::max(
          std::min(
              lrint(
                  127.5 -
                  0.5 * double(accumulatorsMin + accumulatorsMax) /
                      outputScale),
              long(std::numeric_limits<uint8_t>::max())),
          long(std::numeric_limits<uint8_t>::min())));

      ASSERT_EQ(pytorch_qnnp_status_success, pytorch_qnnp_initialize());
      std::vector<float> requantization_scales(num_zero_points_padded, 1.0 * 1.0 / outputScale);
      auto f32rng =
          std::bind(std::uniform_real_distribution<float>(1, 5), rng);
      if (per_channel()) {
        auto scale_generator = [&]() -> float {return (f32rng()/outputScale);};
        std::generate(
            requantization_scales.begin(),
            requantization_scales.end(),
            std::ref(scale_generator));
      }
      pytorch_qnnp_operator_t deconvolution = nullptr;

      ASSERT_EQ(
          pytorch_qnnp_status_success,
          pytorch_qnnp_create_deconvolution2d_nhwc_q8(
              paddingHeight(),
              paddingWidth(),
              adjustmentHeight(),
              adjustmentWidth(),
              kernelHeight(),
              kernelWidth(),
              strideHeight(),
              strideWidth(),
              dilationHeight(),
              dilationWidth(),
              groups(),
              groupInputChannels(),
              groupOutputChannels(),
              inputZeroPoint,
              kernelZeroPoints.data(),
              kernel.data(),
              bias.data(),
              outputZeroPoint,
              qmin(),
              qmax(),
              0,
              requantization_scales.data(),
              &deconvolution));

      switch (mode) {
        case qnnpack::testing::Mode::Static: {
          ASSERT_EQ(
              pytorch_qnnp_status_success,
              pytorch_qnnp_setup_deconvolution2d_nhwc_q8(
                  deconvolution,
                  batchSize(),
                  inputHeight(),
                  inputWidth(),
                  inputPtr,
                  inputPixelStride(),
                  output.data(),
                  outputPixelStride(),
                  nullptr /* thread pool */));

          ASSERT_EQ(
              pytorch_qnnp_status_success,
              pytorch_qnnp_run_operator(deconvolution, nullptr /* thread pool */));

          ASSERT_EQ(
              pytorch_qnnp_status_success,
              pytorch_qnnp_delete_operator(deconvolution));
          deconvolution = nullptr;
        } break;

        case qnnpack::testing::Mode::Runtime:
        {
          auto packW = std::unique_ptr<qnnpack::PrePackConvWeights>(
              new qnnpack::PrePackConvWeights(
                  deconvolution,
                  kernelZeroPoints.data(),
                  kernel.data(),
                  bias.data()));
          ASSERT_EQ(
              pytorch_qnnp_status_success,
              qnnpack::qnnpackDeConv(
                  deconvolution,
                  packW->getPackedWeights(),
                  batchSize(),
                  inputHeight(),
                  inputWidth(),
                  inputZeroPoint,
                  inputPtr,
                  kernelZeroPoints.data(),
                  requantization_scales.data(),
                  outputZeroPoint,
                  qmin(),
                  qmax(),
                  output.data(),
                  nullptr));
        }
        break;

        default:
          ASSERT_TRUE(false);
      }

      for (size_t i = 0; i < batchSize(); i++) {
        for (size_t y = 0; y < outputHeight(); y++) {
          for (size_t x = 0; x < outputWidth(); x++) {
            for (size_t g = 0; g < groups(); g++) {
              for (size_t c = 0; c < groupOutputChannels(); c++) {
                const double scaledAccumulator =
                    accumulators
                        [(((i * outputHeight() + y) * outputWidth() + x) *
                              groups() +
                          g) *
                             groupOutputChannels() +
                         c] *
                         requantization_scales[g * groupOutputChannels() + c];
                const double clampedAccumulator = std::max(
                    std::min(
                        scaledAccumulator,
                        double(qmax()) - double(outputZeroPoint)),
                    double(qmin()) - double(outputZeroPoint));
                ASSERT_NEAR(
                    clampedAccumulator,
                    (int32_t(
                         output
                             [((i * outputHeight() + y) * outputWidth() + x) *
                                  outputPixelStride() +
                              g * groupOutputChannels() + c]) -
                     outputZeroPoint),
                    0.9)
                    << "(x, y) = (" << x << ", " << y << "), group = " << g
                    << ", channel = " << c;
                ASSERT_LE(
                    double(
                        int32_t(output
                                    [((i * outputHeight() + y) * outputWidth() +
                                      x) *
                                         outputPixelStride() +
                                     g * groupOutputChannels() + c]) -
                        outputZeroPoint),
                    double(qmax()) - double(outputZeroPoint))
                    << "(x, y) = (" << x << ", " << y << "), group = " << g
                    << ", channel = " << c;
                ASSERT_GE(
                    double(
                        int32_t(output
                                    [((i * outputHeight() + y) * outputWidth() +
                                      x) *
                                         outputPixelStride() +
                                     g * groupOutputChannels() + c]) -
                        outputZeroPoint),
                    double(qmin()) - double(outputZeroPoint))
                    << "(x, y) = (" << x << ", " << y << "), group = " << g
                    << ", channel = " << c;
              }
            }
          }
        }
      }
    }
  }

 private:
  uint32_t paddingHeight_{0};
  uint32_t paddingWidth_{0};
  size_t inputHeight_{1};
  size_t inputWidth_{1};
  uint32_t groups_{1};
  size_t groupInputChannels_{1};
  size_t inputPixelStride_{0};
  size_t groupOutputChannels_{1};
  size_t outputPixelStride_{0};
  size_t batchSize_{1};
  uint32_t kernelHeight_{1};
  uint32_t kernelWidth_{1};
  uint32_t adjustmentHeight_{0};
  uint32_t adjustmentWidth_{0};
  uint32_t dilationHeight_{1};
  uint32_t dilationWidth_{1};
  uint32_t strideHeight_{1};
  uint32_t strideWidth_{1};
  uint8_t qmin_{0};
  uint8_t qmax_{255};
  size_t iterations_{1};
  bool per_channel_{false};
};