#include "ATen/ATen.h"
#include "ATen/NativeFunctions.h"

#include <tuple>
#include <vector>

namespace at {
namespace native {
namespace {

template<typename scalar_t>
static std::vector<int> generate_intervals(
  scalar_t sample,
  int64_t inputSize,
  int64_t outputSize,
  int64_t poolSize) {
  scalar_t alpha = static_cast<scalar_t>(inputSize - poolSize) /
    static_cast<scalar_t>(outputSize - 1);
  std::vector<int> sequence(outputSize);

  for (int i = 0; i < outputSize - 1; ++i) {
    sequence[i] =
      static_cast<int>((i + sample) * alpha) - static_cast<int>(sample * alpha);
  }
  sequence[outputSize - 1] = inputSize - poolSize;

  return sequence;
}

template<typename scalar_t>
static void fractional_max_pool3d_out_single_batch_frame(
  scalar_t* input,
  scalar_t* output,
  int64_t* indices,
  scalar_t* randomSamples,
  int64_t numPlanes,
  int64_t inputT, int64_t inputH, int64_t inputW,
  int64_t outputT, int64_t outputH, int64_t outputW,
  int64_t poolSizeT, int64_t poolSizeH, int64_t poolSizeW) {
  int64_t plane;
#pragma omp parallel for private(plane)
  for (plane = 0; plane < numPlanes; ++plane) {
    /* each plane contains 3 random samples,
       one for T, one for W, and one for H */
    scalar_t* randomSamplesForPlane = randomSamples + plane * 3;

    /* Generate interval sequence */
    auto sequenceT = generate_intervals<scalar_t>(
        randomSamplesForPlane[0], inputT, outputT, poolSizeT);
    auto sequenceH = generate_intervals<scalar_t>(
        randomSamplesForPlane[1], inputH, outputH, poolSizeH);
    auto sequenceW = generate_intervals<scalar_t>(
        randomSamplesForPlane[2], inputW, outputW, poolSizeW);

    /* loop over output */
    int64_t t, h, w;

    scalar_t* inputForPlane = input + plane * inputT * inputH * inputW;
    scalar_t* outputForPlane = output + plane * outputT * outputH * outputW;
    int64_t* indicesForPlane = indices + plane * outputT * outputH * outputW;

    for (t = 0; t < outputT; ++t) {
      int64_t inputTStart = sequenceT[t];

      for (h = 0; h < outputH; ++h) {
        int64_t inputHStart = sequenceH[h];

        for (w = 0; w < outputW; ++w) {
          int64_t inputWStart = sequenceW[w];

          scalar_t maxVal = -std::numeric_limits<scalar_t>::infinity();
          int64_t maxIndex = -1;

          int64_t t2, h2, w2;
          for (t2 = inputTStart; t2 < inputTStart + poolSizeT; ++t2) {
            for (h2 = inputHStart; h2 < inputHStart + poolSizeH; ++h2) {
              for (w2 = inputWStart; w2 < inputWStart + poolSizeW; ++w2) {
                AT_ASSERT(t2 >= 0 && t2 < inputT);
                AT_ASSERT(h2 >= 0 && h2 < inputH);
                AT_ASSERT(w2 >= 0 && w2 < inputW);

                int64_t planeIndex = t2 * inputH * inputW + h2 * inputW + w2;
                scalar_t val = inputForPlane[planeIndex];
                if (val > maxVal) {
                  maxVal = val;
                  maxIndex = planeIndex;
                }
              }
            }
          }

          AT_ASSERT(maxVal != -std::numeric_limits<scalar_t>::infinity());
          AT_ASSERT(maxIndex != -1);

          outputForPlane[t * outputH * outputW + h * outputW + w] = maxVal;
          indicesForPlane[t * outputH * outputW + h * outputW + w] = maxIndex;
        }
      }
    }

  }
}

template<typename scalar_t>
static void fractional_max_pool3d_out_frame(
  scalar_t* input,
  scalar_t* output,
  int64_t* indices,
  scalar_t* randomSamples,
  int64_t numBatch, int64_t numPlanes,
  int64_t inputT, int64_t inputH, int64_t inputW,
  int64_t outputT, int64_t outputH, int64_t outputW,
  int64_t poolSizeT, int64_t poolSizeH, int64_t poolSizeW) {
    if(numBatch == 1) {
      fractional_max_pool3d_out_single_batch_frame<scalar_t>(
        input, output, indices, randomSamples,
        numPlanes,
        inputT, inputH, inputW,
        outputT, outputH, outputW,
        poolSizeT, poolSizeH, poolSizeW
      );
      return;
    }
    int64_t batch;
#pragma omp parallel for private(batch)
    for(batch = 0; batch < numBatch; ++batch) {
      fractional_max_pool3d_out_single_batch_frame<scalar_t>(
        input + batch * numPlanes * inputW * inputH * inputT,
        output + batch * numPlanes * outputW * outputH * outputT,
        indices + batch * numPlanes * outputW * outputH * outputT,
        randomSamples + batch * numPlanes * 3,
        numPlanes,
        inputT, inputH, inputW,
        outputT, outputH, outputW,
        poolSizeT, poolSizeH, poolSizeW
      );
    }
  }

void fractional_max_pool3d_out_cpu_template(
  Tensor& output,
  Tensor& indices,
  const Tensor& input_,
  IntList pool_size,
  IntList output_size,
  const Tensor& randomSamples) {

  int64_t outputT = output_size[0];
  int64_t outputH = output_size[1];
  int64_t outputW = output_size[2];
  int64_t poolSizeT = pool_size[0];
  int64_t poolSizeH = pool_size[1];
  int64_t poolSizeW = pool_size[2];

  int64_t numBatch = 1;
  int64_t planeDim = 0;
  int64_t timeDim = 1;
  int64_t heightDim = 2;
  int64_t widthDim = 3;

  int64_t ndims = input_.ndimension();
  AT_CHECK(input_.numel() != 0 && (ndims == 4 || ndims == 5),
    "fractional_max_pool3d_out(): non-empty 4D or 5D (batch mode) tensor ",
    " expected for input, but got: ", ndims);

  if (ndims == 5) {
    numBatch = input_.size(0);
    planeDim++;
    timeDim++;
    heightDim++;
    widthDim++;
  }

  /* sizes */
  int64_t numPlanes = input_.size(planeDim);
  int64_t inputT = input_.size(timeDim);
  int64_t inputH = input_.size(heightDim);
  int64_t inputW = input_.size(widthDim);

  AT_CHECK(outputT + poolSizeT - 1 < inputT,
           "fractional_max_pool3d_out(): pool time ", poolSizeT,
           " too large relative to input time ", inputT);
  AT_CHECK(outputW + poolSizeW - 1 < inputW,
           "fractional_max_pool3d_out(): pool width ", poolSizeW,
           " too large relative to input width ", inputW);
  AT_CHECK(outputH + poolSizeH - 1 < inputH,
           "fractional_max_pool3d_out(): pool height ", poolSizeH,
           " too large relative to input height ", inputH);

  /* get contiguous input */
  auto input = input_.contiguous();

  if (ndims == 4) {
    /* resize output */
    output.resize_({numPlanes, outputT, outputH, outputW});
    /* indices will contain the locations for each output point */
    indices.resize_({numPlanes, outputT, outputH, outputW});
  } else {
    output.resize_({numBatch, numPlanes, outputT, outputH, outputW});
    /* indices will contain the locations for each output point */
    indices.resize_({numBatch, numPlanes, outputT, outputH, outputW});
  }
  AT_DISPATCH_FLOATING_TYPES(
    input.type(),
    "fractional_max_pool3d_out_frame",
    [&] {
      fractional_max_pool3d_out_frame<scalar_t>(
        input.data<scalar_t>(),
        output.data<scalar_t>(),
        indices.data<int64_t>(),
        randomSamples.data<scalar_t>(),
        numBatch, numPlanes,
        inputT, inputH, inputW,
        outputT, outputH, outputW,
        poolSizeT, poolSizeH, poolSizeW
      );
    }
  );
}

template<typename scalar_t>
static void fractional_max_pool3d_backward_out_single_batch_frame(
  scalar_t* gradInput,
  scalar_t* gradOutput,
  int64_t* indices,
  int64_t numPlanes,
  int64_t inputT, int64_t inputH, int64_t inputW,
  int64_t outputT, int64_t outputH, int64_t outputW) {
  int64_t plane;
#pragma omp parallel for private(plane)
  for (plane = 0; plane < numPlanes; plane++) {
    scalar_t* gradInputForPlane = gradInput + plane * inputT * inputH * inputW;
    scalar_t* gradOutputForPlane = gradOutput +
                plane * outputT * outputH * outputW;
    int64_t* indicesForPlane = indices + plane * outputT * outputH * outputW;

    int64_t h, w, t;
    for (t = 0; t < outputT; ++t) {
      for (h = 0; h < outputH; ++h) {
        for (w = 0; w < outputW; ++w) {
          int64_t outputIndex = t * outputH * outputW + h * outputW + w;
          int64_t index = indicesForPlane[outputIndex];
          AT_ASSERT(index >= 0 && index < inputT * inputH * inputW);
          gradInputForPlane[index] += gradOutputForPlane[outputIndex];
        }
      }
    }
  }
}

template<typename scalar_t>
static void fractional_max_pool3d_backward_out_frame(
  scalar_t* gradInput,
  scalar_t* gradOutput,
  int64_t* indices,
  int64_t numBatch, int64_t numPlanes,
  int64_t inputT, int64_t inputH, int64_t inputW,
  int64_t outputT, int64_t outputH, int64_t outputW) {
    if(numBatch == 1) {
      fractional_max_pool3d_backward_out_single_batch_frame<scalar_t>(
        gradInput, gradOutput, indices,
        numPlanes,
        inputT, inputH, inputW,
        outputT, outputH, outputW
      );
      return;
    }
    int64_t batch;
#pragma omp parallel for private(batch)
    for(batch = 0; batch < numBatch; ++ batch) {
      fractional_max_pool3d_backward_out_single_batch_frame<scalar_t>(
        gradInput + batch * numPlanes * inputW * inputH * inputT,
        gradOutput + batch * numPlanes * outputW * outputH * outputT,
        indices + batch * numPlanes * outputW * outputH * outputT,
        numPlanes,
        inputT, inputH, inputW,
        outputT, outputH, outputW
      );
    }
  }


void fractional_max_pool3d_backward_out_cpu_template(
  const Tensor& input,
  const Tensor& gradOutput_,
  Tensor& gradInput,
  IntList output_size,
  IntList pool_size /* unused */,
  const Tensor& indices) {

  int64_t outputT = output_size[0];
  int64_t outputH = output_size[1];
  int64_t outputW = output_size[2];

  int64_t numBatch = 1;
  int64_t planeDim = 0;
  int64_t timeDim = 1;
  int64_t heightDim = 2;
  int64_t widthDim = 3;

  int64_t ndims = input.ndimension();
  if (ndims == 5) {
    numBatch = input.size(0);
    planeDim = 1;
    heightDim++;
    widthDim++;
    timeDim++;
  }

  /* sizes */
  int64_t numPlanes = input.size(planeDim);
  int64_t inputT = input.size(timeDim);
  int64_t inputH = input.size(heightDim);
  int64_t inputW = input.size(widthDim);

  AT_CHECK(outputT == gradOutput_.size(timeDim),
           "fractional_max_pool3d_backward_out(): gradOutput time unexpected");
  AT_CHECK(outputH == gradOutput_.size(heightDim),
           "fractional_max_pool3d_backward_out(): ",
           "gradOutput height unexpected");
  AT_CHECK(outputW == gradOutput_.size(widthDim),
           "fractional_max_pool3d_backward_out(): gradOutput width unexpected");

  /* get contiguous gradOutput */
  auto gradOutput = gradOutput_.contiguous();

  /* resize */
  gradInput.resize_as_(input);
  gradInput.zero_();

  /* backprop */
  AT_DISPATCH_FLOATING_TYPES(
    input.type(),
    "fractional_max_pool3d_backward_out_frame",
    [&]{
      fractional_max_pool3d_backward_out_frame<scalar_t>(
        gradInput.data<scalar_t>(),
        gradOutput.data<scalar_t>(),
        indices.data<int64_t>(),
        numBatch, numPlanes,
        inputT, inputH, inputW,
        outputT, outputH, outputW
      );
    }
  );
}

}// namespace

std::tuple<Tensor&, Tensor&> fractional_max_pool3d_out_cpu(
 at::Tensor& output,
 at::Tensor& indices,
 const at::Tensor& input,
 IntList pool_size,
 IntList output_size,
 const at::Tensor& randomSamples) {
 fractional_max_pool3d_out_cpu_template(
   output,
   indices,
   input,
   pool_size,
   output_size,
   randomSamples);
 return std::tuple<Tensor&, Tensor&>(output, indices);
}

std::tuple<Tensor, Tensor> fractional_max_pool3d_cpu(
 const at::Tensor& input,
 IntList pool_size,
 IntList output_size,
 const at::Tensor& randomSamples) {
 Tensor output = at::empty(output_size, input.options());
 Tensor indices = at::empty(output_size, at::kLong);
 fractional_max_pool3d_out_cpu_template(
   output,
   indices,
   input,
   pool_size,
   output_size,
   randomSamples);
 return std::tuple<Tensor, Tensor>(output, indices);
}

Tensor& fractional_max_pool3d_backward_out_cpu(
 at::Tensor& gradInput,
 const at::Tensor& gradOutput_,
 const at::Tensor& input,
 IntList pool_size,
 IntList output_size,
 const at::Tensor& indices) {
 fractional_max_pool3d_backward_out_cpu_template(
   input,
   gradOutput_,
   gradInput,
   output_size,
   pool_size,
   indices);
 return gradInput;
}

Tensor fractional_max_pool3d_backward_cpu(
 const at::Tensor& gradOutput_,
 const at::Tensor& input,
 IntList pool_size,
 IntList output_size,
 const at::Tensor& indices) {
 Tensor gradInput = at::empty({0}, input.options());
 fractional_max_pool3d_backward_out_cpu_template(
   input,
   gradOutput_,
   gradInput,
   output_size,
   pool_size,
   indices);
 return gradInput;
}

}// native
}// at
